Improving Human-Autonomous Vehicle Interaction in Complex Systems

Robert Kaufman

REVIEW 3 major objections 5 minor 237 references

Improving Human-Autonomous Vehicle Interaction in Complex Systems

T0 review · 3 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This dissertation argues that AV communications must adapt to rider, goal, and context; three studies show explanations can teach, errors can erode trust even when driving is flawless, and risk-benefit attitudes best predict trust.

desk verdict A useful, honest dissertation whose Chapter 3 provides a real but over-stated result: explanation errors hurt trust even with identical driving, but the errors also leak information about perceived driving competence. read the letter →

arxiv 2504.17170 v1 pith:GNSYDG2V submitted 2025-04-24 cs.HC cs.AIcs.CY

Robert Kaufman This is my paper · ORCID

classification cs.HCcs.AIcs.CY

keywords autonomousvehiclesexplainableAIhuman-vehicletrustmultimodalexplanationssituationalawarenessdrivingsimulationpersonalizedinterfacesrisk-benefitattitudes

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This dissertation argues that human-autonomous-vehicle (AV) interaction fails when design treats all riders and situations the same, and succeeds when communication adapts to the person, the goal, the driving context, and the AV's own traits. In a driving-simulator experiment, an AI coach that explained what it was doing and why improved novices' racing-line performance, with visual 'what' cues plus auditory 'why' cues outperforming all-auditory explanation. In a 232-person video study, inaccurate AV explanations reduced riders' comfort, reliance, satisfaction, and even their confidence in the AV's driving ability, even though the driving shown was identical and flawless across conditions. A machine-learning survey study found that perceived risks and benefits of AVs are the strongest predictors of young adults' trust, outweighing other personal characteristics. Together the studies support transparent, adaptable, personalized AV communication.

What carries the argument

The central object is the human-AV complex system viewed as joint action: a multi-level success function in which high-level goals like safe transport or learning have subgoal criteria, and those criteria shift when components of the system change. Situational awareness—perception, comprehension, and projection—is the mechanism by which the team coordinates, and explanatory communication ('what' the AV is doing and 'why') is the main lever for building it. The dissertation manipulates that lever across three studies: explanation type and modality, explanation errors, and personal predictors of trust.

What would settle it

Run the same full-motion simulator protocol with an added control group that drives the same pre and post laps with the same time-on-task but no AI coach; if that group shows pre-post gains equal to the coached groups, the claim that AI explanations drive learning is falsified.

Watch

Extended reading notes

Core claim

In its own terms, the dissertation's central claim is that a human and an autonomous vehicle form a joint-action team whose success depends on filling situational-awareness gaps, and that these gaps change with the human's traits, the AV's traits, the goal, and the driving context. The sharpest evidence comes from the explanation-error study: riders saw identical, flawless driving, yet when the AV's spoken and on-screen explanations were inaccurate, comfort, reliance, satisfaction, and confidence in driving ability all fell significantly, with bigger falls for more severe errors. The AI-coach study shows the same communication machinery can be tuned for learning: visual 'what' guidance plus auditory 'why' rationale improved novices' racing-line performance without the overload produced by all-auditory explanations. The trust-prediction study shows that perceived risks and benefits of AVs dominate other personal factors in predicting young adults' trust. The dissertation concludes that accurate, context-sensitive, personalized explanations are a necessary condition for AV acceptance.

Load-bearing premise

The Chapter 2 claim that the AI coach caused the learning gains assumes the gains are larger than plain practice would produce, but the study has no practice-only control group, a gap the author acknowledges; if practice alone explains the gains, the coaching effect does not stand.

Editorial extensions

If this is right

AV explanation systems should be held to accuracy standards comparable to driving-system safety, because wrong explanations reduce riders' willingness to rely on the AV even when the driving is perfect.
HMI design should match information to modality: visuospatial 'what' guidance works best as a visual cue, while 'why' rationale can be auditory, reducing uncertainty and cognitive overload.
Communication should adapt to context: high perceived harm amplifies the damage of explanation errors, while in very difficult situations riders may prefer to keep relying on the AV rather than take over.
Personalization of trust should target risk and benefit perceptions, since these predicted young adults' AV trust more strongly than other psychosocial traits in the machine-learning model.
Explanation systems should be deployed only when their accuracy is assured, since the studies indicate faulty communication can be worse than silence or observation alone.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial: If explanation errors contaminate perceived driving ability, then a Level-3 conditional automation system could provoke unsafe takeovers after a communication failure; a takeover-timing study would test this.
Editorial: The dominance of risk-benefit perceptions implies trust interventions could work through benefit framing and risk communication rather than more transparency alone; the dissertation does not test this.
Editorial: The error-to-driving-confidence crossover may generalize to other XAI domains, where explanation quality shapes perceived model competence even when outputs are correct.

Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The dissertation is a solid collection of three empirical studies, each already peer-reviewed in good venues. The genuinely new work is Chapter 3: a controlled, well-powered (n=232) test of how AV explanation errors affect trust, reliance, satisfaction, and confidence. Showing that errors move all four outcomes, and that the effect scales with error severity, is a real contribution. Chapter 2 is a smaller (n=41) simulator study of an AI driving coach; it has a plausible design and honest limitations, but the lack of a practice-only control really does limit the causal claim, exactly as the author says in Section 2.5.2. Chapter 4 is a competent ML prediction exercise; the SHAP analyses are transparent, and the finding that risk/benefit attitudes beat demographics is believable. The framework in Chapter 1 is more of a useful organizational lens than a rigorous theory, and it leans on the author's own prior work, but the empirical chapters stand independently and the self-citation is not circular.

The soft spot worth flagging is the interpretation of Chapter 3. The stress-test concern lands: the 'low' and 'high' error conditions are not purely communication failures. When the AV says 'a cyclist is crossing' while a pedestrian is visible, or says 'merging right' while braking, the participant is rationally entitled to infer that the AV's perception or planning is faulty. So the drop in 'confidence in driving ability' is not clean evidence that explanation quality is evaluated independently of driving quality. The crossover is still interesting and important, but the strong claim—'even if driving is perfect, explanations alone reduce adoption'—is not fully supported by this manipulation. A cleaner test would use errors that are clearly external to the driving system, or would add a condition where the AV explicitly acknowledges uncertainty. The author does discuss the crossover, but the framing leans on the strong reading.

That said, the paper is honest, the analyses are mostly appropriate, and the limitations are stated rather than hidden. The Chapter 3 result will be useful to anyone working on AV HMIs or trust in automation, even if the precise mechanism needs follow-up.

Recommendation: this deserves a serious referee. It is a degree dissertation whose chapters have already passed CHI/Nature-level review; the compilation adds a coherent systems framing. I would not desk-reject it. I'd send it out, with an instruction to the author to soften the Chapter 3 causal language or add a disambiguation study.

Referee Report

3 major / 5 minor

Summary. The dissertation argues that human-autonomous-vehicle (AV) interaction should be understood as a complex system in which successful communication depends on the driving context, the interaction goal, human traits, and AV traits. Three empirical studies support the argument. Chapter 2 reports a pre-post driving-simulator experiment (n=41) evaluating an AI race-car driving coach that provides 'what' and 'why' explanations in visual or auditory modalities; it concludes that AI coaching can improve novice performance-driving skills and that modality-appropriate, task-sensitive explanations reduce cognitive load and uncertainty. Chapter 3 reports a within-subjects online video study (n=232) in which AV explanation errors were manipulated while the AV's driving was identical across conditions; it finds that explanation errors reduce comfort, reliance, satisfaction, and confidence in the AV's driving ability, with context (perceived harm, difficulty) and personal traits (initial trust, expertise) moderating these outcomes. Chapter 4 applies machine learning to survey data from young adults to predict trust in AVs from psychosocial traits, risk-benefit attitudes, and driving factors, reporting that risk and benefit perceptions are the strongest predictors. The dissertation concludes with design implications for transparent, adaptable, and personalized AV communication.

Significance. If the central claims hold, the dissertation makes a useful contribution to human-AI interaction and AV human factors. Its strengths include three distinct empirical studies, the use of linear mixed-effects models with random effects for participant and scenario, and a relatively large Chapter 3 sample. The chapters have already appeared in or been accepted for peer-reviewed venues, which speaks to the maturity of the individual studies. Chapter 4's machine-learning pipeline with SHAP values and ablation experiments provides a concrete, reproducible method for studying trust prediction. The dissertation is also unusually candid about its limitations, particularly the practice-effect confound in Chapter 2 and the post hoc scenario exclusions in Chapter 3. The proposed complex-systems framing is generative and practical. However, the strongest interpretive claim, that explanation errors matter 'even if the AV's driving performance is perfect,' is not cleanly supported by the Chapter 3 manipulation, because the errors used there are diagnostic of the AV's perceptual and planning competence.

major comments (3)

[Section 3.4.3, Table 3.2; Section 3.5.1; Section 3.6] The crossover claim that explanation errors reduce 'confidence in driving ability' despite 'identical driving' is not cleanly supported, because the error manipulation does not isolate communication accuracy from perceived system competence. In the Low condition the AV says 'Braking, a cyclist is crossing the road' while the video shows a pedestrian; in the High condition it says 'Merging right, a cyclist is crossing the road' while the vehicle is slowing and the obstacle is a pedestrian. These are not merely inaccurate descriptions of an independent communication channel; they are incorrect statements about the AV's perceptual beliefs and intended actions. A participant can rationally infer that the AV's object recognition or planning model is faulty and update their confidence in the AV's driving ability accordingly. The dissertation's strong conclusion in Section 3.6 ('even if the AV's driving performance is perfect ... people may still refuse to adopt AV technology') therefore overreaches. The study remains informative about holistic judgments of AV competence, but the claim that explanation quality is evaluated independently of driving quality must be reframed, or the design needs errors that cannot be diagnostic of perception or planning (and a manipulation check confirming that participants perceive the errors as communication-only). The within-subjects repetition of the same scenario three times is a secondary source of contrast effects and should be acknowledged in the interpretation.
[Section 3.4.2 and Section 3.4.4] The post hoc removal of three scenarios because the AV's driving was deemed 'imperfect' is a defensible quality check, but it means the 'identical driving' claim is defined only on the retained subset of 24 scenarios. Please report the main analyses both with and without the removed scenarios, and justify that the exclusion rule was applied independently of the outcome measures. Relatedly, the binary reliance measure ('rely on AV' vs. 'take control myself') is scaled from 0-1 to 0-10 and entered into linear mixed-effects models as if it were continuous. A mixed-effects logistic model or ordinal model would be more statistically appropriate. This does not necessarily change the direction of the reported effects, but a dissertation-level analysis should address the distributional issue rather than only noting that scaling does not affect interpretation.
[Chapter 2, Sections 2.4.2 and 2.5.2] The pre-post design has no practice-only control group for the all-groups-combined improvements, and the manuscript explicitly acknowledges this limitation. Because the chapter's broad claim that 'AI coaching can effectively teach performance driving skills' partly relies on these combined improvements, the practice confound is load-bearing; the credible between-group evidence is essentially limited to racing-line distance in a sample of n=41. In addition, the analysis runs numerous linear mixed-effects models across multiple performance and self-report outcomes without correction for multiple comparisons. I recommend reporting effect sizes and confidence intervals, and either correcting for multiple comparisons or explicitly labeling secondary analyses as exploratory. This does not undermine the chapter's contribution, but it should be reflected in the strength of the conclusions.

minor comments (5)

[Table 2.1 caption] The caption contains a typo: 'Presentaton' should be 'Presentation'.
[Section 3.5.1] The text says 'Individual comparisons between each condition ... show highly significant effects ... (Figure 3.4)' but the pairwise condition comparisons are reported in Table 3.4; Figure 3.4 is the later chart of reliance-decision factor importance. Please correct the cross-reference.
[Section 4.4.3] The model-development section would benefit from explicit hyperparameter settings, software versions, and the exact train/validation split used, so that the SHAP analyses are reproducible from the text.
[Chapter 3, Section 3.5.2] The interaction-effect discussion distinguishes significant, trending, and non-significant results in the text, but the tables only use significance stars; adding standard errors or confidence intervals to the text summary would help readers calibrate the strength of the moderation effects.
[Chapter 2, Section 2.4.5] The interview quotes are illustrative but are not accompanied by a formal coding scheme or inter-rater reliability; a sentence describing how themes were derived would strengthen the mixed-methods claims.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the dissertation's three studies rest on independent data, and the self-cited framework is not used as a derivation.

full rationale

This is an empirical dissertation rather than a derivational one. Chapter 2 is a pre-post simulator experiment, Chapter 3 is a within-subjects scenario-rating experiment, and Chapter 4 is a survey-based machine-learning study. None of the chapters defines a target quantity in terms of a fitted input, and none renames a fitted parameter as a prediction. The 'success function' framework in Chapter 1 is a conceptual checklist, not an equation, and it is supported by independent literature (e.g., Endsley, Stanton, Hoff and Bashir) as well as by the dissertation's own new data; the self-citation to Kaufman, Kirsh and Weibel (2024) reprints framework material but is not load-bearing for any empirical result. The passage in Section 2.5.2 explicitly acknowledges that the all-groups pre-post gains cannot be separated from practice effects because there is no practice-only control; this is a validity limitation, not a circularity, and the text itself flags it as a limitation for future study. The Chapter 3 crossover finding—identical driving with explanation errors lowering confidence in driving ability—is an empirical outcome, not a construction: it is inferred from observed ratings rather than encoded in the manipulation definitions. A skeptic's concern that the error manipulation may imply faulty perception or planning is a threat to internal validity, but it is not a circularity, because the claim is not derived from the definition of the error conditions. Chapter 4 uses cross-validated machine learning, and the manuscript does not show that the trust target is composed of the predictor items, so the 'risk and benefit perceptions predict trust' result is not forced by definition. No specific circular step could be identified.

Assumptions & free parameters 1 free parameters · 6 assumptions · 0 invented entities

The empirical claims rest primarily on domain assumptions about simulator validity and self-report measures, plus two ad-hoc choices: excluding three driving scenarios post hoc, and attributing learning to the coach without a practice control. No derivational free parameters or invented entities are present.

free parameters (1)

Machine learning model hyperparameters (Chapter 4)
Model hyperparameters are not reported in the provided text. The claim that personal factors predict trust depends on the model achieving the reported performance, so these choices are load-bearing.

assumptions (6)

standard math Linear mixed-effects models correctly account for repeated measures and random effects in Chapters 2 and 3.
Statistical inference relies on LME assumptions, but no model diagnostics are reported.
domain assumption Driving simulator and video scenarios are valid proxies for real-world human-AV interaction.
Generalization of findings to real AVs depends on this, stated in methods but not externally validated.
domain assumption Self-report scales (trust, comfort, satisfaction, confidence) validly measure the intended constructs.
All main outcomes are self-report; no behavioral validation beyond stated scale origins.
ad hoc to paper The three scenarios removed from Chapter 3 analysis were excluded because of imperfect AV driving, and the remaining scenarios represent the domain.
Post-hoc exclusion, announced in Section 3.4.2, could bias results if the removed scenarios differ systematically.
ad hoc to paper In Chapter 2, pre-post improvements are attributable to the AI coach rather than practice alone.
The authors state in Section 2.5.2 that the design cannot separate practice effects from training effects.
domain assumption In Chapter 4, the machine learning pipeline (5-fold CV, SHAP) is correctly applied and avoids leakage.
Details in the provided text are insufficient to verify.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Improving Human-Autonomous Vehicle Interaction in Complex Systems." pith.science (2026). https://pith.science/paper/GNSYDG2V

@misc{pith2026250417170,
  author       = {Pith},
  title        = {Pith review of: Improving Human-Autonomous Vehicle Interaction in Complex Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GNSYDG2V}},
  note         = {Machine review of arXiv:2504.17170}
}

read the original abstract

Unresolved questions about how autonomous vehicles (AVs) should meet the informational needs of riders hinder real-world adoption. Complicating our ability to satisfy rider needs is that different people, goals, and driving contexts have different criteria for what constitutes interaction success. Unfortunately, most human-AV research and design today treats all people and situations uniformly. It is crucial to understand how an AV should communicate to meet rider needs, and how communications should change when the human-AV complex system changes. I argue that understanding the relationships between different aspects of the human-AV system can help us build improved and adaptable AV communications. I support this argument using three empirical studies. First, I identify optimal communication strategies that enhance driving performance, confidence, and trust for learning in extreme driving environments. Findings highlight the need for task-sensitive, modality-appropriate communications tuned to learner cognitive limits and goals. Next, I highlight the consequences of deploying faulty communication systems and demonstrate the need for context-sensitive communications. Third, I use machine learning (ML) to illuminate personal factors predicting trust in AVs, emphasizing the importance of tailoring designs to individual traits and concerns. Together, this dissertation supports the necessity of transparent, adaptable, and personalized AV systems that cater to individual needs, goals, and contextual demands. By considering the complex system within which human-AV interactions occur, we can deliver valuable insights for designers, researchers, and policymakers. This dissertation also provides a concrete domain to study theories of human-machine joint action and situational awareness, and can be used to guide future human-AI interaction research. [shortened for arxiv]

Figures

Figures reproduced from arXiv: 2504.17170 by the authors.

**Figure 1.1.** Overview of the Human-AV system. . . . . . . . . . . . . . . . . . . . . . . . . . . 7 [PITH_FULL_IMAGE:figures/full_fig_p009_1_1.png] view at source ↗

**Figure 1.2.** Example Success function for the goal of Safe Transportation. [PITH_FULL_IMAGE:figures/full_fig_p027_1_2.png] view at source ↗

Figures from the paper (19 more)

**Figure 1.3.** Figure 1.3: Outline of the premises of this dissertation. [PITH_FULL_IMAGE:figures/full_fig_p031_1_3.png]

**Figure 2.1.** Figure 2.1: Full-motion driving simulator. 2.3.3 AI Coach Explanations During the AI coach observation sessions, explanatory instructions were provided to all participants except the control group. Auditory explanations were presented via an in-cabin speaker. These were produced…

**Figure 2.2.** Figure 2.2: Visual ‘what’ racing line projection. The green racing line projected on the track is an example of a visual ‘what’ explanation seen by Group 4. 2.3.4 Conditions Participants were randomly assigned to one of four conditions ( [PITH_FULL_IMAGE:figures/full_fig_p045_2…

**Figure 2.3.** Figure 2.3: Study timeline. The study duration was approximately 1 hour in total and included a questionnaire, participant driving, AI coach observation, and interview. were going for their best lap time. These instructions align with the standard instructions for racing used in…

**Figure 2.4.** Figure 2.4: Change (∆) from pre-post observation for driving performance measures by group. 2.4.3 Impact of AI Coaching Information Type and Modality on AV Trust, Self-perceived Confidence, and Expertise As a whole, impressions of the AI coach were very positive across all group…

**Figure 2.5.** Figure 2.5: Pre-post scores for self-report measures, all groups combined. [PITH_FULL_IMAGE:figures/full_fig_p053_2_5.png]

**Figure 3.1.** Figure 3.1: Example images from four driving scenarios. [PITH_FULL_IMAGE:figures/full_fig_p081_3_1.png]

**Figure 3.2.** Figure 3.2: Mean Outcome by Error Condition Across All Scenarios. [PITH_FULL_IMAGE:figures/full_fig_p087_3_2.png]

**Figure 3.3.** Figure 3.3: Mean Harm and Difficulty Across All Scenarios. [PITH_FULL_IMAGE:figures/full_fig_p091_3_3.png]

**Figure 3.4.** Figure 3.4: Relative Importance of Factors on Reliance Decisions. [PITH_FULL_IMAGE:figures/full_fig_p097_3_4.png]

**Figure 4.1.** Figure 4.1: Overall process architecture. Our pipeline moves from data collection and processing to model development and explanations using SHAP. 4.4.1 Participants and Data Collection A total of 1457 participants completed the study and passed all quality control requirements.…

**Figure 4.** Figure 4: shows how individual feature values contribute to the model’s output. We observe that [PITH_FULL_IMAGE:figures/full_fig_p138_4.png]

**Figure 4.2.** Figure 4.2: Full Feature Model: mean absolute SHAP scores, averaged across 5 [PITH_FULL_IMAGE:figures/full_fig_p139_4_2.png]

**Figure 4.3.** Figure 4.3: Full Feature Model SHAP summary plot showing feature value [PITH_FULL_IMAGE:figures/full_fig_p139_4_3.png]

**Figure 4.4.** Figure 4.4: Feature Subset (risk and benefit only): mean absolute SHAP scores, [PITH_FULL_IMAGE:figures/full_fig_p141_4_4.png]

**Figure 4.** Figure 4: shows the top contributing factors in terms of feature importance via mean [PITH_FULL_IMAGE:figures/full_fig_p141_4.png]

**Figure 4.5.** Figure 4.5: Feature Subset (risk and benefit only) SHAP summary plot showing [PITH_FULL_IMAGE:figures/full_fig_p142_4_5.png]

**Figure 4.6.** Figure 4.6: Full Feature Model without risk and benefit factors: mean absolute SHAP scores, averaged across 5 folds. This shows the most important features for predicting both high and low trust (shown in combination, as they are mirrors of each other). Without risks and benefit…

**Figure 4.7.** Figure 4.7: Full Feature Model without risk/benefit factors: SHAP summary plot showing feature value impact on trust. This plot shows how the value of a feature impacts the model’s output. The more extreme the SHAP value, the more indicative that value was of being high trust (p…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

237 extracted references · 61 canonical work pages

[1]

URL https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813473.pdf

National highway traffic safety administration traffic safety facts: 2021 data, 2021. URL https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813473.pdf

2021
[2]

Autonomous vehicles and alternatives to driving: trust, preferences, and effects of age

Hillary Abraham, Chaiwoo Lee, Samantha Brady, Craig Fitzgerald, Bruce Mehler, Bryan Reimer, and Joseph F Coughlin. Autonomous vehicles and alternatives to driving: trust, preferences, and effects of age. In Proceedings of the transportation research board 96th annual meeting , pages 8–12. Transportation Research Board Washington, DC, 2017

2017
[3]

Case study of today’s automotive dealerships: Introduction and delivery of advanced driver assistance systems

Hillary Abraham, Hale McAnulty, Bruce Mehler, and Bryan Reimer. Case study of today’s automotive dealerships: Introduction and delivery of advanced driver assistance systems. Transportation research record, 2660(1):7–14, 2017

2017
[4]

Ransford A Acheampong and Federico Cugurullo. Capturing the behavioural deter- minants behind the adoption of autonomous vehicles: Conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars. Transportation research part F: traffic psychology and behaviour , 62: 349–375, 2019

2019
[5]

Risk and trust in artificial intelligence technologies: A case study of autonomous vehicles

Ighoyota Ben Ajenaghughrure, Sonia Claudia da Costa Sousa, and David Lamas. Risk and trust in artificial intelligence technologies: A case study of autonomous vehicles. In 2020 13th international conference on human system interaction (HSI) , pages 118–123. Ieee, 2020

2020
[6]

A classification model for sensing human trust in machines using eeg and gsr

Kumar Akash, Wan-Lin Hu, Neera Jain, and Tahira Reid. A classification model for sensing human trust in machines using eeg and gsr. ACM Transactions on Interactive Intelligent Systems (TiiS) , 8(4):1–20, 2018

2018
[7]

Incorporating personality in user interface design: A review

Tom´ as Alves, Joana Nat´ alio, Joana Henriques-Calado, and Sandra Gama. Incorporating personality in user interface design: A review. Personality and Individual Differences , 155:109709, 2020

2020
[8]

The future of well-being in a tech-saturated world

Janna Anderson and Lee Rainie. The future of well-being in a tech-saturated world. 2018. 154

2018

Show all 237 references

[9]

In ai we trust? perceptions about automated decision-making by artificial intelligence

Theo Araujo, Natali Helberger, Sanne Kruikemeier, and Claes H De Vreese. In ai we trust? perceptions about automated decision-making by artificial intelligence. AI & society, 35(3):611–623, 2020

2020
[10]

Ex- plainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions

Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, and Randy Goebel. Ex- plainable artificial intelligence for autonomous driving: A comprehensive overview and field guide for future research directions. arXiv preprint arXiv:2112.11561 , 2021

2021 arXiv
[11]

Investigating explanations in condi- tional and highly automated driving: The effects of situation awareness and modality

Lilit Avetisyan, Jackie Ayoub, and Feng Zhou. Investigating explanations in condi- tional and highly automated driving: The effects of situation awareness and modality. Transportation research part F: traffic psychology and behaviour , 89:456–466, 2022

2022
[12]

An investigation of drivers’ dynamic situational trust in conditionally automated driving

Jackie Ayoub, Lilit Avetisyan, Mustapha Makki, and Feng Zhou. An investigation of drivers’ dynamic situational trust in conditionally automated driving. IEEE Transac- tions on Human-Machine Systems , 52(3):501–511, 2021

2021
[13]

Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability

Jackie Ayoub, X Jessie Yang, and Feng Zhou. Modeling dispositional and initial learned trust in automated vehicles with predictability and explainability. Transportation research part F: traffic psychology and behaviour , 77:102–116, 2021

2021
[14]

Real-time trust prediction in conditionally automated driving using physiological measures

Jackie Ayoub, Lilit Avetisian, X Jessie Yang, and Feng Zhou. Real-time trust prediction in conditionally automated driving using physiological measures. IEEE Transactions on Intelligent Transportation Systems , 2023

2023
[15]

Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning

David Baidoo-Anu and Leticia Owusu Ansah. Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning. Journal of AI , 7(1):52–62, 2023

2023
[16]

Random effects structure for confirmatory hypothesis testing: Keep it maximal

Dale J Barr, Roger Levy, Christoph Scheepers, and Harry J Tily. Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of memory and language, 68(3):255–278, 2013

2013
[17]

The role of domain expertise in trusting and following explainable ai decision support systems

Sarah Bayer, Henner Gimpel, and Moritz Markgraf. The role of domain expertise in trusting and following explainable ai decision support systems. Journal of Decision Systems, 32(1):110–138, 2022

2022
[18]

Programming is hard-or at least it used to be: Educational opportunities and challenges of ai code generation

Brett A Becker, Paul Denny, James Finnie-Ansley, Andrew Luxton-Reilly, James Prather, and Eddie Antonio Santos. Programming is hard-or at least it used to be: Educational opportunities and challenges of ai code generation. In Proceedings of the 54th ACM Technical Symposium on ...

2023
[19]

Conversense: An automated 155 approach to assess patient-provider interactions using social signals

Manas Satish Bedmutha, Anuujin Tsedenbal, Kelly Tobar, Sarah Borsotto, Kimberly R Sladek, Deepansha Singh, Reggie Casanova-Perez, Emily Bascom, Brian Wood, Janice Sabin, Wanda Pratt, Andrea Hartzler, and Nadir Weibel. Conversense: An automated 155 approach to assess patient-pr...

2024
[20]

Can we trust ai? an empirical investigation of trust requirements and guide to successful ai adoption

Patrick Bedu´ e and Albrecht Fritzsche. Can we trust ai? an empirical investigation of trust requirements and guide to successful ai adoption. Journal of Enterprise Information Management, 35(2):530–549, 2022

2022
[21]

Trust in and adoption of online recommendation agents

Izak Benbasat and Weiquan Wang. Trust in and adoption of online recommendation agents. Journal of the association for information systems , 6(3):4, 2005

2005
[22]

Attitudes towards autonomous vehicles among people with physical disabilities

Roger Bennett, Rohini Vijaygopal, and Rita Kottasz. Attitudes towards autonomous vehicles among people with physical disabilities. Transportation research part A: policy and practice, 127:1–17, 2019

2019
[23]

Uncovering early adopter’s perceptions and purchase intentions of automated vehicles: Insights from early adopters of electric vehicles in california

Rosaria M Berliner, Scott Hardman, and Gil Tal. Uncovering early adopter’s perceptions and purchase intentions of automated vehicles: Insights from early adopters of electric vehicles in california. Transportation research part F: traffic psychology and behaviour , 60:712–722, 2019

2019
[24]

The economic and societal impact of motor vehicle crashes, 2019

Lawrence Blincoe, Ted R Miller, Jing-Shiarn Wang, David Swedler, Tom Coughlin, Bruce Lawrence, Feng Guo, Sheila Klauer, and Thomas Dingus. The economic and societal impact of motor vehicle crashes, 2019. Technical report, 2022

2019
[25]

Can you trust the black box? the effect of personality traits on trust in ai-enabled user interfaces

Martin B¨ ockle, Kwaku Yeboah-Antwi, and Iana Kouris. Can you trust the black box? the effect of personality traits on trust in ai-enabled user interfaces. In International Conference on Human-Computer Interaction , pages 3–20. Springer, 2021

2021
[26]

The anova to mixed model transition

Matthieu P Boisgontier and Boris Cheval. The anova to mixed model transition. Neuroscience & Biobehavioral Reviews, 68:1004–1005, 2016

2016
[27]

Race driver model

Francesco Braghin, Federico Cheli, Stefano Melzi, and Edoardo Sabbioni. Race driver model. Computers & Structures , 86(13-14):1503–1516, 2008

2008
[28]

A quasi steady state approach to race car lap simulation in order to understand the effects of racing line and centre of gravity location

DL Brayshaw and MF Harrison. A quasi steady state approach to race car lap simulation in order to understand the effects of racing line and centre of gravity location. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 219(6):725...

2005
[29]

Random forests

Leo Breiman. Random forests. Machine learning, 45:5–32, 2001

2001
[30]

The desirability of control

Jerry M Burger and Harris M Cooper. The desirability of control. Motivation and emotion, 3:381–393, 1979. 156

1979
[31]

Post-traumatic stress reactions following motor vehicle accidents

Dennis J Butler, H Steven Moffic, and NICK W TURKAL. Post-traumatic stress reactions following motor vehicle accidents. American Family Physician, 60(2):524–530, 1999

1999
[32]

Explana- tions considered harmful: The impact of misleading explanations on accuracy in hybrid human-ai decision making

Federico Cabitza, Caterina Fregosi, Andrea Campagner, and Chiara Natali. Explana- tions considered harmful: The impact of misleading explanations on accuracy in hybrid human-ai decision making. In World Conference on Explainable Artificial Intelligence , pages 255–269. Springer, 2024

2024
[33]

Human-vehicle interaction to support driver’s situation awareness in automated vehicles: a systematic review

Marine Capallera, Leonardo Angelini, Quentin Meteier, Omar Abou Khaled, and Elena Mugellini. Human-vehicle interaction to support driver’s situation awareness in automated vehicles: a systematic review. IEEE Transactions on intelligent vehicles , 8 (3):2551–2567, 2022

2022
[34]

Ai in cai: An artificial-intelligence approach to computer-assisted instruction

Jaime R Carbonell. Ai in cai: An artificial-intelligence approach to computer-assisted instruction. IEEE transactions on man-machine systems , 11(4):190–202, 1970

1970
[35]

Don’t speak and drive: cognitive workload of in-vehicle speech interactions

Chun-Cheng Chang, Jaka Sodnik, and Linda Ng Boyle. Don’t speak and drive: cognitive workload of in-vehicle speech interactions. In Adjunct Proceedings of the 8th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, pages 99–104, 2016

2016
[36]

Smote: synthetic minority over-sampling technique

Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of artificial intelligence research, 16:321–357, 2002

2002
[37]

Situation awareness-based agent transparency and human- autonomy teaming effectiveness

Jessie YC Chen, Shan G Lakhmani, Kimberly Stowers, Anthony R Selkowitz, Julia L Wright, and Michael Barnes. Situation awareness-based agent transparency and human- autonomy teaming effectiveness. Theoretical issues in ergonomics science, 19(3):259–282, 2018

2018
[38]

A review of technology acceptance by older adults

Ke Chen and Alan HS Chan. A review of technology acceptance by older adults. Gerontechnology, 2011

2011
[39]

Learn thy enemy: Online, task-aware opponent modeling in autonomous racing

Letian Chen, Shawn Manuel James Delgado John Subosits, and Paul Tylkin. Learn thy enemy: Online, task-aware opponent modeling in autonomous racing. Machine Learning for Autonomous Driving Symposium (ML4AD) , 2023

2023
[40]

Xgboost: A scalable tree boosting system

Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining , pages 785–794, 2016

2016
[41]

Relation between trust attitudes toward automation, hofstede’s cultural dimensions, and big five personality traits

Shih-Yi Chien, Katia Sycara, Jyi-Shane Liu, and Asiye Kumru. Relation between trust attitudes toward automation, hofstede’s cultural dimensions, and big five personality traits. In Proceedings of the human factors and ergonomics society annual meeting , 157 volume 60, pages 84...

2016
[42]

The effect of culture on trust in automation: reliability and workload

Shih-Yi Chien, Michael Lewis, Katia Sycara, Jyi-Shane Liu, and Asiye Kumru. The effect of culture on trust in automation: reliability and workload. ACM Transactions on Interactive Intelligent Systems (TiiS) , 8(4):1–31, 2018

2018
[43]

Investigating the importance of trust on adopting an autonomous vehicle

Jong Kyu Choi and Yong Gu Ji. Investigating the importance of trust on adopting an autonomous vehicle. International Journal of Human-Computer Interaction , 31(10): 692–702, 2015

2015
[44]

Trust and ethics in ai

Hyesun Choung, Prabu David, and Arun Ross. Trust and ethics in ai. Ai & Society, 38 (2):733–745, 2023

2023
[45]

Situated cognition: On human knowledge and computer representa- tions

William J Clancey. Situated cognition: On human knowledge and computer representa- tions. Cambridge university press, 1997

1997
[46]

Effects of semantic segmentation visualization on trust, situation awareness, and cognitive load in highly automated vehicles

Mark Colley, Benjamin Eder, Jan Ole Rixen, and Enrico Rukzio. Effects of semantic segmentation visualization on trust, situation awareness, and cognitive load in highly automated vehicles. In Proceedings of the 2021 CHI conference on human factors in computing systems, pages 1...

2021
[47]

Little road driving hud: Heads-up display complexity influences drivers’ perceptions of automated vehicles

Rebecca Currano, So Yeon Park, Dylan James Moore, Kent Lyons, and David Sirkin. Little road driving hud: Heads-up display complexity influences drivers’ perceptions of automated vehicles. In Proceedings of the 2021 CHI conference on human factors in computing systems, pages 1–15, 2021

2021
[48]

Knowing me, knowing you: theory of mind in ai

Fabio Cuzzolin, Alice Morelli, Bogdan Cirstea, and Barbara J Sahakian. Knowing me, knowing you: theory of mind in ai. Psychological medicine, 50(7):1057–1061, 2020

2020
[49]

Cooperative ai: machines must learn to find common ground, 2021

Allan Dafoe, Yoram Bachrach, Gillian Hadfield, Eric Horvitz, Kate Larson, and Thore Graepel. Cooperative ai: machines must learn to find common ground, 2021

2021
[50]

Opportunities and challenges in explainable artificial intelli- gence (xai): A survey

Arun Das and Paul Rad. Opportunities and challenges in explainable artificial intelli- gence (xai): A survey. arXiv preprint arXiv:2006.11371 , 2020

2006 arXiv
[51]

Amazon scraps secret ai recruiting tool that showed bias against women

Jeffrey Dastin. Amazon scraps secret ai recruiting tool that showed bias against women. Reuters, October 10 2018. URL https://www.reuters.com/article/world/ insight-amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women

2018
[52]

Designing an ai-companion to support the driver in highly autonomous cars

Emmanuel De Salis, Marine Capallera, Quentin Meteier, Leonardo Angelini, Omar Abou Khaled, Elena Mugellini, Marino Widmer, and Stefano Carrino. Designing an ai-companion to support the driver in highly autonomous cars. In Human-Computer Interaction. Multimodal and Natural Inte...

2020
[53]

How to increase automated vehicles’ acceptance through in-vehicle interaction design: A review

Henrik Detjen, Sarah Faltaous, Bastian Pfleging, Stefan Geisler, and Stefan Schneegass. How to increase automated vehicles’ acceptance through in-vehicle interaction design: A review. International Journal of Human–Computer Interaction , 37(4):308–330, 2021

2021
[54]

Haptic feedback for the transfer of control in autonomous vehicles

Patrizia Di Campli San Vito, Edward Brown, Stephen Brewster, Frank Pollick, Simon Thompson, Lee Skrypchuk, and Alexandros Mouzakitis. Haptic feedback for the transfer of control in autonomous vehicles. In 12th International Conference on Automotive User Interfaces and Interact...

2020
[55]

Individual risk attitudes: New evidence from a large, representative, experimentally-validated survey

Thomas J Dohmen, Armin Falk, David Huffman, Uwe Sunde, J¨ urgen Schupp, and Gert G Wagner. Individual risk attitudes: New evidence from a large, representative, experimentally-validated survey. 2005

2005
[56]

Carla: An open urban driving simulator

Alexey Dosovitskiy, German Ros, Felipe Codevilla, Antonio Lopez, and Vladlen Koltun. Carla: An open urban driving simulator. In Conference on robot learning, pages 1–16. Pmlr, 2017

2017
[57]

Artificial intelligence for precision education in radiology

Michael Tran Duong, Andreas M Rauschecker, Jeffrey D Rudie, Po-Hao Chen, Tessa S Cook, R Nick Bryan, and Suyash Mohan. Artificial intelligence for precision education in radiology. The British journal of radiology , 92(1103):20190389, 2019

2019
[58]

The role of trust in automation reliance

Mary T Dzindolet, Scott A Peterson, Regina A Pomranky, Linda G Pierce, and Hall P Beck. The role of trust in automation reliance. International journal of human-computer studies, 58(6):697–718, 2003

2003
[59]

The driving cognitions questionnaire: Development and preliminary psychometric properties

Anke Ehlers, Joanne E Taylor, Thomas Ehring, Stefan G Hofmann, Frank P Deane, Walton T Roth, and John V Podd. The driving cognitions questionnaire: Development and preliminary psychometric properties. Journal of anxiety disorders , 21(4):493–509, 2007

2007
[60]

Human-centered explainable ai: Towards a reflective sociotechnical approach

Upol Ehsan and Mark O Riedl. Human-centered explainable ai: Towards a reflective sociotechnical approach. In HCI International 2020-Late Breaking Papers: Multimodal- ity and Intelligence: 22nd HCI International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proc...

2020
[61]

The who in explainable ai: How ai background shapes perceptions of ai explanations

Upol Ehsan, Samir Passi, Q Vera Liao, Larry Chan, I Lee, Michael Muller, Mark O Riedl, et al. The who in explainable ai: How ai background shapes perceptions of ai explanations. arXiv preprint arXiv:2107.13509 , 2021

2021 arXiv
[62]

Creating appropriate trust in automated vehicle systems: A framework for hmi design

Fredrick Ekman, Mikael Johansson, and Jana Sochor. Creating appropriate trust in automated vehicle systems: A framework for hmi design. IEEE Transactions on Human-Machine Systems, 48(1):95–101, 2017

2017
[63]

Toward a theory of situation awareness in dynamic systems

Mica R Endsley. Toward a theory of situation awareness in dynamic systems. Human factors, 37(1):32–64, 1995. 159

1995
[64]

Situation awareness in future autonomous vehicles: Beware of the unexpected

Mica R Endsley. Situation awareness in future autonomous vehicles: Beware of the unexpected. In Congress of the International Ergonomics Association , pages 303–309. Springer, 2018

2018
[65]

Cognitive uncertainty

Benjamin Enke and Thomas Graeber. Cognitive uncertainty. The Quarterly Journal of Economics, 138(4):2021–2067, 2023

2021
[66]

Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations

Daniel J Fagnant and Kara Kockelman. Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77:167–181, 2015

2015
[67]

Analysing the role of chatgpt in improving student productivity in higher education

Fauzi Fauzi, Laros Tuhuteru, Ferdinandus Sampe, Abu Muna Almaududi Ausat, and Heliza Rahmania Hatta. Analysing the role of chatgpt in improving student productivity in higher education. Journal on Education , 5(4):14886–14891, 2023

2023
[68]

Examining accident reports involving autonomous vehicles in california

Francesca M Favar` o, Nazanin Nader, Sky O Eurich, Michelle Tripp, and Naresh Varadaraju. Examining accident reports involving autonomous vehicles in california. PLoS one, 12(9):e0184952, 2017

2017
[69]

An examination of dispositional trust in human and autonomous system interactions

Priscilla Ferronato and Masooda Bashir. An examination of dispositional trust in human and autonomous system interactions. In Human-Computer Interaction. Human Values and Quality of Life: Thematic Area, HCI 2020, Held as Part of the 22nd International Conference, HCII 2020, Co...

2020
[70]

Transition pathways to fully automated driving and its implications for the sociotechnical system of automobility

Eva Fraedrich, Sven Beiker, and Barbara Lenz. Transition pathways to fully automated driving and its implications for the sociotechnical system of automobility. European Journal of Futures Research, 3:1–11, 2015

2015
[71]

A personal resource for technology interaction: development and validation of the affinity for technology interaction (ati) scale

Thomas Franke, Christiane Attig, and Daniel Wessel. A personal resource for technology interaction: development and validation of the affinity for technology interaction (ati) scale. International Journal of Human–Computer Interaction , 35(6):456–467, 2019

2019
[72]

In ux we trust: Investigation of aesthetics and usability of driver-vehicle interfaces and their impact on the perception of automated driving

Anna-Katharina Frison, Philipp Wintersberger, Andreas Riener, Clemens Schartm¨ uller, Linda Ng Boyle, Erika Miller, and Klemens Weigl. In ux we trust: Investigation of aesthetics and usability of driver-vehicle interfaces and their impact on the perception of automated driving...

2019
[73]

Effect size estimates: current use, calculations, and interpretation

Catherine O Fritz, Peter E Morris, and Jennifer J Richler. Effect size estimates: current use, calculations, and interpretation. Journal of experimental psychology: General , 141 (1):2, 2012

2012
[74]

Trust and mistrust in americans’ views of scientific experts

Cary Funk, Meg Hefferon, Brian Kennedy, and Courtney Johnson. Trust and mistrust in americans’ views of scientific experts. Pew Research Center, 2:1–96, 2019. 160

2019
[75]

Explainable active learning (xal) toward ai explanations as interfaces for machine teachers

Bhavya Ghai, Q Vera Liao, Yunfeng Zhang, Rachel Bellamy, and Klaus Mueller. Explainable active learning (xal) toward ai explanations as interfaces for machine teachers. Proceedings of the ACM on Human-Computer Interaction , 4(Cscw3):1–28, 2021

2021
[76]

Trusting explainable autonomous driving: Simulated studies

Claudia V Goldman and Ronit Bustin. Trusting explainable autonomous driving: Simulated studies. In 2022 IEEE Intelligent Vehicles Symposium (IV) , pages 1255–1260. Ieee, 2022

2022
[77]

Darpa’s explainable artificial intelligence (xai) program

David Gunning and David Aha. Darpa’s explainable artificial intelligence (xai) program. AI magazine, 40(2):44–58, 2019

2019
[78]

Effects of explanation types and perceived risk on trust in autonomous vehicles

Taehyun Ha, Sangyeon Kim, Donghak Seo, and Sangwon Lee. Effects of explanation types and perceived risk on trust in autonomous vehicles. Transportation research part F: traffic psychology and behaviour , 73:271–280, 2020

2020
[79]

Personality and the autonomous vehicle: Overcoming psychological barriers to the driverless car

Yair Amichai Hamburger, Yaron Sela, Sharon Kaufman, Tamar Wellingstein, Noy Stein, and Joel Sivan. Personality and the autonomous vehicle: Overcoming psychological barriers to the driverless car. Technology in Society, 69:101971, 2022

2022
[80]

How and why humans trust: A meta-analysis and elaborated model

PA Hancock, Theresa T Kessler, Alexandra D Kaplan, Kimberly Stowers, J Christopher Brill, Deborah R Billings, Kristin E Schaefer, and James L Szalma. How and why humans trust: A meta-analysis and elaborated model. Frontiers in psychology, 14, 2023

2023
[81]

A meta-analysis of factors affecting trust in human- robot interaction

Peter A Hancock, Deborah R Billings, Kristin E Schaefer, Jessie YC Chen, Ewart J De Visser, and Raja Parasuraman. A meta-analysis of factors affecting trust in human- robot interaction. Human factors, 53(5):517–527, 2011

2011
[82]

Nasa-task load index (nasa-tlx); 20 years later

Sandra G Hart. Nasa-task load index (nasa-tlx); 20 years later. In Proceedings of the human factors and ergonomics society annual meeting , volume 50, pages 904–908. Sage publications Sage CA: Los Angeles, CA, 2006

2006
[83]

Interpreting black-box models: a review on explainable artificial intelligence

Vikas Hassija, Vinay Chamola, Atmesh Mahapatra, Abhinandan Singal, Divyansh Goel, Kaizhu Huang, Simone Scardapane, Indro Spinelli, Mufti Mahmud, and Amir Hussain. Interpreting black-box models: a review on explainable artificial intelligence. Cognitive Computation, 16(1):45–74, 2024

2024
[84]

Modelling perceived risk and trust in driving automation reacting to merging and braking vehicles

Xiaolin He, Jork Stapel, Meng Wang, and Riender Happee. Modelling perceived risk and trust in driving automation reacting to merging and braking vehicles. Transportation research part F: traffic psychology and behaviour , 86:178–195, 2022

2022
[85]

In automatic we trust: investigating the impact of trust, control, personality characteristics, and extrinsic and intrinsic motivations on the acceptance of autonomous vehicles

Sabrina M Hegner, Ardion D Beldad, and Gary J Brunswick. In automatic we trust: investigating the impact of trust, control, personality characteristics, and extrinsic and intrinsic motivations on the acceptance of autonomous vehicles. International Journal 161 of Human–Compute...

2019
[86]

Assessing public perception of self-driving cars: The autonomous vehicle acceptance model

Charlie Hewitt, Ioannis Politis, Theocharis Amanatidis, and Advait Sarkar. Assessing public perception of self-driving cars: The autonomous vehicle acceptance model. In Proceedings of the 24th international conference on intelligent user interfaces , pages 518–527, 2019

2019
[87]

Trust in automation: Integrating empirical evidence on factors that influence trust

Kevin Anthony Hoff and Masooda Bashir. Trust in automation: Integrating empirical evidence on factors that influence trust. Human factors, 57(3):407–434, 2015

2015
[88]

Metrics for explainable ai: Challenges and prospects

Robert R Hoffman, Shane T Mueller, Gary Klein, and Jordan Litman. Metrics for explainable ai: Challenges and prospects. arXiv preprint arXiv:1812.04608 , 2018

2018 arXiv
[89]

How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups

Christoph Hohenberger, Matthias Sp¨ orrle, and Isabell M Welpe. How and why do men and women differ in their willingness to use automated cars? the influence of emotions across different age groups. Transportation Research Part A: Policy and Practice , 94: 374–385, 2016

2016
[90]

Situational trust scale for automated driving (sts-ad): Development and initial valida- tion

Brittany E Holthausen, Philipp Wintersberger, Bruce N Walker, and Andreas Riener. Situational trust scale for automated driving (sts-ad): Development and initial valida- tion. In 12th international conference on automotive user interfaces and interactive vehicular applications...

2020
[91]

Causability and explainability of artificial intelligence in medicine

Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, and Heimo M¨ uller. Causability and explainability of artificial intelligence in medicine. Wiley Interdisci- plinary Reviews: Data Mining and Knowledge Discovery , 9(4):e1312, 2019

2019
[92]

Artificial intelligence in radiology

Ahmed Hosny, Chintan Parmar, John Quackenbush, Lawrence H Schwartz, and Hugo JWL Aerts. Artificial intelligence in radiology. Nature Reviews Cancer , 18 (8):500–510, 2018

2018
[93]

Public perceptions of self-driving cars: The case of berkeley, california

Daniel Howard and Danielle Dai. Public perceptions of self-driving cars: The case of berkeley, california. In Transportation research board 93rd annual meeting, volume 14, pages 1–16. The National Academies of Sciences, Engineering, and Medicine Washington, DC . . . , 2014

2014
[94]

Exploring factors related to drivers’ mental model of and trust in advanced driver assistance systems using an abn-based mixed approach

Chunxi Huang, Jiyao Wang, Song Yan, and Dengbo He. Exploring factors related to drivers’ mental model of and trust in advanced driver assistance systems using an abn-based mixed approach. IEEE Transactions on Human-Machine Systems , 2024

2024
[95]

People’s attitudes to autonomous vehicles

John Hudson, Marta Orviska, and Jan Hunady. People’s attitudes to autonomous vehicles. Transportation research part A: policy and practice, 121:164–176, 2019

2019
[96]

Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age

Lynn M Hulse, Hui Xie, and Edwin R Galea. Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age. Safety science, 102:1–13, 2018. 162

2018
[97]

A critique of the sae conditional driving automation definition, and analyses of options for improvement

Toshiyuki Inagaki and Thomas B Sheridan. A critique of the sae conditional driving automation definition, and analyses of options for improvement. Cognition, technology & work, 21:569–578, 2019

2019
[98]

Aws amazon polly

Amazon Web Services Inc. Aws amazon polly. https://aws.amazon.com/polly/, 2023. URL https://aws.amazon.com/polly/

2023
[99]

Tesla Model Y Owner’s Manual

Tesla Inc. Tesla Model Y Owner’s Manual. https://www.tesla.com/ ownersmanual/modely/en us/GUID-2CB60804-9CEA-4F4B-8B04-09B991368DC5. html. URL https://www.tesla.com/ownersmanual/modely/en us/ GUID-2CB60804-9CEA-4F4B-8B04-09B991368DC5.html. Accessed: 2024-07- 22

2024
[100]

Chex- pert: A large chest radiograph dataset with uncertainty labels and expert comparison

Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al. Chex- pert: A large chest radiograph dataset with uncertainty labels and expert comparison. In Proceedings of the AAA...

2019
[101]

Enhanced auditory menu cues improve dual task performance and are preferred with in-vehicle technologies

Myounghoon Jeon, Benjamin K Davison, Michael A Nees, Jeff Wilson, and Bruce N Walker. Enhanced auditory menu cues improve dual task performance and are preferred with in-vehicle technologies. In Proceedings of the 1st international conference on automotive user interfaces and ...

2009
[102]

Foundations for an empirically determined scale of trust in automated systems

Jiun-Yin Jian, Ann M Bisantz, and Colin G Drury. Foundations for an empirically determined scale of trust in automated systems. International journal of cognitive ergonomics, 4(1):53–71, 2000

2000
[103]

The global landscape of ai ethics guidelines

Anna Jobin, Marcello Ienca, and Effy Vayena. The global landscape of ai ethics guidelines. Nature machine intelligence , 1(9):389–399, 2019

2019
[104]

Trust in artificial intelligence: Meta-analytic findings

Alexandra D Kaplan, Theresa T Kessler, J Christopher Brill, and Peter A Hancock. Trust in artificial intelligence: Meta-analytic findings. Human factors, 65(2):337–359, 2023

2023
[105]

Explainable ai and visual reasoning: Insights from radiology

Robert Kaufman and David Kirsh. Explainable ai and visual reasoning: Insights from radiology. arXiv preprint arXiv:2304.03318 , 2023

2023 arXiv
[106]

Warning this contains misin- formation: The effect of cognitive factors, beliefs, and personality on misinformation warning tag attitudes

Robert Kaufman, Aaron Broukhim, and Michael Haupt. Warning this contains misin- formation: The effect of cognitive factors, beliefs, and personality on misinformation warning tag attitudes. arXiv preprint arXiv:2407.02710 , 2024

2024 arXiv
[107]

What did my car say? impact of autonomous vehicle explanation errors and driving context on comfort, reliance, satisfaction, and driving confidence

Robert Kaufman, Aaron Broukhim, David Kirsh, and Nadir Weibel. What did my car say? impact of autonomous vehicle explanation errors and driving context on comfort, reliance, satisfaction, and driving confidence. arXiv preprint arXiv:2409.05731 , 2024. 163

2024 arXiv
[108]

Effects of multimodal explanations for autonomous driving on driving performance, cognitive load, expertise, confidence, and trust

Robert Kaufman, Jean Costa, and Everlyne Kimani. Effects of multimodal explanations for autonomous driving on driving performance, cognitive load, expertise, confidence, and trust. Scientific reports, 14(1):13061, 2024

2024
[109]

Learning racing from an ai coach: Effects of multimodal autonomous driving explanations on driving performance, cognitive load, expertise, and trust

Robert Kaufman, Jean Costa, and Everlyne Kimani. Learning racing from an ai coach: Effects of multimodal autonomous driving explanations on driving performance, cognitive load, expertise, and trust. arXiv preprint arXiv:2401.04206 , 2024

2024 arXiv
[110]

Developing situational awareness for joint action with autonomous vehicles

Robert Kaufman, David Kirsh, and Nadir Weibel. Developing situational awareness for joint action with autonomous vehicles. arXiv preprint arXiv:2404.11800 , 2024

2024 arXiv
[111]

Predicting trust in autonomous vehicles: Modeling young adult psychosocial traits, risk-benefit attitudes, and driving factors with machine learning

Robert Kaufman, Emi Lee, Manas Satish Bedmutha, David Kirsh, and Nadir Weibel. Predicting trust in autonomous vehicles: Modeling young adult psychosocial traits, risk-benefit attitudes, and driving factors with machine learning. arXiv preprint arXiv:2409.08980, 2024

2024 arXiv
[112]

Cognitive differences in human and ai explanation

Robert A Kaufman and David Kirsh. Cognitive differences in human and ai explanation. In Proceedings of the Annual Meeting of the Cognitive Science Society , volume 44, 2022

2022
[113]

Who’s in the crowd matters: Cognitive factors and beliefs predict misinformation assessment accuracy

Robert A Kaufman, Michael Robert Haupt, and Steven P Dow. Who’s in the crowd matters: Cognitive factors and beliefs predict misinformation assessment accuracy. Proceedings of the ACM on Human-Computer Interaction , 6(Cscw2):1–18, 2022

2022
[114]

Trust and perceived risk: How different manifestations affect the adoption of autonomous vehicles

Zs´ ofia Kenesei, Katalin´Asv´ anyi, L´ aszl´ o K¨ ok´ eny, Melinda J´ aszber´ enyi, M´ ark Miskolczi, Tam´ as Gyulav´ ari, and Jhanghiz Syahrivar. Trust and perceived risk: How different manifestations affect the adoption of autonomous vehicles. Transportation research part A...

2022
[115]

Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in xai user studies

Eoin M Kenny, Courtney Ford, Molly Quinn, and Mark T Keane. Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in xai user studies. Artificial Intelligence, 294:103459, 2021

2021
[116]

Calibrat- ing trust through knowledge: Introducing the concept of informed safety for automation in vehicles

Siddartha Khastgir, Stewart Birrell, Gunwant Dhadyalla, and Paul Jennings. Calibrat- ing trust through knowledge: Introducing the concept of informed safety for automation in vehicles. Transportation research part C: emerging technologies, 96:290–303, 2018

2018
[117]

What and when to explain? on-road evaluation of explanations in highly automated vehicles

Gwangbin Kim, Dohyeon Yeo, Taewoo Jo, Daniela Rus, and SeungJun Kim. What and when to explain? on-road evaluation of explanations in highly automated vehicles. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies , 7(3):1–26, 2023

2023
[118]

Textual explanations for self-driving vehicles

Jinkyu Kim, Anna Rohrbach, Trevor Darrell, John Canny, and Zeynep Akata. Textual explanations for self-driving vehicles. In Proceedings of the European conference on computer vision (ECCV) , pages 563–578, 2018. 164

2018
[119]

Cognitive load theory: Implications of cognitive load theory on the design of learning, 2002

Paul A Kirschner. Cognitive load theory: Implications of cognitive load theory on the design of learning, 2002

2002
[120]

Problem solving and situated cognition

David Kirsh. Problem solving and situated cognition. 2009

2009
[121]

Measurement of trust in automation: A narrative review and reference guide

Spencer C Kohn, Ewart J De Visser, Eva Wiese, Yi-Ching Lee, and Tyler H Shaw. Measurement of trust in automation: A narrative review and reference guide. Frontiers in psychology, 12:604977, 2021

2021
[122]

Why did my car just do that? explaining semi-autonomous driving actions to improve driver understanding, trust, and performance

Jeamin Koo, Jungsuk Kwac, Wendy Ju, Martin Steinert, Larry Leifer, and Clifford Nass. Why did my car just do that? explaining semi-autonomous driving actions to improve driver understanding, trust, and performance. International Journal on Interactive Design and Manufacturing ...

2015
[123]

Scared to trust?–predicting trust in highly automated driving by depres- siveness, negative self-evaluations and state anxiety

Johannes Kraus, David Scholz, Eva-Maria Messner, Matthias Messner, and Martin Baumann. Scared to trust?–predicting trust in highly automated driving by depres- siveness, negative self-evaluations and state anxiety. Frontiers in Psychology, 10:2917, 2020

2020
[124]

What’s driving me? exploration and validation of a hierarchical personality model for trust in automated driving.Human factors, 63(6):1076–1105, 2021

Johannes Kraus, David Scholz, and Martin Baumann. What’s driving me? exploration and validation of a hierarchical personality model for trust in automated driving.Human factors, 63(6):1076–1105, 2021

2021
[125]

What is a complex system? European Journal for Philosophy of Science , 3:33–67, 2013

James Ladyman, James Lambert, and Karoline Wiesner. What is a complex system? European Journal for Philosophy of Science , 3:33–67, 2013

2013
[126]

Diagnostic doubt and artificial intelligence: An inductive field study of radiology work

Sarah Lebovitz. Diagnostic doubt and artificial intelligence: An inductive field study of radiology work. International Conference on Information Systems , 2019

2019
[127]

Trust in automation: Designing for appropriate reliance

John D Lee and Katrina A See. Trust in automation: Designing for appropriate reliance. Human factors, 46(1):50–80, 2004

2004
[128]

Who is included in human perceptions of ai?: Trust and perceived fairness around healthcare ai and cultural mistrust

Min Kyung Lee and Katherine Rich. Who is included in human perceptions of ai?: Trust and perceived fairness around healthcare ai and cultural mistrust. In Proceedings of the 2021 CHI conference on human factors in computing systems , pages 1–14, 2021

2021
[129]

Investigating effects of multimodal explanations using multiple in-vehicle displays for takeover request in conditionally automated driving

Sangwon Lee, Jeonguk Hong, Gyewon Jeon, Jeongmin Jo, Sanghyeok Boo, Hwiseong Kim, Seoyoon Jung, Jieun Park, Inheon Choi, and Sangyeon Kim. Investigating effects of multimodal explanations using multiple in-vehicle displays for takeover request in conditionally automated drivin...

2023
[130]

Towards fully autonomous driving: Systems and algorithms

Jesse Levinson, Jake Askeland, Jan Becker, Jennifer Dolson, David Held, Soeren Kammel, J Zico Kolter, Dirk Langer, Oliver Pink, Vaughan Pratt, et al. Towards fully autonomous driving: Systems and algorithms. In 2011 IEEE intelligent vehicles 165 symposium (IV), pages 163–168. ...

2011
[131]

Personality openness predicts driver trust in automated driving

Wenmin Li, Nailang Yao, Yanwei Shi, Weiran Nie, Yuhai Zhang, Xiangrong Li, Jiawen Liang, Fang Chen, and Zaifeng Gao. Personality openness predicts driver trust in automated driving. Automotive Innovation, 3:3–13, 2020

2020
[132]

Designing for responsible trust in ai systems: A communication perspective

Q Vera Liao and S Shyam Sundar. Designing for responsible trust in ai systems: A communication perspective. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency, pages 1257–1268, 2022

2022
[133]

Human-centered explainable ai (xai): From algorithms to user experiences

Q Vera Liao and Kush R Varshney. Human-centered explainable ai (xai): From algorithms to user experiences. arXiv preprint arXiv:2110.10790 , 2021

2021 arXiv
[134]

Questioning the ai: informing design practices for explainable ai user experiences

Q Vera Liao, Daniel Gruen, and Sarah Miller. Questioning the ai: informing design practices for explainable ai user experiences. In Proceedings of the 2020 CHI conference on human factors in computing systems , pages 1–15, 2020

2020
[135]

Connecting algorithmic research and usage contexts: a perspective of contextualized evaluation for explainable ai

Q Vera Liao, Yunfeng Zhang, Ronny Luss, Finale Doshi-Velez, and Amit Dhurandhar. Connecting algorithmic research and usage contexts: a perspective of contextualized evaluation for explainable ai. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, vol...

2022
[136]

Driver profile modeling based on driving style, personality traits, and mood states

Xishun Liao, Shashank Mehrotra, Samson Ho, Yuki Gorospe, Xingwei Wu, and Teruhisa Mistu. Driver profile modeling based on driving style, personality traits, and mood states. In 2022 IEEE 25th international conference on intelligent transportation systems (ITSC), pages 709–716....

2022
[137]

Why and why not explanations improve the intelligibility of context-aware intelligent systems

Brian Y Lim, Anind K Dey, and Daniel Avrahami. Why and why not explanations improve the intelligibility of context-aware intelligent systems. In Proceedings of the SIGCHI conference on human factors in computing systems , pages 2119–2128, 2009

2009
[138]

Why these explanations? selecting intelligibility types for explanation goals

Brian Y Lim, Qian Yang, Ashraf M Abdul, and Danding Wang. Why these explanations? selecting intelligibility types for explanation goals. In IUI Workshops, 2019

2019
[139]

What is gender, anyway: a review of the options for operationalising gender

Anna Lindqvist, Marie Gustafsson Send´ en, and Emma A Renstr¨ om. What is gender, anyway: a review of the options for operationalising gender. Psychology & sexuality , 12 (4):332–344, 2021

2021
[140]

Design study on the effect of intelligent vehicles interaction mode on drivers’ cognitive load

Miao Liu and Bo Qi. Design study on the effect of intelligent vehicles interaction mode on drivers’ cognitive load. In International Conference on Human-Computer Interaction, pages 42–57. Springer, 2023

2023
[141]

Public acceptance of fully automated driving: Effects of social trust and risk/benefit perceptions

Peng Liu, Run Yang, and Zhigang Xu. Public acceptance of fully automated driving: Effects of social trust and risk/benefit perceptions. Risk Analysis, 39(2):326–341, 2019. 166

2019
[142]

A generic trust framework for large-scale open systems using machine learning

Xin Liu, Gilles Tredan, and Anwitaman Datta. A generic trust framework for large-scale open systems using machine learning. Computational Intelligence, 30(4):700–721, 2014

2014
[143]

Waymo One

Waymo LLC. Waymo One. https://waymo.com/waymo-one/, 2024. URL https: //waymo.com/waymo-one/. Accessed: 2024-07-22

2024
[144]

Towards adaptive ambient in-vehicle displays and interactions: Insights and design guidelines from the 2015 automotiveui dedicated workshop

Andreas L¨ ocken, Shadan Sadeghian Borojeni, Heiko M¨ uller, Thomas M Gable, Stefano Triberti, Cyriel Diels, Christiane Glatz, Ignacio Alvarez, Lewis Chuang, and Susanne Boll. Towards adaptive ambient in-vehicle displays and interactions: Insights and design guidelines from th...

2015
[145]

Automated or human: Which driver wins the race for the passengers’ trust? examining passenger trust in human-driven and automated vehicles following a dangerous situation

Leonie Lohaus, Marcel Woide, Nicole Damm, Zeynep Demiral, Hannah Friedrich, Anna Pet´ akov´ a, and Francesco Walker. Automated or human: Which driver wins the race for the passengers’ trust? examining passenger trust in human-driven and automated vehicles following a dangerous...

2024
[146]

Towards a generic trust management framework using a machine-learning-based trust model

Jorge L´ opez and Stephane Maag. Towards a generic trust management framework using a machine-learning-based trust model. In 2015 IEEE Trustcom/BigDataSE/ISPA, volume 1, pages 1343–1348. Ieee, 2015

2015
[147]

From local explanations to global understanding with explainable ai for trees

Scott M Lundberg, Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. From local explanations to global understanding with explainable ai for trees. Nature machine intelligence, 2(1):56–67, 2020

2020
[148]

Trust dynamics in human-av (automated vehicle) interaction

Ruikun Luo, Jian Chu, and X Jessie Yang. Trust dynamics in human-av (automated vehicle) interaction. In Extended abstracts of the 2020 CHI conference on human factors in computing systems , pages 1–7, 2020

2020
[149]

Analysing the effects of scenario-based explanations on automated vehicle hmis from objective and subjective perspectives

Jun Ma and Xuejing Feng. Analysing the effects of scenario-based explanations on automated vehicle hmis from objective and subjective perspectives. Sustainability, 16 (1):63, 2023

2023
[150]

Dsac: Distributional soft actor critic for risk-sensitive reinforcement learning

Xiaoteng Ma, Li Xia, Zhengyuan Zhou, Jun Yang, and Qianchuan Zhao. Dsac: Distributional soft actor critic for risk-sensitive reinforcement learning. arXiv preprint arXiv:2004.14547, 2020

2004 arXiv
[151]

Understanding reliance decisions in automated vehicles using random forest analysis

Xingjian Ma, Xizi Xiao, Ranjana Mehta, and Anthony D McDonald. Understanding reliance decisions in automated vehicles using random forest analysis. In Proceedings of the Human Factors and Ergonomics Society Annual Meeting , page 10711813241262982. SAGE Publications Sage CA: Lo...

2024
[152]

The politics of new driving technologies: Political ideology and autonomous vehicle adoption

Elizabeth A Mack, Steven R Miller, Chu-Hsiang Chang, Jenna A Van Fossen, Shelia R 167 Cotten, Peter T Savolainen, and John Mann. The politics of new driving technologies: Political ideology and autonomous vehicle adoption. Telematics and Informatics, 61: 101604, 2021

2021
[153]

The relative roles of visuospatial and linguistic working memory systems in generating inferences during visual narrative comprehension

Joseph P Magliano, Adam M Larson, Karyn Higgs, and Lester C Loschky. The relative roles of visuospatial and linguistic working memory systems in generating inferences during visual narrative comprehension. Memory & Cognition , 44:207–219, 2016

2016
[154]

Technology acceptance model: a literature review from 1986 to 2013

Nikola Maranguni´ c and Andrina Grani´ c. Technology acceptance model: a literature review from 1986 to 2013. Universal access in the information society , 14:81–95, 2015

1986
[155]

Ethical issues in focus by the autonomous vehicles industry

Andreia Martinho, Nils Herber, Maarten Kroesen, and Caspar Chorus. Ethical issues in focus by the autonomous vehicles industry. Transport reviews, 41(5):556–577, 2021

2021
[156]

Identifying objective behavioural measures of expert driver situation awareness

Angus McKerral and Kristen Pammer. Identifying objective behavioural measures of expert driver situation awareness. Accident Analysis & Prevention, 163:106465, 2021

2021
[157]

Best practice guidance for linear mixed-effects models in psychological science

Lotte Meteyard and Robert AI Davies. Best practice guidance for linear mixed-effects models in psychological science. Journal of Memory and Language , 112:104092, 2020

2020
[158]

baby, you can drive my car

Lars Meyer-Waarden and Julien Cloarec. “baby, you can drive my car”: Psycholog- ical antecedents that drive consumers’ adoption of ai-powered autonomous vehicles. Technovation, 109:102348, 2022

2022
[159]

Explanation in artificial intelligence: Insights from the social sciences

Tim Miller. Explanation in artificial intelligence: Insights from the social sciences. Artificial intelligence, 267:1–38, 2019

2019
[160]

Understanding driver-automated vehicle interactions through wizard of oz design improvisation

Brian Ka-Jun Mok, David Sirkin, Srinath Sibi, David Bryan Miller, and Wendy Ju. Understanding driver-automated vehicle interactions through wizard of oz design improvisation. In Driving Assessment Conference, volume 8. University of Iowa, 2015

2015
[161]

The efficient assessment of self- esteem: proposing the brief rosenberg self-esteem scale

Renan P Monteiro, Gabriel Lins de Holanda Coelho, Paul HP Hanel, Emerson Di´ ogenes de Medeiros, and Phillip Dyamond Gomes da Silva. The efficient assessment of self- esteem: proposing the brief rosenberg self-esteem scale. Applied Research in Quality of Life, 17(2):931–947, 2022

2022
[162]

Building trust in autonomous vehicles: Role of virtual reality driving simulators in hmi design

Lia Morra, Fabrizio Lamberti, F Gabriele Prattic´ o, Salvatore La Rosa, and Paolo Montuschi. Building trust in autonomous vehicles: Role of virtual reality driving simulators in hmi design. IEEE Transactions on Vehicular Technology, 68(10):9438– 9450, 2019

2019
[163]

How personality, demographics, and technology affinity affect trust in autonomous vehicles: A case study

Saeedeh Mosaferchi, Rosaria Califano, and Alessandro Naddeo. How personality, demographics, and technology affinity affect trust in autonomous vehicles: A case study. Human Factors in Transportation, 95(95), 2023. 168

2023
[164]

Artificial intelligence to support human instruction

Michael C Mozer, Melody Wiseheart, and Timothy P Novikoff. Artificial intelligence to support human instruction. Proceedings of the National Academy of Sciences , 116 (10):3953–3955, 2019

2019
[165]

Trust in automation: Part i

Bonnie M Muir. Trust in automation: Part i. theoretical issues in the study of trust and human intervention in automated systems. Ergonomics, 37(11):1905–1922, 1994

1905
[166]

Pre-crash scenario typology for crash avoidance research

Wassim G Najm, John D Smith, Mikio Yanagisawa, et al. Pre-crash scenario typology for crash avoidance research. Technical report, United States. Department of Transportation. National Highway Traffic Safety . . . , 2007

2007
[167]

Computers are social actors

Clifford Nass, Jonathan Steuer, and Ellen R Tauber. Computers are social actors. In Proceedings of the SIGCHI conference on Human factors in computing systems , pages 72–78, 1994

1994
[168]

Moodcapture: Depression detection using in-the-wild smartphone images

Subigya Nepal, Arvind Pillai, Weichen Wang, Tess Griffin, Amanda C Collins, Michael Heinz, Damien Lekkas, Shayan Mirjafari, Matthew Nemesure, George Price, et al. Moodcapture: Depression detection using in-the-wild smartphone images. In Proceedings of the CHI Conference on Hum...

2024
[169]

Defining and measuring shared situational awareness

Albert A Nofi. Defining and measuring shared situational awareness. 2000

2000
[170]

The role of domain expertise in user trust and the impact of first impressions with intelligent systems

Mahsan Nourani, Joanie King, and Eric Ragan. The role of domain expertise in user trust and the impact of first impressions with intelligent systems. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing , volume 8, pages 112–121, 2020

2020
[171]

Why not explain? effects of explanations on human perceptions of autonomous driving

Daniel Omeiza, Konrad Kollnig, Helena Web, Marina Jirotka, and Lars Kunze. Why not explain? effects of explanations on human perceptions of autonomous driving. In 2021 IEEE International Conference on Advanced Robotics and Its Social Impacts (ARSO), pages 194–199. Ieee, 2021

2021
[172]

Towards accountability: providing intelligible explanations in autonomous driving

Daniel Omeiza, Helena Web, Marina Jirotka, and Lars Kunze. Towards accountability: providing intelligible explanations in autonomous driving. In 2021 IEEE Intelligent Vehicles Symposium (IV), pages 231–237. Ieee, 2021

2021
[173]

Explanations in autonomous driving: A survey

Daniel Omeiza, Helena Webb, Marina Jirotka, and Lars Kunze. Explanations in autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(8):10142–10162, 2021

2021
[174]

It’s complicated: The relationship between user trust, model accuracy and explanations in ai

Andrea Papenmeier, Dagmar Kern, Gwenn Englebienne, and Christin Seifert. It’s complicated: The relationship between user trust, model accuracy and explanations in ai. ACM Transactions on Computer-Human Interaction (TOCHI) , 29(4):1–33, 2022

2022
[175]

Learning styles: 169 Concepts and evidence

Harold Pashler, Mark McDaniel, Doug Rohrer, and Robert Bjork. Learning styles: 169 Concepts and evidence. Psychological science in the public interest , 9(3):105–119, 2008

2008
[176]

Influencing human–ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness

Pat Pataranutaporn, Ruby Liu, Ed Finn, and Pattie Maes. Influencing human–ai interaction by priming beliefs about ai can increase perceived trustworthiness, empathy and effectiveness. Nature Machine Intelligence , 5(10):1076–1086, 2023

2023
[177]

Expert-informed, user-centric explanations for machine learning

Michael Pazzani, Severine Soltani, Robert Kaufman, Samson Qian, and Albert Hsiao. Expert-informed, user-centric explanations for machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence , volume 36, pages 12280–12286, 2022

2022
[178]

The book of why: the new science of cause and effect

Judea Pearl and Dana Mackenzie. The book of why: the new science of cause and effect . Basic books, 2018

2018
[179]

Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them

Thomas Pl ¨Otz. Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them. ACM Computing Surveys (CSUR), 54(6):1–25, 2021

2021
[180]

Does the chimpanzee have a theory of mind? Behavioral and brain sciences , 1(4):515–526, 1978

David Premack and Guy Woodruff. Does the chimpanzee have a theory of mind? Behavioral and brain sciences , 1(4):515–526, 1978

1978
[181]

Qualtrics: Online survey software, 2024

Qualtrics. Qualtrics: Online survey software, 2024. URL https://www.qualtrics.com. Provo, UT, USA. https://www.qualtrics.com

2024
[182]

Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving

Vishnu Radhakrishnan, Natasha Merat, Tyron Louw, Rafael Cirino Gon¸ calves, Guil- hermina Torrao, Wei Lyu, Pablo Puente Guillen, and Michael G Lenn´ e. Physiological indicators of driver workload during car-following scenarios and takeovers in highly automated driving. Transpo...

2022
[183]

Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning.arXiv preprint arXiv:1711.05225, 2017

Pranav Rajpurkar, Jeremy Irvin, Kaylie Zhu, Brandon Yang, Hershel Mehta, Tony Duan, Daisy Ding, Aarti Bagul, Curtis Langlotz, Katie Shpanskaya, et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning.arXiv preprint arXiv:1711.05225, 2017

2017 arXiv
[184]

Measuring personality in one minute or less: A 10-item short version of the big five inventory in english and german

Beatrice Rammstedt and Oliver P John. Measuring personality in one minute or less: A 10-item short version of the big five inventory in english and german. Journal of research in Personality, 41(1):203–212, 2007

2007
[185]

” why should i trust you?” explaining the predictions of any classifier

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. ” why should i trust you?” explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , pages 1135–1144, 2016

2016
[186]

Item selection and validation of a brief, 15-item version of the need for closure scale

Arne Roets and Alain Van Hiel. Item selection and validation of a brief, 15-item version of the need for closure scale. Personality and individual differences , 50(1):90–94, 2011. 170

2011
[187]

Englishbot: An ai-powered conversational system for second language learning

Sherry Ruan, Liwei Jiang, Qianyao Xu, Zhiyuan Liu, Glenn M Davis, Emma Brunskill, and James A Landay. Englishbot: An ai-powered conversational system for second language learning. In 26th international conference on intelligent user interfaces , pages 434–444, 2021

2021
[188]

Enhancing trust in autonomous vehicles through intelligent user interfaces that mimic human behavior

Peter AM Ruijten, Jacques MB Terken, and Sanjeev N Chandramouli. Enhancing trust in autonomous vehicles through intelligent user interfaces that mimic human behavior. Multimodal Technologies and Interaction, 2(4):62, 2018

2018
[189]

Person-centered predictions of psychological constructs with social media contextualized by multimodal sensing

Koustuv Saha, Ted Grover, Stephen M Mattingly, Vedant Das Swain, Pranshu Gupta, Gonzalo J Martinez, Pablo Robles-Granda, Gloria Mark, Aaron Striegel, and Munmun De Choudhury. Person-centered predictions of psychological constructs with social media contextualized by multimodal...

2021
[190]

Situation awareness in team performance: Implications for measurement and training

Eduardo Salas, Carolyn Prince, David P Baker, and Lisa Shrestha. Situation awareness in team performance: Implications for measurement and training. Human factors, 37 (1):123–136, 1995

1995
[191]

A missing piece in the puzzle: Considering the role of task complexity in human-ai decision making

Sara Salimzadeh, Gaole He, and Ujwal Gadiraju. A missing piece in the puzzle: Considering the role of task complexity in human-ai decision making. In Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization , pages 215–227, 2023

2023
[192]

Distributed situation awareness: From awareness in individuals and teams to the awareness of technologies, sociotechnical systems, and societies

Paul M Salmon and Katherine L Plant. Distributed situation awareness: From awareness in individuals and teams to the awareness of technologies, sociotechnical systems, and societies. Applied Ergonomics, 98:103599, 2022

2022
[193]

13 distributed situation awareness and vehicle automation

Paul M Salmon, Neville A Stanton, and Guy H Walker. 13 distributed situation awareness and vehicle automation. Handbook of human factors for automated, connected, and intelligent vehicles , 2020

2020
[194]

Measuring and predicting shared situation awareness in teams

Lelyn D Saner, Cheryl A Bolstad, Cleotilde Gonzalez, and Haydee M Cuevas. Measuring and predicting shared situation awareness in teams. Journal of cognitive engineering and decision making , 3(3):280–308, 2009

2009
[195]

Text comprehension: Heads-up vs

Clemens Schartm¨ uller, Klemens Weigl, Philipp Wintersberger, Andreas Riener, and Marco Steinhauser. Text comprehension: Heads-up vs. auditory displays: Implications for a productive work environment in sae level 3 automated vehicles. In Proceedings of the 11th International C...

2019
[196]

Context-aware computing applications

Bill Schilit, Norman Adams, and Roy Want. Context-aware computing applications. In 1994 first workshop on mobile computing systems and applications , pages 85–90. Ieee, 1994. 171

1994
[197]

Personalized explanation in machine learning: A conceptualization

Johanes Schneider and Joshua Handali. Personalized explanation in machine learning: A conceptualization. arXiv preprint arXiv:1901.00770 , 2019

1901 arXiv
[198]

Waymo public road safety performance data

Matthew Schwall, Tom Daniel, Trent Victor, Francesca Favaro, and Henning Hohnhold. Waymo public road safety performance data. arXiv preprint arXiv:2011.00038 , 2020

2011 arXiv
[199]

Decision-making style: The development and assessment of a new measure

Susanne G Scott and Reginald A Bruce. Decision-making style: The development and assessment of a new measure. Educational and psychological measurement, 55(5): 818–831, 1995

1995
[200]

Joint action: bodies and minds moving together

Natalie Sebanz, Harold Bekkering, and G¨ unther Knoblich. Joint action: bodies and minds moving together. Trends in cognitive sciences, 10(2):70–76, 2006

2006
[201]

Differential impact of autonomous vehicle malfunctions on human trust

Manuel Seet, Jonathan Harvy, Rohit Bose, Andrei Dragomir, Anastasios Bezerianos, and Nitish Thakor. Differential impact of autonomous vehicle malfunctions on human trust. IEEE transactions on intelligent transportation systems , 23(1):548–557, 2020

2020
[202]

Dreye vr: democratizing vir- tual reality driving simulation for behavioural & interaction research

Gustavo Silvera, Abhijat Biswas, and Henny Admoni. Dreye vr: democratizing vir- tual reality driving simulation for behavioural & interaction research. In 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) , pages 639–

2022
[203]

Deep inside convolutional networks: Visualising image classification models and saliency maps

Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034, 2013

2013 arXiv
[204]

Critical reasons for crashes investigated in the national motor vehicle crash causation survey

Santokh Singh. Critical reasons for crashes investigated in the national motor vehicle crash causation survey. Technical report, 2015

2015
[205]

User-centric enhancements to explainable ai algorithms for image classification

Severine Soltani, Robert A Kaufman, and Michael J Pazzani. User-centric enhancements to explainable ai algorithms for image classification. In Proceedings of the Annual Meeting of the Cognitive Science Society , volume 44, 2022

2022
[206]

Investigating the effect of childhood socioeconomic background on interpersonal trust: Lower childhood socioe- conomic status predicts lower levels of trust

Angelos Stamos, Efthymios Altsitsiadis, and Siegfried Dewitte. Investigating the effect of childhood socioeconomic background on interpersonal trust: Lower childhood socioe- conomic status predicts lower levels of trust. Personality and Individual Differences , 145:19–25, 2019

2019
[207]

Situational awareness and safety

Neville A Stanton, Peter RG Chambers, and John Piggott. Situational awareness and safety. Safety science, 39(3):189–204, 2001

2001
[208]

Distributed situation awareness in dynamic systems: theoretical development and application of an ergonomics methodology

Neville A Stanton, Rebecca Stewart, Don Harris, Robert J Houghton, Chris Baber, Richard McMaster, Paul Salmon, Geoff Hoyle, Guy Walker, Mark S Young, et al. Distributed situation awareness in dynamic systems: theoretical development and application of an ergonomics methodology...

2006
[209]

Trust in ambient assisted living (aal)-a systematic review of trust in automation and assistance systems

Frederick Steinke, Tobias Fritsch, and Lina Silbermann. Trust in ambient assisted living (aal)-a systematic review of trust in automation and assistance systems. International Journal on Advances in Life Sciences , 4(3-4), 2012

2012
[210]

The multidimensional driving style inventory—scale construct and validation

Orit Taubman-Ben-Ari, Mario Mikulincer, and Omri Gillath. The multidimensional driving style inventory—scale construct and validation. Accident Analysis & Prevention, 36(3):323–332, 2004

2004
[211]

Understanding the intention to use technology by preservice teachers: An empirical test of competing theoretical models

Timothy Teo and Paul Van Schaik. Understanding the intention to use technology by preservice teachers: An empirical test of competing theoretical models. International Journal of Human-Computer Interaction , 28(3):178–188, 2012

2012
[212]

Linear discriminant analysis: A detailed tutorial

Alaa Tharwat, Tarek Gaber, Abdelhameed Ibrahim, and Aboul Ella Hassanien. Linear discriminant analysis: A detailed tutorial. AI communications, 30(2):169–190, 2017

2017
[213]

Regression shrinkage and selection via the lasso

Robert Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society Series B: Statistical Methodology , 58(1):267–288, 1996

1996
[214]

Explainable Agency in Artificial Intelligence: Research and Practice

Silvia Tulli and David W Aha. Explainable Agency in Artificial Intelligence: Research and Practice. CRC Press, 2024

2024
[215]

Multimodal interaction: A review

Matthew Turk. Multimodal interaction: A review. Pattern recognition letters, 36: 189–195, 2014

2014
[216]

Declines in trust in others and confidence in institutions among american adults and late adolescents, 1972–2012

Jean M Twenge, W Keith Campbell, and Nathan T Carter. Declines in trust in others and confidence in institutions among american adults and late adolescents, 1972–2012. Psychological science, 25(10):1914–1923, 2014

1972
[217]

Differences between racing and non-racing drivers: A simulator study using eye-tracking

Peter M Van Leeuwen, Stefan De Groot, Riender Happee, and Joost CF De Winter. Differences between racing and non-racing drivers: A simulator study using eye-tracking. PLoS one, 12(11):e0186871, 2017

2017
[218]

Explanations can reduce overreliance on ai systems during decision-making

Helena Vasconcelos, Matthew J¨ orke, Madeleine Grunde-McLaughlin, Tobias Gersten- berg, Michael S Bernstein, and Ranjay Krishna. Explanations can reduce overreliance on ai systems during decision-making. Proceedings of the ACM on Human-Computer Interaction, 7(Cscw1):1–38, 2023

2023
[219]

Integrating transparency, trust, and acceptance: The intelligent systems technology acceptance model (istam)

Eric S Vorm and David JY Combs. Integrating transparency, trust, and acceptance: The intelligent systems technology acceptance model (istam). International Journal of Human–Computer Interaction, 38(18-20):1828–1845, 2022

2022
[220]

Designing theory-driven user-centric explainable ai

Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y Lim. Designing theory-driven user-centric explainable ai. In Proceedings of the 2019 CHI conference on human factors in computing systems , pages 1–15, 2019. 173

2019
[221]

A survey on trust evaluation based on machine learning

Jingwen Wang, Xuyang Jing, Zheng Yan, Yulong Fu, Witold Pedrycz, and Laurence T Yang. A survey on trust evaluation based on machine learning. ACM Computing Surveys (CSUR), 53(5):1–36, 2020

2020
[222]

Towards mutual theory of mind in human-ai interaction: How language reflects what students perceive about a virtual teaching assistant

Qiaosi Wang, Koustuv Saha, Eric Gregori, David Joyner, and Ashok Goel. Towards mutual theory of mind in human-ai interaction: How language reflects what students perceive about a virtual teaching assistant. In Proceedings of the 2021 CHI conference on human factors in computin...

2021
[223]

Immediate perceptual response to intersensory discrepancy

Robert B Welch and David H Warren. Immediate perceptual response to intersensory discrepancy. Psychological bulletin, 88(3):638, 1980

1980
[224]

Processing resources and attention

Christopher D Wickens. Processing resources and attention. In Multiple task perfor- mance, pages 3–34. CRC Press, 2020

2020
[225]

I drive-you trust: Explaining driving behavior of autonomous cars

Gesa Wiegand, Matthias Schmidmaier, Thomas Weber, Yuanting Liu, and Heinrich Hussmann. I drive-you trust: Explaining driving behavior of autonomous cars. In Extended abstracts of the 2019 chi conference on human factors in computing systems , pages 1–6, 2019

2019
[226]

Towards a personalized trust model for highly automated driving.Mensch und Computer 2016–Workshopband, 2016

Philipp Wintersberger, Anna-Katharina Frison, Andreas Riener, and Linda Ng Boyle. Towards a personalized trust model for highly automated driving.Mensch und Computer 2016–Workshopband, 2016

2016
[227]

Racing line optimization

Ying Xiong et al. Racing line optimization . PhD thesis, Massachusetts Institute of Technology, 2010

2010
[228]

Hallucination is inevitable: An innate limitation of large language models

Ziwei Xu, Sanjay Jain, and Mohan Kankanhalli. Hallucination is inevitable: An innate limitation of large language models. arXiv preprint arXiv:2401.11817 , 2024

2024 arXiv
[229]

Choosing prediction over explanation in psychology: Lessons from machine learning

Tal Yarkoni and Jacob Westfall. Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6):1100–1122, 2017

2017
[230]

The relation of strength of stimulus to rapidity of habit-formation

Robert Mearns Yerkes, John D Dodson, et al. The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology , 1908

1908
[231]

Measurement and real-time recognition of driver trust in conditionally automated vehicles: Using multimodal feature fusions network

Binlin Yi, Haotian Cao, Xiaolin Song, Jianqiang Wang, Wenfeng Guo, and Zhi Huang. Measurement and real-time recognition of driver trust in conditionally automated vehicles: Using multimodal feature fusions network. Transportation research record, 2677(8):311–330, 2023

2023
[232]

Measuring hofstede’s five dimensions of cultural values at the individual level: Development and validation of cvscale

Boonghee Yoo, Naveen Donthu, and Tomasz Lenartowicz. Measuring hofstede’s five dimensions of cultural values at the individual level: Development and validation of cvscale. Journal of international consumer marketing , 23(3-4):193–210, 2011. 174

2011
[233]

Human acceptance of autonomous vehicles: Research status and prospects

Qidi Zhang, Tingru Zhang, and Liang Ma. Human acceptance of autonomous vehicles: Research status and prospects. International journal of industrial ergonomics , 95: 103458, 2023

2023
[234]

Effects of driving style on takeover performance during automated driving: Under the influence of warning system factors

Yaping Zhang, Qianli Ma, Jianhong Qu, and Ronggang Zhou. Effects of driving style on takeover performance during automated driving: Under the influence of warning system factors. Applied Ergonomics, 117:104229, 2024

2024
[235]

Trust in automated vehicle: A meta-analysis

Zhengming Zhang, Renran Tian, and Vincent G Duffy. Trust in automated vehicle: A meta-analysis. In Human-Automation Interaction: Transportation, pages 221–234. Springer, 2022

2022
[236]

The effectiveness of artificial intelligence on learning achievement and learning perception: A meta-analysis

Lanqin Zheng, Jiayu Niu, Lu Zhong, and Juliana Fosua Gyasi. The effectiveness of artificial intelligence on learning achievement and learning perception: A meta-analysis. Interactive Learning Environments, 31(9):5650–5664, 2023

2023
[237]

A syntactical approach for interpersonal trust prediction in social web applications: Combining contextual and structural data

Kiyana Zolfaghar and Abdollah Aghaie. A syntactical approach for interpersonal trust prediction in social web applications: Combining contextual and structural data. Knowledge-Based Systems, 26:93–102, 2012. 175

2012

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

URL https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813473.pdf

National highway traffic safety administration traffic safety facts: 2021 data, 2021. URL https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813473.pdf

2021

[2] [2]

Autonomous vehicles and alternatives to driving: trust, preferences, and effects of age

Hillary Abraham, Chaiwoo Lee, Samantha Brady, Craig Fitzgerald, Bruce Mehler, Bryan Reimer, and Joseph F Coughlin. Autonomous vehicles and alternatives to driving: trust, preferences, and effects of age. In Proceedings of the transportation research board 96th annual meeting , pages 8–12. Transportation Research Board Washington, DC, 2017

2017

[3] [3]

Case study of today’s automotive dealerships: Introduction and delivery of advanced driver assistance systems

Hillary Abraham, Hale McAnulty, Bruce Mehler, and Bryan Reimer. Case study of today’s automotive dealerships: Introduction and delivery of advanced driver assistance systems. Transportation research record, 2660(1):7–14, 2017

2017

[4] [4]

Ransford A Acheampong and Federico Cugurullo. Capturing the behavioural deter- minants behind the adoption of autonomous vehicles: Conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars. Transportation research part F: traffic psychology and behaviour , 62: 349–375, 2019

2019

[5] [5]

Risk and trust in artificial intelligence technologies: A case study of autonomous vehicles

Ighoyota Ben Ajenaghughrure, Sonia Claudia da Costa Sousa, and David Lamas. Risk and trust in artificial intelligence technologies: A case study of autonomous vehicles. In 2020 13th international conference on human system interaction (HSI) , pages 118–123. Ieee, 2020

2020

[6] [6]

A classification model for sensing human trust in machines using eeg and gsr

Kumar Akash, Wan-Lin Hu, Neera Jain, and Tahira Reid. A classification model for sensing human trust in machines using eeg and gsr. ACM Transactions on Interactive Intelligent Systems (TiiS) , 8(4):1–20, 2018

2018

[7] [7]

Incorporating personality in user interface design: A review

Tom´ as Alves, Joana Nat´ alio, Joana Henriques-Calado, and Sandra Gama. Incorporating personality in user interface design: A review. Personality and Individual Differences , 155:109709, 2020

2020

[8] [8]

The future of well-being in a tech-saturated world

Janna Anderson and Lee Rainie. The future of well-being in a tech-saturated world. 2018. 154

2018