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REVIEW 4 major objections 7 minor 27 references

Evaluating Driver Perceptions of Integrated Safety Monitoring Systems for Alcohol Impairment and Distraction

T0 review · 4 major / 7 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This paper reports that U.S. drivers prefer non-intrusive eye-movement monitors over breath-alcohol interlocks, and that privacy and trust are the main conditions for accepting in-car impairment monitoring.

desk verdict Central claim is contradicted by the paper's own reported means, so the useful survey data is buried under an unsupported conclusion. read the letter →

arxiv 2505.22969 v1 pith:XKGW3OWJ submitted 2025-05-29 cs.HC

RoshikNagaSai Patibandla , Ross Greer This is my paper
classification cs.HC
keywords drivermonitoringsystemsalcohol-impaireddrivingdistractedBrACignitioninterlockeyetrackingsurveyresearchacceptanceprivacy
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 paper tries to establish how U.S. drivers would respond to integrated safety systems that monitor alcohol impairment and distraction, at a moment when federal law is pushing such systems into new cars around 2026–2027. Based on a survey of 115 respondents, it argues that acceptance of these systems is real but conditional: drivers are more comfortable with non-intrusive monitoring such as eye-movement tracking than with restrictive breath-alcohol devices, and they strongly prefer local data processing with anonymized identity. It also argues that familiarity with the technology raises acceptance, while trust in a system's accuracy is what makes drivers willing to change their behavior and tolerate errors. If these results hold, they give manufacturers and regulators a concrete agenda—transparency, privacy safeguards, and public education—for making mandated safety monitors more acceptable.

What carries the argument

The argument runs through the survey instrument and the derived Individual Acceptance Score (IAS), defined as the mean of seven 1–5 responses: comfort with eye-movement monitoring (q9), posture and hand tracking (q10), an integrated BrAC ignition lock (q11), passive BrAC monitoring (q12), trust in system accuracy (q13), likelihood of changing driving behavior after alerts (q14), and overall appeal of the full monitoring package (q24). The Average Acceptance Score (AAS) is the class-average of IAS values, and it is the tool that produces the paper's comparative findings: more aware respondents score higher, while education, income, and automation level show no clear effect. The survey also feeds separate analyses of privacy preference, acceptable error rates, and preferred system responses.

What would settle it

A reader can check the abstract's central claim against the paper's own results section: mean comfort was 3.72 for an integrated BrAC ignition lock and 3.66 for passive BrAC monitoring, versus 3.24 for eye-movement monitoring and 3.17 for posture/hand tracking. If those reported means stand, the data contradict the stated preference for non-intrusive eye-movement systems over alcohol-detection devices.

Watch

Extended reading notes

Core claim

The paper's central claim is that survey respondents reveal a clear preference ordering over monitoring technologies: camera-based systems that track eye movements and posture are accepted more readily than systems that detect breath-alcohol concentration (BrAC) and lock the ignition. It further claims that privacy is a major barrier to adoption—76.52% of respondents want their identity anonymized during local processing and 83.33% before cloud processing—and that trust in system accuracy is what makes drivers willing to adapt their behavior and tolerate false positives or missed detections. The study introduces the Individual Acceptance Score (IAS), the average of seven 1–5 answers covering comfort, trust, and willingness, and uses the group-level Average Acceptance Score to compare respondents; it reports that acceptance rises with awareness of the technology but shows no strong trend with education, income, or vehicle automation level.

Load-bearing premise

The load-bearing premise is that 115 respondents recruited through research-lab flyers and local tech and education networks represent U.S. drivers; the sample's strong skew toward Asian, highly educated, full-time employed, and high-income respondents means the stated preferences and acceptance levels may not generalize.

Editorial extensions

If this is right

  • If acceptance rises with awareness, public-education campaigns and demonstration programs could raise voluntary adoption before federal rules take effect.
  • If privacy is the main barrier, systems that process data locally and anonymize identity should receive markedly higher acceptance than cloud-processed designs.
  • If trust loosens tolerance for false positives and missed detections, manufacturers can build acceptance through transparency and reliability even when perfect accuracy is not achievable.
  • If drivers prefer non-intrusive monitoring, camera-based eye and posture monitors will face less public resistance than breath-based ignition interlocks in regulatory rollouts.
  • If respondents favor warnings and pulling over over contacting authorities, vehicle response defaults should alert and safely stop the car rather than report the driver.

Reading between the lines

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

  • A paired re-analysis of the raw survey data would test the paper's stated preference ordering, because the tabulated comfort means run the other way: BrAC ignition locks average 3.72 and passive BrAC monitoring 3.66, while eye-movement monitoring averages 3.24 and posture/hand tracking 3.17.
  • Given the sample skew (58.4% Asian, 88.7% bachelor's degree or higher, 79.8% full-time employed, 31% earning over $150,000), the acceptance levels should be re-estimated on a nationally representative sample before being used to predict real U.S. adoption.
  • A natural next step is a field pilot that installs the actual monitoring modalities in vehicles and measures sustained opt-in behavior, which would test whether stated comfort in a survey predicts real acceptance.
  • Because education and income show no trend in acceptance, outreach targeting can focus on awareness and demonstration rather than socioeconomic segments, but the skewed sample makes this implication tentative.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 7 minor

Summary. The paper reports a survey of 115 U.S. respondents on their comfort, trust, privacy concerns, and acceptance of in-vehicle driver monitoring systems for alcohol impairment and distraction, covering eye tracking, posture/hand tracking, active and passive breath-alcohol (BrAC) interlocks, and various system responses. It introduces an Individual Acceptance Score (IAS) and compares groups by awareness, trust, demographics, and vehicle automation level. The paper claims that drivers prefer non-intrusive eye/posture monitoring over restrictive BrAC interlock systems and that privacy, trust, and awareness drive acceptance. However, the reported mean comfort scores contradict the headline preference claim, no statistical tests are reported, and the sample is not representative of the U.S. driving population.

Significance. If the findings were reliable, the study would offer timely, policy-relevant evidence for regulators and manufacturers considering passive impaired-driving detection mandates under the Infrastructure Investment and Jobs Act. The survey instrument is broad and covers important dimensions such as local versus cloud processing, anonymization, and tolerance for false positives and false negatives. However, the central preference claim is contradicted by the paper's own reported means, no inferential statistics are provided, and the sample composition severely limits generalizability. As such, the paper cannot currently support its conclusions, and its potential significance remains unrealized.

major comments (4)
  1. [III-A, Abstract, V] The reported mean comfort scores in Section III-A are q9 (eye tracking) = 3.24, q10 (posture/hand) = 3.17, q11 (active BrAC interlock) = 3.72, and q12 (passive BrAC) = 3.66. The abstract and Section V claim that drivers preferred non-intrusive systems such as eye tracking over restrictive alcohol-detection devices, but the reported means indicate the opposite ordering: both BrAC interlock systems received higher mean comfort ratings than eye tracking or posture/hand tracking. This is an internal contradiction in the core result. The paper provides no paired significance tests or confidence intervals that could reinterpret these differences. If the means are correct, the central claim must be reversed; if the claim is correct, the reported data are miscomputed, mislabeled, or drawn from a different variable than described.
  2. [IV, Eq. (1), Fig. 10] The Individual Acceptance Score (IAS) is computed in Eq. (1) as the unweighted average of q9, q10, q11, q12, q13, q14, and q24. The same items (especially q9–q12) are also the outcome measures for comfort in Section III-A, so the IAS is not an independent acceptance construct but rather a repackaging of the dependent variables. No reliability or validity analysis (e.g., Cronbach's alpha, factor analysis) is provided to justify this aggregation. Moreover, the caption of Figure 10 states that 'the trust question (q13) was removed from the calculation of the acceptance score to prevent correlating variables from affecting each other,' yet Eq. (1) includes q13. This internal inconsistency must be resolved because all group comparisons in Figures 3–10 and the associated discussion rely on the IAS.
  3. [IV, Figs. 3–10] The paper makes comparative claims such as 'a certain education level is not needed to accept monitoring systems,' 'trust significantly influenced participants' willingness to change their driving behavior,' and 'education level and income level did not have a significant impact on acceptance score,' but it reports no hypothesis tests, confidence intervals, or effect sizes. Without inferential statistics, these statements are unsupported, and the descriptive patterns in Figures 3–10 could be due to sampling error, especially given the small subgroup sizes (e.g., automation Level 4 appears to have very few respondents). The absence of statistical analysis undermines the paper's claims about which factors 'drive' or 'influence' acceptance.
  4. [III (Method), Table II] The sample is described in Table II as 58.4% Asian, 79.8% full-time employed, 88.7% with a bachelor's degree or higher, 31% with household income above $150,000, and 67.5% married, recruited through research labs and local tech/education networks. This composition is far from representative of the U.S. driving population, yet the abstract and conclusion generalize broadly to 'drivers' without qualification. The paper does not discuss this as a limitation or provide any weighting, sensitivity analysis, or comparative benchmark. Consequently, the prevalence estimates (e.g., 76.52% preferring local anonymization, 61.74% preferring local processing) and all policy recommendations lack demonstrated external validity.
minor comments (7)
  1. [I] The sentence 'alcohol-impaired driving caused one death every 45 minutes' should be clarified as a statistical average and should cite the correct NHTSA source year; the following sentence, 'Traffic crashes involving one or more alcohol-impaired drivers kill approximately 32 people every day,' should be checked against the cited report for consistency with the 2020 or 2022 statistics, as the numbers appear to mix different years.
  2. [II-A] In the 'Detection Through Breath & Biological Features' subsection, the sentence beginning 'This study used infrared spectroscopy...' is ambiguous: it refers to the cited work by Jonas Ljungblad et al., not to the authors' own study. Rephrase to 'That study used infrared spectroscopy...' to avoid confusion.
  3. [III, Table I] Several survey question items contain grammatical errors, notably q11 ('How comfortable are with having an integrated BrAC level monitoring system...') and q12 ('How comfortable are with having a passive integrated BrAC level monitoring system...'); these should be 'How comfortable would you be with...' for consistency with q9 and q10.
  4. [IV, Fig. 7] Figure 7 compares reported with actual automation levels, but the text does not explain how 'actual automation level' was determined for all 115 respondents, whether vehicle manuals were available for every vehicle, or how missing data were handled; please provide this detail in the methods or figure caption.
  5. [References] Reference [24] lists 'MomsAgainstDrunkDriving' but the correct name is 'Mothers Against Drunk Driving' (MADD); this should be corrected.
  6. [III-A] The paper states that responses were 'on a scale of 1-5' but never specifies the Likert item anchors (e.g., whether 1 = 'not at all comfortable' and 5 = 'extremely comfortable'). This is essential for interpreting the reported means and should be added to the Methods section.
  7. [Figures 3–6] The figures would be more informative with error bars (e.g., standard error) and sample sizes per group; several subgroups appear to have small n (e.g., automation Level 4), and without this information readers cannot judge the stability of the reported averages.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the Individual Acceptance Score is a definitional composite, not a prediction, and the central preference claim contradicts the reported means rather than reducing to them.

full rationale

The paper's only composite measure, the Individual Acceptance Score (IAS, Section IV, Eq. 1), is defined as the unweighted average of seven survey items (q9-q14, q24). Because it is a descriptive index rather than a fitted parameter or a predicted outcome, using it to compare groups by awareness, education, income, or automation level is not circular: those external demographic and awareness variables are not inputs to the score. The trust-acceptance relationship shown in Figure 10 explicitly removes q13 from the acceptance calculation to avoid a construction-induced correlation, although Eq. 1 still includes q13 in the score, which is an internal inconsistency but not a circular derivation. No parameter is fitted to a subset of the data and then reported as a prediction; no self-citation is load-bearing; and no uniqueness theorem or ansatz is imported from the authors' prior work. The abstract's headline claim that drivers prefer eye-movement monitoring over BrAC interlocks is contradicted by the reported means in Section III-A (q9=3.24 and q10=3.17 versus q11=3.72 and q12=3.66), but that is a data-reporting and correctness problem, not a circularity, because the claim is not obtained by reducing the conclusion to its own inputs. The convenience-sample limitation affects external validity but is not a circularity. Accordingly, no circular step meeting the quoted-equation standard is present, and the appropriate score is 0.

Assumptions & free parameters 1 free parameters · 3 assumptions · 1 invented entities

The paper's central results rest on one hand-chosen composite score and several domain assumptions about self-report and sample representativeness. No external benchmarks are used, and the one invented metric (IAS) lacks validation.

free parameters (1)
  • Equal weights for seven survey items in the Individual Acceptance Score = 1/7 each
    The IAS in Equation 1 gives equal weight to q9, q10, q11, q12, q13, q14, and q24. This weighting is chosen by the authors without justification or validation, and it affects all acceptance comparisons.
assumptions (3)
  • domain assumption Self-reported survey responses truthfully reflect participants' attitudes and behaviors.
    The entire analysis depends on the validity of self-report data, with no objective measures or validation checks. This is stated implicitly in the Method section.
  • domain assumption The recruited sample is representative enough to generalize conclusions to U.S. drivers.
    The authors generalize to 'drivers' in the abstract and conclusion, but the convenience sample is heavily skewed toward Asian, highly educated, high-income, full-time workers, as shown in Table II. This assumption is load-bearing but unmet.
  • standard math Averaging Likert-scale items produces an interval-scale acceptance score.
    The IAS treats ordinal Likert responses as numeric values equally spaced from 1 to 5. This is a common but nontrivial assumption in survey analysis, not stated in the paper.
invented entities (1)
  • Individual Acceptance Score (IAS)
    purpose: A composite metric to quantify a respondent's overall acceptance of driver monitoring systems from seven survey items.
    The IAS is defined in Equation 1 but is not validated against any external measure of acceptance, behavior, or prior scale. It is used throughout the analysis as the primary outcome, so its validity is internal to the paper.

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Cite this review

Pith. "Pith review of Evaluating Driver Perceptions of Integrated Safety Monitoring Systems for Alcohol Impairment and Distraction." pith.science (2026). https://pith.science/paper/XKGW3OWJ

@misc{pith2026250522969,
  author       = {Pith},
  title        = {Pith review of: Evaluating Driver Perceptions of Integrated Safety Monitoring Systems for Alcohol Impairment and Distraction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XKGW3OWJ}},
  note         = {Machine review of arXiv:2505.22969}
}
read the original abstract

The increasing number of accidents caused by alcohol-impaired driving has prompted the development of integrated safety systems in vehicles to monitor driver behavior and prevent crashes. This paper explores how drivers perceive these systems, focusing on their comfort, trust, privacy concerns, and willingness to adopt the technology. Through a survey of 115 U.S. participants, the study reveals a preference for non-intrusive systems, such as those monitoring eye movements, over more restrictive technologies like alcohol detection devices. Privacy emerged as a major concern, with many participants preferring local data processing and anonymity. Trust in these systems was crucial for acceptance, as drivers are more likely to adapt their behavior when they believe the system is accurate and reliable. To encourage adoption, it is important to address concerns about privacy and balance the benefits of safety with personal freedom. By improving transparency, ensuring reliability, and increasing public awareness, these systems could play a significant role in reducing road accidents and improving safety.

Figures

Figures reproduced from arXiv: 2505.22969 by the authors.

Figure 1
Figure 1. This graph compares the number of respondents that chose each [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. This graph compares the number of respondents that chose each [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. This graph compares the average points for the Acceptance Score of [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: This graph compares the average acceptance score with education [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: This graph compares the average acceptance score with income level; [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 8
Figure 8. Figure 8: This graph compares the average percentage of false positives & [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: This graph compares the average points for the Acceptance Score [PITH_FULL_IMAGE:figures/full_fig_p007_9.png]
Figure 10
Figure 10. Figure 10: This graph plots each trust level depending on each acceptance [PITH_FULL_IMAGE:figures/full_fig_p007_10.png]

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Reference graph

Works this paper leans on

27 extracted references · 27 canonical work pages

  1. [1]

    Traffic safety facts 2020 data: Alcohol-impaired driving,

    National Highway Traffic Safety Administration, “Traffic safety facts 2020 data: Alcohol-impaired driving,” Tech. Rep. DOT HS 813 294, U.S. Department of Transportation, Washington, DC, 2021. Accessed: November 13, 2024

    work page 2020

  2. [2]

    Traffic safety facts 2022 data: Alcohol-impaired driving,

    National Highway Traffic Safety Administration, “Traffic safety facts 2022 data: Alcohol-impaired driving,” tech. rep., U.S. Department of Transportation, 2023. Accessed: November 13, 2024. url- https://rosap.ntl.bts.gov/view/dot/78162

    work page 2022

  3. [3]

    The economic and societal impact of motor vehicle crashes, 2010 (revised),

    L. Blincoe, T. R. Miller, E. Zaloshnja, and B. A. Lawrence, “The economic and societal impact of motor vehicle crashes, 2010 (revised),” tech. rep., 2015

    work page 2010

  4. [4]

    Alcohol involvement and other risky driver behaviors: Effects on crash initiation and crash severity,

    K. Shyhalla, “Alcohol involvement and other risky driver behaviors: Effects on crash initiation and crash severity,” Traffic Injury Prevention, vol. 15, no. 4, pp. 325–334, 2014. PMID: 24471355

    work page 2014

  5. [5]

    Alcohol ignition interlock programs,

    D. Beirness and P. Marques, “Alcohol ignition interlock programs,” Traffic Injury Prevention , vol. 5, no. 3, pp. 299–308, 2004. PMID: 15276931

    work page 2004

  6. [6]

    Euro ncap vision 2030: A safer future for mobility,

    European New Car Assessment Programme (Euro NCAP), “Euro ncap vision 2030: A safer future for mobility,” 2023. Out- lines the push for integrating advanced driver monitoring sys- tems to detect impairment and distraction. Accessed: November 13,

    work page 2023

  7. [7]

    Ntsb urges action on im- paired driving detection systems,

    National Transportation Safety Board, “Ntsb urges action on im- paired driving detection systems,” December 2023. Press release. Accessed: November 13, 2024. urlhttps://www.ntsb.gov/news/press- releases/Pages/NR20231211.aspx

    work page 2023

  8. [8]

    Ntsb calls for alcohol impairment detection systems following california crash,

    National Transportation Safety Board, “Ntsb calls for alcohol impairment detection systems following california crash,” September 2022. Press release. Accessed: November 13, 2024. urlhttps://www.ntsb.gov/news/press-releases/Pages/NR20220920.aspx

    work page 2022

Show all 27 references
  1. [9]

    Infrastructure investment and jobs act, public law no: 117-58,

    U. S. Congress, “Infrastructure investment and jobs act, public law no: 117-58,” 2021. urlhttps://www.congress.gov/bill/117th-congress/house- bill/3684

    2021

  2. [10]

    Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving,

    K. Koch, M. Maritsch, E. Van Weenen, S. Feuerriegel, M. Pf ¨affli, E. Fleisch, W. Weinmann, and F. Wortmann, “Leveraging driver vehicle and environment interaction: Machine learning using driver monitoring cameras to detect drunk driving,” in Proceedings of the 2023 CHI Confer...

    2023

  3. [11]

    Gałecki and T

    A. Gałecki and T. Burzykowski, Linear Mixed-Effects Model , pp. 245–

  4. [12]

    A. H. Leyland and P. P. Groenewegen, Intraclass Correlation Coefficient (ICC), pp. 3643–3644. Cham: Springer International Publishing, 2023

    2023

  5. [13]

    Practical aspects of measuring camera-based indicators of alcohol intoxication in manual and automated driving,

    R. Zemblys, C. Ahlstr ¨om, K. Kircher, and S. Fin ´er, “Practical aspects of measuring camera-based indicators of alcohol intoxication in manual and automated driving,” IET Intelligent Transport Systems, vol. 18, no. 8, pp. 1408–1427, 2024

    2024

  6. [14]

    How breathalyzers work,

    C. Freudenrich, “How breathalyzers work,” How Stuff Works, 2011

    2011

  7. [15]

    Assessment of Response of the Intoxilyzer® 8000C to V olatiles of Forensic Relevance In Vitro, Part I: Acetone, Isopropanol, and Methanol,

    J. H. Watterson, “Assessment of Response of the Intoxilyzer® 8000C to V olatiles of Forensic Relevance In Vitro, Part I: Acetone, Isopropanol, and Methanol,” Journal of Analytical Toxicology, vol. 33, pp. 109–117, 03 2009

    2009

  8. [16]

    Passive in-vehicle driver breath alcohol detection using advanced sensor signal acquisition and fusion,

    A. A. Jonas Ljungblad, Bertil H ¨ok and H. Pettersson, “Passive in-vehicle driver breath alcohol detection using advanced sensor signal acquisition and fusion,” Traffic Injury Prevention, vol. 18, no. sup1, pp. S31–S36,

  9. [17]

    In-vehicle alcohol detection using low-cost sensors and genetic algorithms to aid in the drinking and driving detection,

    J. M. Celaya-Padilla, J. S. Romero-Gonz ´alez, C. E. Galvan-Tejada, J. I. Galvan-Tejada, H. Luna-Garc ´ıa, J. G. Arceo-Olague, N. K. Gamboa- Rosales, C. Sifuentes-Gallardo, A. Martinez-Torteya, J. I. De la Rosa, and H. Gamboa-Rosales, “In-vehicle alcohol detection using low-co...

    2021

  10. [18]

    Detectdui: An in-car detection system for drink driving and bacs,

    Y . Chen, M. Xue, J. Zhang, R. Ou, Q. Zhang, and P. Kuang, “Detectdui: An in-car detection system for drink driving and bacs,” IEEE/ACM Transactions on Networking , vol. 30, no. 2, pp. 896–910, 2022

    2022

  11. [19]

    P. R. Marques, The alcohol ignition interlock and other technologies for the prediction and control of impaired drivers , pp. 457–476. Basel: Birkh¨auser Basel, 2009

    2009

  12. [20]

    A literature survey of drunk driving detec- tion approaches,

    A. Kumar and A. Kumar, “A literature survey of drunk driving detec- tion approaches,” in Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing , IC3-2022, (New York, NY , USA), p. 342–349, Association for Computing Machinery, 2022

    2022

  13. [21]

    Interlocks for first offenders: Effective?,

    R. V . Richard Roth and P. Marques, “Interlocks for first offenders: Effective?,” Traffic Injury Prevention, vol. 8, no. 4, pp. 346–352, 2007. PMID: 17994487

    2007

  14. [22]

    Effectiveness of ignition interlocks for preventing alcohol-impaired driving and alcohol-related crashes: A community guide systematic review,

    R. W. Elder, R. V oas, D. Beirness, R. A. Shults, D. A. Sleet, J. L. Nichols, and R. Compton, “Effectiveness of ignition interlocks for preventing alcohol-impaired driving and alcohol-related crashes: A community guide systematic review,” American Journal of Preventive Medicin...

    2011

  15. [23]

    Using a passive alcohol sensor to detect legally intoxicated drivers.,

    R. D. Foss, R. B. V oas, and D. J. Beirness, “Using a passive alcohol sensor to detect legally intoxicated drivers.,” American Journal of Public Health, vol. 83, no. 4, pp. 556–560, 1993. PMID: 8460734

    1993

  16. [24]

    Madd grades each state on ignition inter- lock laws, pushes for lifesaving improvements,

    MomsAgainstDrunkDriving, “Madd grades each state on ignition inter- lock laws, pushes for lifesaving improvements,” Jul 2022

    2022

  17. [25]

    SAE J3016™: Levels of Driving Automation Standard

    Society of Automotive Engineers (SAE), “SAE J3016™: Levels of Driving Automation Standard.” https://www.sae.org/standards/content/ j3016 202104/, 2021. Accessed: 2024-12-11

    2021

  18. [273]

    New York, NY: Springer New York, 2013

    2013

  19. [2024]

    urlhttps://www.euroncap.com/en/press-media/press-releases/euro- ncap-vision-2030-a-safer-future-for-mobility/

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.