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arxiv: 2604.23513 · v1 · submitted 2026-04-26 · 💻 cs.RO

Large Language Model based Interactive Decision-Making for Autonomous Driving

Xinwei Dong , Jiyang Li , Jiabin Xie , Yang Yi , Tianshang Jia , Shiyu Fang , Ye Tian , Peng Hang This is my paper

Pith reviewed 2026-05-08 06:18 UTC · model grok-4.3

classification 💻 cs.RO
keywords autonomous drivinglarge language modelsinteractive decision makingmixed trafficintent reasoningobject-process methodologyhuman-machine interfacetrajectory optimization
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The pith

A framework pairs object-process scene modeling with large language models to let autonomous vehicles interpret surrounding drivers' intents, choose maneuvers, and explain decisions in natural language.

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

The paper aims to show that current autonomous driving systems are too cautious in mixed traffic with human drivers because they fail to grasp implicit intents and lack clear communication. By first converting raw sensor data into a structured model of objects, processes, and relations, then feeding that model to a large language model, the system can jointly reason about safety and efficiency, pick actions, and broadcast simple messages to nearby road users. A sympathetic reader would care because successful intent-aware interaction could reduce unnecessary braking, improve flow, and raise public trust in autonomous vehicles.

Core claim

The framework abstracts low-level perceptual data into objects, processes, and relations using Object-Process Methodology, enabling a large language model to parse explicit and implicit intents of surrounding agents. Under jointly enforced safety and efficiency constraints the model selects candidate maneuvers, refines them with Monte Carlo trajectory sampling, and converts the final decision into concise natural-language messages broadcast via an external human-machine interface.

What carries the argument

Object-Process Methodology scene abstraction that turns raw perception into structured objects, processes, and relations so a large language model can perform intent reasoning and produce communicative actions.

If this is right

  • The approach records higher safety, comfort, and efficiency scores than traditional baselines in cluster-simulator tests of dense mixed traffic.
  • Turing-test-style evaluations indicate decisions that feel highly human-like to observers.
  • The system closes a loop from semantic understanding to action to language output, enabling explicit coordination with nearby drivers.
  • Semantic abstraction plus language-model intent reasoning supplies a route to more interactive and trustworthy autonomous driving in crowded mixed environments.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the abstraction step generalizes beyond simulation, nearby human drivers could begin treating autonomous vehicles as predictable partners rather than unpredictable obstacles.
  • Broadcast natural-language messages might lower the rate of near-misses caused by mutual misunderstanding in unsignalized intersections.
  • The same modeling-plus-language pattern could be tested in other multi-agent settings such as warehouse robots or drone traffic where intent inference matters.

Load-bearing premise

The Object-Process Methodology turns raw sensor data into an accurate model of objects, processes, and relations that captures the hidden causes needed for reliable language-model reasoning about other drivers' intentions.

What would settle it

Real-world mixed-traffic trials in which the system produces no measurable gain in safety metrics or produces more conflicts than standard planning baselines would falsify the central performance claim.

Figures

Figures reproduced from arXiv: 2604.23513 by Jiabin Xie, Jiyang Li, Peng Hang, Shiyu Fang, Tianshang Jia, Xinwei Dong, Yang Yi, Ye Tian.

Figure 1
Figure 1. Figure 1: LLM-based Interactive Autonomous Driving Framework view at source ↗
Figure 3
Figure 3. Figure 3: Driving Simulator System VI. EXPERIMENTAL RESULTS AND ANALYSIS To verify the autonomous driving interactive decision￾making model in complex mixed driving environments, we conducted experiments using the Tongji University Cluster Driving Simulator (as shown in view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of three autonomous driving decision models under different initial speed conditions. (a) Average speed across models. (b) Average jerk view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of OPM with other methods. (a) Comparison of accuracy. (b) Comparison of latency. (c) Comparison of Reasoning steps. view at source ↗
Figure 6
Figure 6. Figure 6: Comparison of decision-making models for CAV at an initial velocity of 6 m/s.(a) Real-time vehicle positions under IDM. (b) Real-time vehicle view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of OPM with other methods in merging scenario. (a) Experimental scenario. (b) Average speed across models. view at source ↗
Figure 8
Figure 8. Figure 8: Turing Test Experimental Scheme view at source ↗
Figure 9
Figure 9. Figure 9: Turing Test Confusion Matrix VII. CONCLUSION This paper proposes an interactive autonomous driving decision-making method based on LLM, aiming to address the issues of conservative decision-making and insufficient interaction ability of autonomous vehicles in human-machine mixed driving environments. By introducing OPM semantic scene modeling, LLM intent parsing, Monte Carlo trajec￾tory optimization, and e… view at source ↗
read the original abstract

In high-conflict mixed-traffic scenarios involving human-driven and autonomous vehicles, most existing autonomous driving systems default to overly conservative behaviors, lack proactive interaction, and consequently suffer from limited public acceptance. To mitigate intent misunderstandings and decision failures, we present a Large Language Model based interactive decision-making framework that augments scene understanding and intent-aware interaction to jointly improve safety and efficiency. The approach uses Object-Process Methodology to semantically model complex multi-vehicle scenes, abstracting low-level perceptual data into objects, processes, and relations, thereby streamlining reasoning over latent causal structure. Building on this representation, the Large Language Model parses both explicit and implicit intents of surrounding agents and, under jointly enforced safety and efficiency constraints, selects candidate maneuvers. We further generate perturbed trajectory candidates via Monte Carlo sampling and evaluate them to obtain an optimized executable trajectory. To foster transparency and coordination with nearby road users, the final decision is translated by the Large Language Model into concise natural-language messages and broadcast through an external Human-Machine Interface, completing a closed loop from scene understanding to action to language. Experiments in a cluster driving simulator demonstrate that the proposed method outperforms traditional baselines across safety, comfort, and efficiency metrics, while a Turing-test-style evaluation indicates a high degree of human-likeness in decision making. Besides, these results suggest that coupling semantic scene abstraction with Large Language Model mediated intent reasoning and language-based eHMI communication offers a practical pathway toward interactive, trustworthy autonomous driving in dense mixed traffic.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript presents a Large Language Model (LLM) based interactive decision-making framework for autonomous driving in high-conflict mixed-traffic scenarios. It employs Object-Process Methodology (OPM) to semantically model scenes by abstracting perceptual data into objects, processes, and relations. The LLM parses intents of surrounding agents, selects maneuvers under jointly enforced safety and efficiency constraints, optimizes trajectories via Monte Carlo sampling of perturbed candidates, and translates the final decision into natural-language messages broadcast via external HMI. Simulator experiments claim outperformance over traditional baselines on safety, comfort, and efficiency metrics, plus high human-likeness via Turing-test-style evaluation.

Significance. If the reported simulator gains hold after proper validation of the core components, the integration of OPM-based semantic abstraction with LLM intent reasoning and language-based eHMI could offer a practical route to more proactive, transparent autonomous driving in dense mixed traffic, potentially addressing acceptance barriers. The closed-loop design from perception to action to communication is a coherent contribution, though its novelty relative to existing LLM+planning pipelines in robotics remains to be fully contextualized.

major comments (2)
  1. [Abstract / Scene modeling component] Abstract and framework description: The central claim that OPM converts low-level perceptual inputs into objects/processes/relations that faithfully encode latent causal structure for reliable LLM intent inference and constrained maneuver selection is load-bearing for the reported safety/efficiency gains and human-likeness. No ablation against alternatives (scene graphs, raw trajectories, learned embeddings), no quantitative causal-fidelity metric (e.g., intervention accuracy), and no failure-case analysis are supplied, so performance improvements cannot be attributed to this step versus other pipeline elements.
  2. [Experiments] Experiments section: The abstract states that the method 'outperforms traditional baselines across safety, comfort, and efficiency metrics' and shows 'high degree of human-likeness,' yet supplies no numerical values, baseline definitions, statistical significance, or simulator configuration details. Without these, the quantitative superiority and Turing-test results cannot be assessed or reproduced.
minor comments (1)
  1. [Abstract] The sentence 'Besides, these results suggest...' in the abstract is slightly awkward; rephrase to 'These results further suggest...' for smoother academic style.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important areas where additional evidence and clarity will strengthen the manuscript. We address each major comment point by point below and outline the revisions we will make.

read point-by-point responses

  1. Referee: [Abstract / Scene modeling component] Abstract and framework description: The central claim that OPM converts low-level perceptual inputs into objects/processes/relations that faithfully encode latent causal structure for reliable LLM intent inference and constrained maneuver selection is load-bearing for the reported safety/efficiency gains and human-likeness. No ablation against alternatives (scene graphs, raw trajectories, learned embeddings), no quantitative causal-fidelity metric (e.g., intervention accuracy), and no failure-case analysis are supplied, so performance improvements cannot be attributed to this step versus other pipeline elements.

    Authors: We agree that stronger validation of the OPM scene-modeling step is needed to attribute performance gains specifically to this component. In the revised manuscript we will add an ablation study comparing OPM against scene-graph, raw-trajectory, and learned-embedding alternatives on the same simulator scenarios. We will also introduce a quantitative causal-fidelity metric based on intervention accuracy (measuring how often perturbing the OPM representation changes the LLM-selected maneuver) and include a dedicated failure-case analysis subsection that discusses representative scenarios in which the abstraction leads to incorrect intent inference or overly conservative maneuvers. These additions will make the contribution of the OPM step explicit. revision: yes

  2. Referee: [Experiments] Experiments section: The abstract states that the method 'outperforms traditional baselines across safety, comfort, and efficiency metrics' and shows 'high degree of human-likeness,' yet supplies no numerical values, baseline definitions, statistical significance, or simulator configuration details. Without these, the quantitative superiority and Turing-test results cannot be assessed or reproduced.

    Authors: We acknowledge that the current Experiments section does not provide sufficient numerical detail, baseline specifications, statistical tests, or simulator parameters for independent assessment and reproduction. In the revised manuscript we will expand this section to report concrete metric values (e.g., collision rates, comfort scores, efficiency measures), explicit definitions and implementations of all baselines, results of statistical significance tests, full simulator configuration (platform, scenario parameters, traffic densities), and a more detailed description of the Turing-test protocol together with its quantitative outcomes. A summary table of key results will also be added for clarity. revision: yes

Circularity Check

0 steps flagged

No circularity: descriptive pipeline with no derivations or self-referential reductions

full rationale

The manuscript describes a modular framework (OPM scene abstraction → LLM intent parsing → constrained maneuver selection → Monte Carlo trajectory optimization → eHMI language output) without any equations, fitted parameters, or derivation chains. No step reduces to its own inputs by construction, no self-citations are load-bearing for a uniqueness claim, and no ansatz or renaming is presented as a mathematical result. Experimental claims rest on simulator comparisons and Turing-style tests rather than internal consistency arguments, satisfying the self-contained criterion.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The claim depends on the unverified effectiveness of the semantic abstraction and LLM intent parsing in real scenarios, with no independent evidence provided in the abstract for these components.

axioms (1)
  • domain assumption Semantic modeling via Object-Process Methodology captures latent causal structure in multi-vehicle scenes
    Central to enabling LLM reasoning over intents.
invented entities (1)
  • LLM-based interactive decision-making framework with eHMI no independent evidence
    purpose: To augment scene understanding and enable intent-aware interaction in AVs
    The paper introduces this as a new integrated system.

pith-pipeline@v0.9.0 · 5579 in / 1381 out tokens · 59371 ms · 2026-05-08T06:18:43.614158+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

39 extracted references · 1 canonical work pages

  1. [1]

    Beijing Innovation Center for Intelligent Vehicles and Mobility Industry,

  2. [2]

    (in Chinese)

    Road Test Report of Autonomous Vehicles in Beijing, 2022. (in Chinese)

    2022

  3. [3]

    MIT Press, Cambridge, MA

    Ben-Akiva, M., Lerman, S.R., 1985.Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press, Cambridge, MA

    1985

  4. [4]

    Brill, S., Payre, W., Debnath, A. ,2023. External Human-Machine Interfaces for Automated Vehicles in Shared Spaces: A Review of the Human-Computer Interaction Literature. Sensors 23(9), 4454

    2023

  5. [5]

    Receive, Reason, and React: Drive as You Say, With Large Language Models in Autonomous Vehicles

    Cui, C., Ma, Y ., 2024. Receive, Reason, and React: Drive as You Say, With Large Language Models in Autonomous Vehicles. IEEE Intelligent Transportation Systems Magazine 16(4), 81-94

    2024

  6. [6]

    Light-Based External Human Machine Interface: Color Evaluation for Self-Driving Vehicle and Pedestrian Interaction

    Faas, S.M., Baumann, M., 2019. Light-Based External Human Machine Interface: Color Evaluation for Self-Driving Vehicle and Pedestrian Interaction. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, V ol. 63, No. 1, pp. 1232-1236

    2019

  7. [7]

    External HMI for Automated Vehicles: Adding a Communication Perspective for all Road Users

    Gao, R., Martens, M., 2022. External HMI for Automated Vehicles: Adding a Communication Perspective for all Road Users. In: Pro- ceedings of the Applied Human Factors and Ergonomics International Conference (AHFE), New York, USA

    2022

  8. [8]

    Decision making for connected automated vehicles at urban intersections considering social and individual benefits

    Hang, P., Huang, C., Hu, Z., Xing, Y ., Lv, C., 2021. Decision making for connected automated vehicles at urban intersections considering social and individual benefits. IEEE Transactions on Intelligent Transportation Systems 23(8), 10979-10991

    2021

  9. [9]

    Literature review of driving risk identification research based on bibliometric analysis

    Ge, H., Bo, Y ., Zang, W., Zhou, L., Dong, L., 2023. Literature review of driving risk identification research based on bibliometric analysis. Journal of Traffic and Transportation Engineering (English Edition) 10(4), 560-577

    2023

  10. [10]

    A survey of decision- making and planning methods for self-driving vehicles

    Hu, J., Wang, M., Zhao, P., Su, P., Lv, C., 2025. A survey of decision- making and planning methods for self-driving vehicles. Frontiers in Neurorobotics 19, 1451923

    2025

  11. [11]

    Hierarchical and game-theoretic decision-making for connected and automated vehicles in overtaking scenarios

    Ji, K., Li, N., Orsag, M., Han, K., 2023. Hierarchical and game-theoretic decision-making for connected and automated vehicles in overtaking scenarios. Transportation Research Part C: Emerging Technologies 150, 104109

    2023

  12. [12]

    General lane-changing model MOBIL for car-following models.Transportation Research Record1999(1), 86–94

    Kesting, A., Treiber, M., Helbing, D., 2007. General lane-changing model MOBIL for car-following models.Transportation Research Record1999(1), 86–94

    2007

  13. [13]

    Human-Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

    Lee, H.G., Kang, D.H., Kim, D.H., 2021. Human-Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles. Electronics 10(19), 2405

    2021

  14. [14]

    Cooperative decision-making for cavs at unsignalized intersections: A marl approach with attention and hierarchical game priors

    Liu, J., Hang, P., Na, X., Chen, S., 2025. Cooperative decision-making for cavs at unsignalized intersections: A marl approach with attention and hierarchical game priors. IEEE Transactions on Intelligent Trans- portation Systems 26(1), 443-456

    2025

  15. [15]

    A three-level game-theoretic decision-making framework for autonomous vehicles

    Liu, M., Wan, Y ., Lewis, F.L., 2022. A three-level game-theoretic decision-making framework for autonomous vehicles. IEEE Transactions on Intelligent Transportation Systems 23(11), 20298-20308

    2022

  16. [16]

    A Game- Theoretic Approach on Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections

    Lu, X., Zhao, H., Li, C.H., Gao, B., Chen, H., 2023. A Game- Theoretic Approach on Conflict Resolution of Autonomous Vehicles at Unsignalized Intersections. IEEE Transactions on Intelligent Trans- portation Systems 24(11), 12106-12120

    2023

  17. [17]

    Conditional logit analysis of qualitative choice behavior

    McFadden, D., 1974. Conditional logit analysis of qualitative choice behavior. In: Zarembka, P. (Ed.),Frontiers in Econometrics. Academic Press, New York, pp. 105–142

    1974

  18. [18]

    Modelling behavioural interactions of drivers in mixed traffic conditions

    Munigety, C.R., 2018. Modelling behavioural interactions of drivers in mixed traffic conditions. Journal of Traffic and Transportation Engineer- ing (English Edition) 5(4), 284-295

    2018

  19. [19]

    Analysis of Autonomous Vehicles' Interaction Strategy in Unprotected Turn Scenarios

    Ni, Y ., Qi, X., Hang, P., et al., 2023. Analysis of Autonomous Vehicles' Interaction Strategy in Unprotected Turn Scenarios. China Journal of Highway and Transport 36(9), 271-287. (in Chinese)

    2023

  20. [20]

    Systematic literature review on the applications, impacts, and public perceptions of autonomous vehicles in road transportation system

    Olayode, I.O., Du, B., Severino, A., Campisi, T., Alex, F.J., 2023. Systematic literature review on the applications, impacts, and public perceptions of autonomous vehicles in road transportation system. Jour- nal of Traffic and Transportation Engineering (English Edition) 10(6), 1037-1060

    2023

  21. [21]

    A review for autonomous vehicles technologies

    Pagale, M., Sharma, R., Thakare, A., 2024. A review for autonomous vehicles technologies. International Journal of Intelligent Systems and Applications in Engineering 12(4), 675-687

    2024

  22. [22]

    End-to-end autonomous driving through dueling double deep Q-network

    Peng, B., Sun, Q., Li, S.E., Kum, D., Yin, Y ., Wei, J., Gu, T., 2023. End-to-end autonomous driving through dueling double deep Q-network. Automotive Innovation 6(2), 328-337

    2023

  23. [23]

    Helping Au- tomated Vehicles With Left-Turn Maneuvers: A Game Theory-Based Decision Framework for Conflicting Maneuvers at Intersections

    Rahmati, Y ., Khajeh Hosseini, M., Talebpour, A., 2022. Helping Au- tomated Vehicles With Left-Turn Maneuvers: A Game Theory-Based Decision Framework for Conflicting Maneuvers at Intersections. IEEE Transactions on Intelligent Transportation Systems 23(8), 11877-11890

    2022

  24. [24]

    Social behavior for autonomous vehicles

    Schwarting, W., Pierson, A., Alonso-Mora, J., 2019. Social behavior for autonomous vehicles. Proceedings of the National Academy of Sciences 116(50), 24972-24978

    2019

  25. [25]

    A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

    Smirnov, N., Liu, Y ., Validi, A., Morales-Alvarez, W., 2021. A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios. Sensors 21(4), 1523

    2021

  26. [26]

    Development and Application of a Public Service Platform for Autonomous Driving Testing

    Sun, J., Huang, Y ., Tian, Y ., et al., 2024. Development and Application of a Public Service Platform for Autonomous Driving Testing. China Journal of Highway and Transport 37(8), 248-258. (in Chinese)

    2024

  27. [27]

    Game theoretic modeling of vehicle interactions at unsignalized intersections and application to autonomous vehicle control

    Tian, R., Li, L., Yang, K., Chien, S., Chen, Y ., Sherony, R., 2018. Game theoretic modeling of vehicle interactions at unsignalized intersections and application to autonomous vehicle control. In: 2018 Annual Amer- ican Control Conference (ACC), Milwaukee, WI, USA, pp. 3215-3220

    2018

  28. [28]

    Tian, R., Li, S., Li, N., Kolmanovsky, I., Girard, A., Yildiz, Y .,

  29. [29]

    Transportation Research Part C: Emerging Technologies 159, 104476

    Adaptive game-theoretic decision making for autonomous vehicle control at roundabouts. Transportation Research Part C: Emerging Technologies 159, 104476

  30. [30]

    Integrated driving behavior modeling.Transportation Research Part C: Emerging Tech- nologies15(2), 96–112

    Toledo, T., Koutsopoulos, H.N., Ben-Akiva, M., 2007. Integrated driving behavior modeling.Transportation Research Part C: Emerging Tech- nologies15(2), 96–112

    2007

  31. [31]

    Competitive and coop- erative behaviour analysis of connected and autonomous vehicles across unsignalised intersections: A game-theoretic approach

    Wang, H., Meng, Q., Chen, S., Xiong, J., 2021. Competitive and coop- erative behaviour analysis of connected and autonomous vehicles across unsignalised intersections: A game-theoretic approach. Transportation Research Part B: Methodological 149, 322-346

    2021

  32. [32]

    A comprehensive survey on cooperative perception in autonomous driving: A survey and a taxonomy of methods

    Wang, J., Liu, W., Li, L., Ma, C., Li, Q., 2023. A comprehensive survey on cooperative perception in autonomous driving: A survey and a taxonomy of methods. IEEE Transactions on Vehicular Technology 72(11), 14234-14252

    2023

  33. [33]

    Cooperative driving at unsignal- ized intersections using tree search

    Xu, H., Zhang, Y ., Li, L., Li, W., 2019. Cooperative driving at unsignal- ized intersections using tree search. IEEE Transactions on Intelligent Transportation Systems 21(11), 4563-4571

    2019

  34. [34]

    DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model

    Xu, Z., Zhang, Y ., Xie, E., Zhao, Z., Guo, Y ., Wong, K.K., Li, Z., Zhao, H., 2024. DriveGPT4: Interpretable End-to-End Autonomous Driving Via Large Language Model. IEEE Robotics and Automation Letters 9(10), 8186-8193

    2024

  35. [35]

    Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles

    Yag ¨ue-Cuevas, D., Mar ´ın-Plaza, P., Lorente, M.P.S., Fuentes-Hurtado, F., Barea, R., Naranjo, J.E., 2025. Mitigating Urban Congestion: A Cooperative Reservation Framework for Automated Vehicles. Applied Sciences 15(10), 5347

    2025

  36. [36]

    Llm4drive: A survey of large language models for autonomous driving.ArXiv, abs/2311.01043, 2023

    Yang, Z., Chai, Y ., Anguelov, D., Zhou, Y ., Sun, P., Kretzschmar, H., 2024. LLM4Drive: A Survey of Large Language Models for Au- tonomous Driving. arXiv preprint arXiv:2311.01043

    work page arXiv 2024

  37. [37]

    Application Research of Interaction Design in Human- Machine Interface of Automobile

    Zhang, C., 2020. Application Research of Interaction Design in Human- Machine Interface of Automobile. In: Proceedings of Emerging Trends in Intelligent and Interactive Systems and Applications (IISA), pp. 123- 130

    2020

  38. [38]

    Human-Machine Interaction for Au- tonomous Vehicles: A Review

    Zhang, J., Shu, Y ., Yu, H., 2021. Human-Machine Interaction for Au- tonomous Vehicles: A Review. In: International Conference on Human- Computer Interaction (HCII), Virtual Event, pp. 145-159

    2021

  39. [39]

    Human-like decision-making of autonomous vehicles in dynamic traffic scenarios

    Zhang, T., Zhan, J., Shi, J., Xin, J., Zheng, N., 2023. Human-like decision-making of autonomous vehicles in dynamic traffic scenarios. IEEE/CAA Journal of Automatica Sinica 10(10), 1905- 1917

    2023