Pith. sign in

REVIEW 2 major objections 2 minor 63 references

PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment

T0 review · 2 major / 2 minor · reviewed 2026-05-20 · grok-4.3

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

Pith's one-line read A framework uses large language models to generate promptable safety-critical urban traffic scenarios and pairs them with reinforcement learning to train vehicle behaviors for closed-loop testing of autonomous driving systems.

desk verdict The paper puts together an LLM-driven scenario generator with RL vehicle behaviors in a closed loop for AV testing, but the claimed gains rest on unverified realism and missing baselines. read the letter →

arxiv 2605.15654 v1 pith:U2JNGLP4 submitted 2026-05-15 cs.RO

Chuancheng Zhang , Zhenhao Wang , Kaizheng Li , Yaran Lin , Qiang Guo , Bin Jiang This is my paper · ORCID
classification cs.RO
keywords adversarialsimulationurbantrafficlargelanguagemodelsreinforcementlearningautonomousdrivingscenariogenerationclosed-looptestingsafety-criticalscenarios
checked against Cost.FunctionalEquation
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 aims to show that integrating knowledge from datasets with large language model generation and reinforcement learning training can create more diverse and realistic adversarial scenarios for testing self-driving cars in cities. A sympathetic reader would care because real-world autonomous vehicles need to handle rare dangerous situations that standard tests miss, and this approach allows custom scenarios based on prompts while keeping them believable. The method builds a knowledge repository from real data, uses the LLM to combine different generation strategies, and trains vehicle agents with RL to make the simulations more challenging and varied. If successful, it would mean better co-evolution of scenario generators and safety agents, leading to more robust testing without needing massive new real-world data collections.

What carries the argument

The promptable closed-loop adversarial simulation that combines LLM-based scenario generation with RL-trained vehicle behaviors to enable mutual enhancement in urban traffic testing.

What would settle it

A direct comparison of the generated scenarios against a large set of real-world urban traffic recordings to measure if the frequency and types of safety-critical events match real distributions, or if RL behaviors create implausible vehicle interactions.

Watch

Extended reading notes

Core claim

The authors propose PCASim, which constructs an adversarial behavior knowledge repository from an open-source dataset using rule-based filtering and retrieval modules. It employs a large language model to generate user-customized safety-critical traffic scenarios by merging knowledge-driven, data-driven, and adversarial-driven methods. Reinforcement learning is used to train different vehicle types' behaviors, enriching scenario diversity while preserving realism. Experiments show the framework improves domain-specific language generation accuracy by 12%, scenario transformation success rate by 8%, and obstacle-avoidance capability by 30%.

Load-bearing premise

The assumption that combining LLM-generated scenarios with RL-trained behaviors produces realistic and safety-critical situations without introducing unrealistic artifacts not found in actual urban traffic.

Editorial extensions

If this is right

  • If the framework works, testing of autonomous vehicles can incorporate user-specified prompts to create targeted safety-critical scenarios.
  • The closed-loop setup allows scenario generators and safety agents to improve each other over iterations.
  • Urban traffic simulations can achieve higher diversity without losing contact with real data patterns.
  • Obstacle avoidance in trained agents improves substantially through this enriched environment.
  • Domain-specific language for describing scenarios becomes more accurate with the integrated approach.

Reading between the lines

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

  • This could allow simulation platforms to adapt scenarios on the fly based on new edge cases discovered during testing.
  • Connecting this to real vehicle logs might further reduce the sim-to-real gap in safety evaluations.
  • Extending the RL training to include multi-agent interactions could model more complex traffic flows.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper proposes PCASim, a framework integrating rule-based filtering of open-source datasets, knowledge retrieval, LLM-driven generation of user-customized safety-critical traffic scenarios, and RL-trained vehicle behaviors for closed-loop adversarial simulation in urban environments. It claims this enables co-evolution of scenario generation and safety agent training, with experimental results showing a 12% improvement in domain-specific language generation accuracy, an 8% increase in newly generated scenario transformation success rate, and a 30% enhancement in obstacle-avoidance capability.

Significance. If the central claims hold after addressing validation gaps, the work could advance autonomous driving testing by providing a promptable, closed-loop approach that combines LLM flexibility with RL-enriched behaviors, potentially improving robustness to corner cases beyond static datasets. The emphasis on mutual enhancement between generation and training is a notable direction, though its impact depends on demonstrated fidelity to real traffic distributions.

major comments (2)
  1. [Abstract and Experimental Results] Abstract and Experimental Results section: The headline gains (12% DSL accuracy, 8% scenario success, 30% obstacle-avoidance) are stated as percentage improvements without any reported baselines, statistical tests, dataset sizes, number of trials, or error bars. This directly affects interpretability of whether the closed-loop RL augmentation drives genuine gains or artifacts.
  2. [RL Integration and Scenario Evaluation] Section on RL-augmented scenario generation (near the description of vehicle behavior training): The assertion that RL-trained behaviors 'enrich scenario diversity beyond existing datasets while preserving realism' lacks any supporting quantitative evidence, such as distributional comparisons (e.g., velocity histograms or time-to-collision statistics) against real urban datasets, expert fidelity ratings, or an ablation isolating RL's effect on scenario validity. This is load-bearing for the obstacle-avoidance claim, as non-physical behaviors could inflate the reported 30% improvement.
minor comments (2)
  1. [Abstract] The abstract ends with a project-page URL rather than a standard reference or DOI; this should be removed or replaced with a proper citation format for the manuscript.
  2. [Framework Overview] Notation for the adversarial behavior knowledge repository and knowledge retrieval modules is introduced without a clear diagram or pseudocode, making the pipeline flow harder to follow on first reading.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments highlight important aspects of experimental reporting and validation that we will address to improve clarity and rigor. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract and Experimental Results] Abstract and Experimental Results section: The headline gains (12% DSL accuracy, 8% scenario success, 30% obstacle-avoidance) are stated as percentage improvements without any reported baselines, statistical tests, dataset sizes, number of trials, or error bars. This directly affects interpretability of whether the closed-loop RL augmentation drives genuine gains or artifacts.

    Authors: We agree that the abstract and experimental results would benefit from additional details to support interpretability. The reported improvements are relative to internal baselines (non-LLM and non-RL variants of the framework), but these were not explicitly described in the initial submission. In the revised manuscript, we will expand the experimental section to specify the exact baselines, dataset sizes (e.g., number of scenarios sampled from the open-source urban dataset), number of trials (minimum 50 independent runs per condition), error bars or standard deviations, and statistical tests (e.g., t-tests for significance). This will clarify the contribution of the closed-loop RL component. revision: yes

  2. Referee: [RL Integration and Scenario Evaluation] Section on RL-augmented scenario generation (near the description of vehicle behavior training): The assertion that RL-trained behaviors 'enrich scenario diversity beyond existing datasets while preserving realism' lacks any supporting quantitative evidence, such as distributional comparisons (e.g., velocity histograms or time-to-collision statistics) against real urban datasets, expert fidelity ratings, or an ablation isolating RL's effect on scenario validity. This is load-bearing for the obstacle-avoidance claim, as non-physical behaviors could inflate the reported 30% improvement.

    Authors: We acknowledge that the current text relies on the downstream performance metrics to imply the value of RL augmentation without direct quantitative validation of diversity and realism. This is a valid concern for the obstacle-avoidance results. We will add an ablation study and supporting analyses in the revised version, including velocity and time-to-collision distribution comparisons against the source real-world urban dataset, plus metrics quantifying scenario diversity (e.g., entropy or coverage of edge cases). This will isolate the RL contribution and better ground the 30% improvement. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity in derivation chain

full rationale

The paper describes an empirical framework combining rule-based filtering, LLM-based scenario generation, and RL-trained vehicle behaviors for closed-loop adversarial simulation. All central claims consist of measured performance deltas (12% DSL accuracy, 8% transformation success, 30% obstacle avoidance) obtained from experiments. No equations, fitted parameters, self-definitional constructs, or load-bearing self-citations appear in the abstract or description; the results are presented as direct experimental outcomes rather than quantities derived from the paper's own inputs by construction. The derivation chain is therefore self-contained and does not reduce to tautology.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

The framework rests on standard domain assumptions in simulation and machine learning without introducing new physical entities or explicitly fitted free parameters in the abstract.

assumptions (2)
  • domain assumption Rule-based filtering of open-source datasets yields a representative adversarial behavior knowledge repository suitable for simulation environments.
    Invoked when constructing the repository to support LLM-driven scenario generation.
  • domain assumption Reinforcement learning training of vehicle behaviors enriches scenario diversity while preserving realism.
    Stated as the mechanism for evaluating and improving generated scenarios.

how reviews work

0 comments
Cite this review

Pith. "Pith review of PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment." pith.science (2026). https://pith.science/paper/U2JNGLP4

@misc{pith2026260515654,
  author       = {Pith},
  title        = {Pith review of: PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U2JNGLP4}},
  note         = {Machine review of arXiv:2605.15654}
}
read the original abstract

Real-world autonomous driving, particularly in urban environments with numerous corner cases, requires rigorous testing to ensure product safety and robustness. However, few studies have explored integrating adversarial scenario generation with the training of safety agents in closed-loop testing, enabling efficient co-evolution and mutual enhancement of both. To address this challenge, an adversarial behavior knowledge repository is constructed by applying rule-based filtering to an open-source dataset, combined with knowledge retrieval modules tailored for simulation environments. A large language model (LLM) is employed to integrate knowledge-, data-, and adversarial-driven approaches, generating safety-critical traffic scenarios customized to user needs. Additionally, while evaluating the generated scenarios, we employ reinforcement learning models to train the behaviors of different types of vehicles, thereby enriching scenario diversity beyond existing datasets while preserving realism. Experimental results demonstrate that the proposed framework improves the accuracy of domain-specific language generation by 12\%. Moreover, the success rate of newly generated scenario transformations increases by 8\%, while obstacle-avoidance capability is enhanced by 30\%. For the complete manuscript, please refer to: https://zhenhaooo.github.io/PCASim.github.io/

Figures

Figures reproduced from arXiv: 2605.15654 by the authors.

Figure 1
Figure 1. The overall framework of the closed-loop adversarial simulation [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. The framework of newly generated adversarial scenarios. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Comparison of DSL scenario generation scores across different LLMs and ablation study of core components in the DSL generation pipeline. ego and adversarial vehicles are retained as closed-loop simulation outputs, thus expanding the repository’s coverage of adversarial intentions and response patterns. Our realism evaluation aligns with the metrics used in the Waymo Sim Agent Challenge, emphasizing kinematic feasibi… view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: and Table I with respect to adversarial metrics such as TTC, PET, and collision rate (CR) clearly demonstrate that our method achieves superior distributions. Notably, PCASim achieves an increase of about 8% in collision rate over baseline method, indicating its enhanc…
Figure 5
Figure 5. Figure 5: Comparison of the greatest heading trajectories [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 6
Figure 6. Figure 6: Generating Scenarios and Validation. (a) (b) are the braking scenarios, (c) (d) are the going straight scenarios, the orange indicates adversarial vehicle, the red indicates ego vehicle without training and the green ego vehicle has been strengthened by the reinforceme…
Figure 7
Figure 7. Figure 7: Framework of retrieval-augmented prompt construction, Chain-of-Thought reasoning, syntax alignment checking, and self-consistency voting for [PITH_FULL_IMAGE:figures/full_fig_p010_7.png]
Figure 8
Figure 8. Figure 8: Workflow of DSL-to-Python code generation: retrieval of relevant code fragments, prompt assembly, LLM-based code generation, and iterative [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

63 extracted references · 63 canonical work pages

  1. [1]

    Waymo’s incidents: As of the end of 2024, the national highway traffic safety administration (nhtsa) had received 835 reports documenting 696 incidents involving waymo vehicles,

    Waymo, “Waymo’s incidents: As of the end of 2024, the national highway traffic safety administration (nhtsa) had received 835 reports documenting 696 incidents involving waymo vehicles,” 2024, accessed: 2025-04-14. [Online]. Available: https://en.wikipedia.org/wiki/Waymo

    work page 2024

  2. [2]

    As of 2024, there have been 51 reported fatalities involving tesla’s autopilot function, with 44 verified by nhtsa or expert testimony

    Tesla, “As of 2024, there have been 51 reported fatalities involving tesla’s autopilot function, with 44 verified by nhtsa or expert testimony.” 2024, accessed: 2025-04-14. [Online]. Available: https://en.wikipedia.org/wiki/Tesla_Autopilot

    work page 2024

  3. [3]

    Performance limit evaluation strategy for automated driving systems,

    F. Gao, J. Mu, X. Han, Y . Yang, and J. Zhou, “Performance limit evaluation strategy for automated driving systems,”Automotive Innovation, vol. 5, no. 1, pp. 79–90, 2022

    work page 2022

  4. [4]

    Risk and complexity assessment of autonomous vehicle testing scenarios,

    Z. Wei, H. Zhou, and R. Zhou, “Risk and complexity assessment of autonomous vehicle testing scenarios,”Applied Sciences, vol. 14, no. 21, p. 9866, 2024

    work page 2024

  5. [5]

    A survey on datasets for the decision making of autonomous vehicles,

    Y . Wang, Z. Han, Y . Xing, S. Xu, and J. Wang, “A survey on datasets for the decision making of autonomous vehicles,”IEEE Intelligent Transportation Systems Magazine, vol. 16, no. 2, pp. 23–40, 2024

    work page 2024

  6. [6]

    A survey on safety-critical driving scenario generation—a methodological perspec- tive,

    W. Ding, C. Xu, M. Arief, H. Lin, B. Li, and D. Zhao, “A survey on safety-critical driving scenario generation—a methodological perspec- tive,”IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 7, pp. 6971–6988, 2023

    work page 2023

  7. [7]

    Generative modeling of adversarial lane-change scenario,

    C. Zhang, Z. Wang, J. Wang, K. Su, Q. Lv, B. Jiang, K. Hao, and W. Wang, “Generative modeling of adversarial lane-change scenario,” arXiv preprint arXiv:2503.12055, 2025

    work page arXiv 2025

  8. [8]

    Kinetic analysis and numerical tests of an adaptive car-following model for real-time traffic in its,

    Y .-H. Yin, X. Lü, R. Jiang, B. Jia, and Z. Gao, “Kinetic analysis and numerical tests of an adaptive car-following model for real-time traffic in its,”Physica A: Statistical Mechanics and its Applications, vol. 635, p. 129494, 2024

    work page 2024

Show all 63 references
  1. [9]

    Advsim: Generating safety-critical scenarios for self- driving vehicles,

    J. Wang, A. Pun, J. Tu, S. Manivasagam, A. Sadat, S. Casas, M. Ren, and R. Urtasun, “Advsim: Generating safety-critical scenarios for self- driving vehicles,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 9909–9918

    2021

  2. [10]

    Trafficgen: Learning to generate diverse and realistic traffic scenarios,

    L. Feng, Q. Li, Z. Peng, S. Tan, and B. Zhou, “Trafficgen: Learning to generate diverse and realistic traffic scenarios,” in2023 IEEE international conference on robotics and automation (ICRA). IEEE, 2023, pp. 3567–3575

    2023

  3. [11]

    Llama: Open and efficient foundation language models,

    H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azharet al., “Llama: Open and efficient foundation language models,”arXiv preprint arXiv:2302.13971, 2023

    2023 arXiv

  4. [12]

    Interaction dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps,

    W. Zhan, L. Sun, D. Wang, H. Shi, A. Clausse, M. Naumann, J. Kummerle, H. Konigshof, C. Stiller, A. de La Fortelleet al., “Interaction dataset: An international, adversarial and cooperative motion dataset in interactive driving scenarios with semantic maps,” arXiv preprint arX...

    1910

  5. [13]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,

    D. Guo, D. Yang, H. Zhang, J. Song, R. Zhang, R. Xu, Q. Zhu, S. Ma, P. Wang, X. Biet al., “Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning,”arXiv preprint arXiv:2501.12948, 2025

    2025 arXiv

  6. [14]

    Deepseek-v3 technical report,

    A. Liu, B. Feng, B. Xue, B. Wang, B. Wu, C. Lu, C. Zhao, C. Deng, C. Zhang, C. Ruanet al., “Deepseek-v3 technical report,”arXiv preprint arXiv:2412.19437, 2024

    2024 arXiv

  7. [15]

    Qwen2. 5 technical report,

    A. Yang, B. Yang, B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Weiet al., “Qwen2. 5 technical report,”arXiv preprint arXiv:2412.15115, 2024

    2024 arXiv

  8. [16]

    Language conditioned traffic generation,

    S. Tan, B. Ivanovic, X. Weng, M. Pavone, and P. Kraehenbuehl, “Language conditioned traffic generation,” in7th Annual Conference on Robot Learning, 2023

    2023

  9. [17]

    Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain,

    J. M. Scanlon, K. D. Kusano, T. Daniel, C. Alderson, A. Ogle, and T. Victor, “Waymo simulated driving behavior in reconstructed fatal crashes within an autonomous vehicle operating domain,”Accident Analysis & Prevention, vol. 163, p. 106454, 2021

    2021

  10. [18]

    Surfelgan: Synthesizing realistic sensor data for autonomous driving,

    Z. Yang, Y . Chai, D. Anguelov, Y . Zhou, P. Sun, D. Erhan, S. Rafferty, and H. Kretzschmar, “Surfelgan: Synthesizing realistic sensor data for autonomous driving,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 11 118–11 127

    2020

  11. [19]

    Interactive critical scenario generation for autonomous vehicles testing based on in-depth crash data using reinforcement learning,

    Z. Wei, H. Huang, G. Zhang, R. Zhou, X. Luo, S. Li, and H. Zhou, “Interactive critical scenario generation for autonomous vehicles testing based on in-depth crash data using reinforcement learning,”IEEE Transactions on Intelligent Vehicles, 2024

    2024

  12. [20]

    Adaptive stress testing of airborne collision avoidance systems,

    R. Lee, M. J. Kochenderfer, O. J. Mengshoel, G. P. Brat, and M. P. Owen, “Adaptive stress testing of airborne collision avoidance systems,” in2015 IEEE/AIAA 34th Digital Avionics Systems Conference (DASC). IEEE, 2015, pp. 6C2–1

    2015

  13. [21]

    Generating adversarial driving scenarios in high-fidelity simulators,

    Y . Abeysirigoonawardena, F. Shkurti, and G. Dudek, “Generating adversarial driving scenarios in high-fidelity simulators,” in2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 8271–8277

    2019

  14. [22]

    Structured domain randomization: Bridging the reality gap by context-aware synthetic data,

    A. Prakash, S. Boochoon, M. Brophy, D. Acuna, E. Cameracci, G. State, O. Shapira, and S. Birchfield, “Structured domain randomization: Bridging the reality gap by context-aware synthetic data,” in2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019, pp. ...

    2019

  15. [23]

    Adversarial safety-critical scenario generation using naturalistic human driving priors,

    K. Hao, W. Cui, Y . Luo, L. Xie, Y . Bai, J. Yang, S. Yan, Y . Pan, and Z. Yang, “Adversarial safety-critical scenario generation using naturalistic human driving priors,”IEEE Transactions on Intelligent Vehicles, 2023

    2023

  16. [24]

    King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients,

    N. Hanselmann, K. Renz, K. Chitta, A. Bhattacharyya, and A. Geiger, “King: Generating safety-critical driving scenarios for robust imitation via kinematics gradients,” inEuropean Conference on Computer Vision. Springer, 2022, pp. 335–352

    2022

  17. [25]

    Llm-attacker: Enhancing closed- loop adversarial scenario generation for autonomous driving with large language models,

    Y . Mei, T. Nie, J. Sun, and Y . Tian, “Llm-attacker: Enhancing closed- loop adversarial scenario generation for autonomous driving with large language models,”arXiv preprint arXiv:2501.15850, 2025

    2025

  18. [26]

    Frea: Feasibility-guided generation of safety-critical scenarios with reason- able adversariality,

    K. Chen, Y . Lei, H. Cheng, H. Wu, W. Sun, and S. Zheng, “Frea: Feasibility-guided generation of safety-critical scenarios with reason- able adversariality,”arXiv preprint arXiv:2406.02983, 2024

    2024

  19. [27]

    Ontology based scene creation for the development of automated vehicles,

    G. Bagschik, T. Menzel, and M. Maurer, “Ontology based scene creation for the development of automated vehicles,” in2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2018, pp. 1813–1820

    2018

  20. [28]

    Summit: A simulator for urban driving in massive mixed traffic,

    P. Cai, Y . Lee, Y . Luo, and D. Hsu, “Summit: A simulator for urban driving in massive mixed traffic,” in2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020, pp. 4023–4029

    2020

  21. [29]

    Scenario factory: Creating safety-critical traffic scenarios for automated vehicles,

    M. Klischat, E. I. Liu, F. Holtke, and M. Althoff, “Scenario factory: Creating safety-critical traffic scenarios for automated vehicles,” in 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2020, pp. 1–7

    2020

  22. [30]

    Target: Automated scenario generation from traffic rules for testing autonomous vehicles,

    Y . Deng, J. Yao, Z. Tu, X. Zheng, M. Zhang, and T. Zhang, “Target: Automated scenario generation from traffic rules for testing autonomous vehicles,”arXiv preprint arXiv:2305.06018, 2023

    2023

  23. [31]

    An llm-enhanced multi-objective evolutionary search for autonomous driving test scenario generation,

    H. Tian, X. Han, G. Wu, Y . Zhou, S. Li, J. Wei, D. Ye, W. Wang, and T. Zhang, “An llm-enhanced multi-objective evolutionary search for autonomous driving test scenario generation,”arXiv preprint arXiv:2406.10857, 2024

    2024

  24. [32]

    Optimizing autonomous driving for safety: A human-centric approach with llm-enhanced rlhf,

    Y . Sun, N. Salami Pargoo, P. Jin, and J. Ortiz, “Optimizing autonomous driving for safety: A human-centric approach with llm-enhanced rlhf,” inCompanion of the 2024 on ACM International Joint Conference on Pervasive and Ubiquitous Computing, 2024, pp. 76–80

    2024

  25. [33]

    Chatscene: Knowledge-enabled safety- critical scenario generation for autonomous vehicles,

    J. Zhang, C. Xu, and B. Li, “Chatscene: Knowledge-enabled safety- critical scenario generation for autonomous vehicles,” inProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 15 459–15 469

    2024

  26. [34]

    Text2scenario: Text-driven scenario generation for autonomous driving test,

    X. Cai, X. Bai, Z. Cui, D. Xie, D. Fu, H. Yu, and Y . Ren, “Text2scenario: Text-driven scenario generation for autonomous driving test,”arXiv preprint arXiv:2503.02911, 2025

    2025

  27. [35]

    Promptable closed-loop traffic simulation,

    S. Tan, B. Ivanovic, Y . Chen, B. Li, X. Weng, Y . Cao, P. Krähenbühl, and M. Pavone, “Promptable closed-loop traffic simulation,”arXiv preprint arXiv:2409.05863, 2024

    2024

  28. [36]

    Sentence-bert: Sentence embeddings using siamese bert-networks,

    N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,”arXiv preprint arXiv:1908.10084, 2019

    1908 arXiv

  29. [37]

    Billion-scale similarity search with gpus,

    J. Johnson, M. Douze, and H. Jégou, “Billion-scale similarity search with gpus,”IEEE Transactions on Big Data, vol. 7, no. 3, pp. 535–547, 2019

    2019

  30. [38]

    Self-consistency improves chain of thought reasoning in language models,

    X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowd- hery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,”arXiv preprint arXiv:2203.11171, 2022

    2022 arXiv

  31. [39]

    The primacy bias in deep reinforcement learning,

    E. Nikishin, M. Schwarzer, P. D’Oro, P.-L. Bacon, and A. Courville, “The primacy bias in deep reinforcement learning,” inInternational conference on machine learning. PMLR, 2022, pp. 16 828–16 847

    2022

  32. [40]

    Cat: Closed-loop adversarial training for safe end-to-end driving,

    L. Zhang, Z. Peng, Q. Li, and B. Zhou, “Cat: Closed-loop adversarial training for safe end-to-end driving,” in7th Annual Conference on Robot Learning, 2023

    2023

  33. [41]

    Vcat: Vulnerability-aware and curiosity-driven adversarial training for enhanc- ing autonomous vehicle robustness,

    X. Cai, Z. Cui, X. Bai, R. Ke, Z. Ma, H. Yu, and Y . Ren, “Vcat: Vulnerability-aware and curiosity-driven adversarial training for enhanc- ing autonomous vehicle robustness,”arXiv preprint arXiv:2304.02391, 2024

    2024

  34. [42]

    Gpt-5 technical overview,

    OpenAI, “Gpt-5 technical overview,” https://openai.com, 2025

    2025

  35. [43]

    Claude 4 model family,

    Anthropic, “Claude 4 model family,” https://www.anthropic.com, 2025

    2025

  36. [44]

    CARLA: An open urban driving simulator,

    A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V . Koltun, “CARLA: An open urban driving simulator,” inProceedings of the 1st Annual Conference on Robot Learning, 2017, pp. 1–16. APPENDIX A. Initialize Corpus and Generate DSL To better illustrate the overall logical flow ...

    2017

  37. [45]

    ego vehicle going straight with background vehi- cle turning left,

    How to Deal with the Initial Open-source Dataset - Interaction:The initial data source utilized in this work is theINTERACTIONdataset, which provides real-world trajectories of vehicles and pedestrians at urban intersections. To effectively extract structured driving scenarios...

  38. [46]

    The integration process is designed as follows: a) Data-driven Insight Extraction.:The ego vehicle’s motion patterns are summarized by parsing the processed JSON trajectory data

    Integrate knowledge-, data- and adversarial-driven information into a complete natural description:To bridge the gap between raw structured data and downstream DSL generation, we integrate information derived from data-driven, knowledge-driven and adversarial-driven sources in...

  39. [47]

    turn left

    Construct the corpus:After generating structured natural language descriptions, we proceed to construct a structured corpus to support downstream DSL generation. This corpus encapsulates not only polished textual descrip- tions but also modular DSL representations segmented in...

  40. [48]

    DSL Scenario Generation According To User’s Need: To enable flexible scenario generation tailored to diverse user needs, we design a Retrieval-Augmented Generation (RAG) pipeline combined with self-consistency voting mechanisms. Given an arbitrary natural language scenario des...

  41. [49]

    Semantic consistency must be strictly maintained—no changes to the original intent are allowed

    Polish the Natural Language Scenario Description: Transform the following raw natural language driving scenario description into a clear, natural and simulation-oriented English narrative in paragraph form. Semantic consistency must be strictly maintained—no changes to the ori...

  42. [50]

    It is not necessary to specify the full road network structure; instead, provide a template that references reading from an .osm map

    Structure Requirements: • geometry.snippetshould define the first-level scene, including the ego vehicle’s location and movement direction. It is not necessary to specify the full road network structure; instead, provide a template that references reading from an .osm map. Add...

  43. [51]

    Each element in the DSL must have a clear one-to-one semantic correspondence to the narrative description

    Ensure that the semantic content of all three DSL modules matches the polished natural language description. Each element in the DSL must have a clear one-to-one semantic correspondence to the narrative description

  44. [52]

    description

    Each section must begin with import or reference statements based on the existing base classes of our project highway-env. Avoid introducing excessive custom classes to ensure structural consistency and maintainability. For example: from NGSIM_env import utils from NGSIM_env.e...

  45. [53]

    Get Repository:To bridge the gap between DSL generation and executable code, we construct a comprehensive adversarial scenario repository that serves as both a semantic retrieval base and a dynamic code generation backbone. Unlike static corpora, this repository is designed as...

  46. [54]

    When creating scenario representations, please prioritize choosing from existing elements

    Hierarchical Scenario Repository Guidance:The Hierarchical Scenario Repository provides a dictionary of scenario components corresponding to each subcomponent. When creating scenario representations, please prioritize choosing from existing elements. If no appropriate element ...

  47. [55]

    •Syntax alignment checking: a) Ensure semantic consistency between the scenario description and generated scenario representation

    Main Conversion Task:Based on the above description and examples, convert the following testing scenario text into the corresponding scenario representation: {{Input Scenario}} 4)Chain of Thought and Syntax Alignment: •Think step by step to reason about appropriate mappings fr...

  48. [56]

    (generated geometry snippet)

    Retriever Context (Optional): [Scene i] {description} [Scenic Geometry] {geometry} [Scenic Spawn] {spawn} [Scenic Behavior]{behavior} Response: DSL for User’s Need: #------geometry.snippet------# { ... (generated geometry snippet) ... } #------spawn.snippet------# { ... (gener...

  49. [57]

    The overall workflow for DSL-to-Python code generation, including retrieval, prompt construction, and iterative debugging, is illustrated in Figure 8

    DSL-to-Python Code Generation:With the repository prepared, we proceed to detail the process of translating DSL representations into executable simulation codes. The overall workflow for DSL-to-Python code generation, including retrieval, prompt construction, and iterative deb...

  50. [58]

    DSL code block b

    Construct prompt string with the following sections: a. DSL code block b. Hints and instructions c. RAG knowledge for code reference

  51. [59]

    Ensure prompt emphasizes: - Logic preservation - Full simulator object structure - Required Python imports and main execution block

  52. [60]

    Call the LLM with constructed prompt

  53. [61]

    open- closed

    Return the generated response 。。。。。。 Code: #------geometry.snippet------# ...... ....... #------spawn.snippet------# ...... ....... #------behavior.snippet------# ...... ....... Fig. 8. Workflow of DSL-to-Python code generation: retrieval of relevant code fragments, prompt ass...

  54. [62]

    2)Instructions for Code Generation: •Preserve all logic encoded in the DSL behaviors without truncation

    Scenario Context:Optionally append top- k similar code snippets retrieved from the FAISS-based repository as additional context to guide generation. 2)Instructions for Code Generation: •Preserve all logic encoded in the DSL behaviors without truncation. •Convert regions and ve...

  55. [63]

    4)DSL Embedding: •Include the full DSL content within the prompt, properly formatted as a code block

    Syntax Error Correction (Optional):If previous generation attempts failed due to syntax errors, prepend the extracted error message as a corrective hint to guide the next generation. 4)DSL Embedding: •Include the full DSL content within the prompt, properly formatted as a code...

Pith tools

Reviewed May 20, 2026 · model on record in the stance chip above.