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arxiv: 2503.09035 · v1 · submitted 2025-03-12 · 💻 cs.RO · cs.AI· cs.SY· eess.SY

ManeuverGPT Agentic Control for Safe Autonomous Stunt Maneuvers

Shawn Azdam , Pranav Doma , Aliasghar Moj Arab This is my paper

Pith reviewed 2026-05-23 00:35 UTC · model grok-4.3

classification 💻 cs.RO cs.AIcs.SYeess.SY
keywords autonomous vehiclesLLM agentsJ-turnCARLA simulationstunt maneuversagentic controlvehicle dynamicsparameter validation
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The pith

LLM agents generate and validate parameters for safe J-turn maneuvers in autonomous vehicles across different dynamics.

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

The paper tries to establish that large language models can act as controllers for aggressive vehicle maneuvers like J-turns by using a team of specialized agents. One agent enriches user commands, another generates control parameters from text prompts, and a third checks those parameters against physics and safety rules. This setup runs in the CARLA simulator and produces working maneuvers for several vehicle types starting with no prior training on the task. A sympathetic reader would care because it suggests autonomous cars could one day perform evasive actions at the limits of handling using flexible language-based reasoning rather than fixed code for every case.

Core claim

ManeuverGPT shows that an agentic architecture with a Query Enricher Agent, a Driver Agent, and a Parameter Validator Agent can turn textual prompts into executable J-turn parameters that succeed across multiple vehicle models in the CARLA simulator. The process starts tabula rasa and refines parameters iteratively, with the validator enforcing physics-based and safety constraints so the maneuvers remain within safe limits.

What carries the argument

The three-agent architecture in which the Parameter Validator Agent enforces physics-based and safety constraints on parameters generated by the Driver Agent from enriched textual prompts.

If this is right

  • Textual prompts adapt the same maneuver to vehicles with differing dynamics without retraining.
  • J-turns execute successfully across multiple vehicle models when the validator is applied.
  • The hybrid combination of language reasoning and algorithmic validation supports high-dynamic control.
  • Performance can be measured against established success criteria for the maneuvers.

Where Pith is reading between the lines

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

  • The same validator structure could support other aggressive maneuvers if its constraint rules are extended.
  • Closing the gap to real vehicles would require testing how simulation-validated parameters behave under sensor noise and actuator delays.
  • Improving numeric precision in the LLM output could reduce reliance on post-validation fixes.

Load-bearing premise

The Parameter Validator Agent can reliably enforce physics-based and safety constraints on LLM-generated parameters to ensure safe maneuvers.

What would settle it

A simulation trial in which parameters approved by the validator still produce a rollover, loss of control, or failed J-turn would show the validation step is insufficient.

Figures

Figures reproduced from arXiv: 2503.09035 by Aliasghar Moj Arab, Pranav Doma, Shawn Azdam.

Figure 1
Figure 1. Figure 1: The five-phase J-turn maneuver executed by our [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed agentic framework. The system comprises [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Time series of vehicle velocities during a J-turn maneuver, showing [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Angle error comparison between single-agent and multi-agent systems. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of angle error between sedan and sports coupe vehicle [PITH_FULL_IMAGE:figures/full_fig_p005_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Learning progress of the steering controller for sedan and sports [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
read the original abstract

The next generation of active safety features in autonomous vehicles should be capable of safely executing evasive hazard-avoidance maneuvers akin to those performed by professional stunt drivers to achieve high-agility motion at the limits of vehicle handling. This paper presents a novel framework, ManeuverGPT, for generating and executing high-dynamic stunt maneuvers in autonomous vehicles using large language model (LLM)-based agents as controllers. We target aggressive maneuvers, such as J-turns, within the CARLA simulation environment and demonstrate an iterative, prompt-based approach to refine vehicle control parameters, starting tabula rasa without retraining model weights. We propose an agentic architecture comprised of three specialized agents (1) a Query Enricher Agent for contextualizing user commands, (2) a Driver Agent for generating maneuver parameters, and (3) a Parameter Validator Agent that enforces physics-based and safety constraints. Experimental results demonstrate successful J-turn execution across multiple vehicle models through textual prompts that adapt to differing vehicle dynamics. We evaluate performance via established success criteria and discuss limitations regarding numeric precision and scenario complexity. Our findings underscore the potential of LLM-driven control for flexible, high-dynamic maneuvers, while highlighting the importance of hybrid approaches that combine language-based reasoning with algorithmic validation.

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 paper presents ManeuverGPT, a three-agent LLM-based framework (Query Enricher Agent, Driver Agent, Parameter Validator Agent) for generating textual prompts that produce parameters for high-dynamic autonomous vehicle maneuvers such as J-turns in the CARLA simulator. It claims that this prompt-based approach, starting without model retraining, successfully executes J-turns across multiple vehicle models by adapting to their dynamics, with the validator enforcing physics and safety constraints, and evaluates performance via established success criteria while noting limitations in numeric precision.

Significance. If the experimental claims hold with quantitative support, the work would demonstrate a novel hybrid LLM-algorithmic approach to flexible, high-agility control that adapts via prompts rather than retraining, potentially relevant for evasive maneuvers in autonomous vehicles; however, the simulation-only setting and absence of metrics limit immediate broader impact.

major comments (2)
  1. [Abstract] Abstract and experimental results: the claim of 'successful J-turn execution across multiple vehicle models' lacks any reported quantitative success rates, error bars, validation metrics, or explicit success criteria definitions, despite the abstract noting limitations in numeric precision; this directly weakens support for the headline experimental claim.
  2. [Proposed agentic architecture] Architecture description (three-agent system): the Parameter Validator Agent is positioned as the sole enforcer of physics-based and safety constraints, yet the manuscript provides no implementation details, constraint definitions, rejection/correction rates, or ablation studies on its performance; this is load-bearing for attributing observed successes to the agentic approach rather than prompt iteration alone.
minor comments (1)
  1. Notation for agent roles and prompt templates could be clarified with explicit pseudocode or example prompts to improve reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive feedback on our manuscript. We address each major comment below and commit to revisions that will strengthen the presentation of our experimental claims and architectural details.

read point-by-point responses

  1. Referee: [Abstract] Abstract and experimental results: the claim of 'successful J-turn execution across multiple vehicle models' lacks any reported quantitative success rates, error bars, validation metrics, or explicit success criteria definitions, despite the abstract noting limitations in numeric precision; this directly weakens support for the headline experimental claim.

    Authors: We agree that the abstract and results would benefit from explicit quantitative support. The manuscript references evaluation via established success criteria but does not report numerical success rates or error bars. In the revised version we will add a results table or subsection with success rates across vehicle models, explicit definitions of the success criteria, and any available validation metrics to better substantiate the claims. revision: yes

  2. Referee: [Proposed agentic architecture] Architecture description (three-agent system): the Parameter Validator Agent is positioned as the sole enforcer of physics-based and safety constraints, yet the manuscript provides no implementation details, constraint definitions, rejection/correction rates, or ablation studies on its performance; this is load-bearing for attributing observed successes to the agentic approach rather than prompt iteration alone.

    Authors: We acknowledge that the current description of the Parameter Validator Agent is high-level. We will expand the architecture section to include specific constraint definitions, implementation details of the validation logic, and any rejection or correction statistics obtained during our experiments. Ablation studies isolating the validator were not conducted; we will note this limitation and clarify how the validator contributes to the observed results versus prompt iteration alone. revision: partial

Circularity Check

0 steps flagged

No circularity: experimental demonstration with no derivation chain

full rationale

The paper presents an agentic LLM framework for generating vehicle maneuver parameters in simulation, with success measured directly via experimental J-turn executions across vehicle models. No mathematical derivation, first-principles prediction, or parameter fitting is claimed; results are reported from prompt-based iterations and agent interactions rather than any reduction to inputs by construction. The Parameter Validator Agent is described as an enforcement component but is not used to derive or predict any result—it is part of the runtime architecture. No self-citations, ansatzes, or uniqueness theorems appear as load-bearing elements. The work is therefore self-contained as an empirical demonstration and receives score 0.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 1 invented entities

The approach relies on LLM capabilities for parameter generation and the CARLA simulator for testing. Free parameters are the author-designed prompts and validation thresholds. No new physical entities are postulated.

free parameters (1)
  • Prompt templates for agents
    Specific textual prompts used to query the Query Enricher, Driver, and Validator agents are chosen by the authors to achieve adaptation across vehicle models.
axioms (2)
  • domain assumption CARLA simulator provides accurate enough vehicle dynamics for evaluating J-turn maneuvers
    All experimental results depend on the simulation environment faithfully representing real physics and safety constraints.
  • domain assumption LLM outputs can be iteratively refined via prompts to meet validator constraints
    The iterative prompt-based approach assumes the agents will converge on valid parameters without retraining.
invented entities (1)
  • Three-agent architecture (Query Enricher Agent, Driver Agent, Parameter Validator Agent) no independent evidence
    purpose: To handle user commands, generate control parameters, and enforce constraints for stunt maneuvers
    These agents form the core proposed framework, with no independent evidence provided beyond the simulation results.

pith-pipeline@v0.9.0 · 5761 in / 1359 out tokens · 78747 ms · 2026-05-23T00:35:56.708914+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

22 extracted references · 22 canonical work pages · 2 internal anchors

  1. [1]

    High-resolution safety verification for evasive obstacle avoidance in autonomous vehicles,

    A. Arab, M. Khaleghi, A. Partovi, A. Abbaspour, C. Shinde, Y . Mousavi, V . Azimi, and A. Karimmoddini, “High-resolution safety verification for evasive obstacle avoidance in autonomous vehicles,”IEEE Open Journal of Vehicular Technology, vol. 6, pp. 276 – 287, 2024

    work page 2024

  2. [2]

    Safe agile hazard avoidance system for autonomous vehicles,

    A. Arab and J. Yi, “Safe agile hazard avoidance system for autonomous vehicles,” Jan. 4 2024, uS Patent App. 18/209,943

    work page 2024

  3. [3]

    On the Opportunities and Risks of Foundation Models

    R. Bommasani, D. A. Hudson, E. Adeli, R. Altman, S. Arora, S. von Arx, M. S. Bernstein, J. Bohg, A. Bosselut, E. Brunskill et al. , “On the opportunities and risks of foundation models,” arXiv preprint arXiv:2108.07258, 2021

    work page internal anchor Pith review Pith/arXiv arXiv 2021

  4. [4]

    Adaptive cruise control based on safe deep reinforcement learning,

    R. Zhao, K. Wang, W. Che, Y . Li, Y . Fan, and F. Gao, “Adaptive cruise control based on safe deep reinforcement learning,” Sensors, vol. 24, no. 8, p. 2657, 2024

    work page 2024

  5. [5]

    An ml-aided reinforcement learning approach for challenging vehicle maneuvers,

    D. C. Selvaraj, S. Hegde, N. Amati, F. Deflorio, and C. F. Chiasserini, “An ml-aided reinforcement learning approach for challenging vehicle maneuvers,” IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1686–1698, 2022

    work page 2022

  6. [6]

    Deep reinforcement learning in autonomous car path planning and control: A survey,

    Y . Chen, C. Ji, Y . Cai, T. Yan, and B. Su, “Deep reinforcement learning in autonomous car path planning and control: A survey,” arXiv preprint arXiv:2404.00340, 2024

    work page arXiv 2024

  7. [7]

    Automated Driving Maneuvers under Interactive Environment based on Deep Reinforcement Learning

    P. Wang, C.-Y . Chan, and H. Li, “Automated driving maneuvers under interactive environment based on deep reinforcement learning,” arXiv preprint arXiv:1803.09200, 2018

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  8. [8]

    Self-planning code generation with large language models,

    X. Jiang, Y . Dong, L. Wang, Z. Fang, Q. Shang, G. Li, Z. Jin, and W. Jiao, “Self-planning code generation with large language models,” ACM Transactions on Software Engineering and Methodology , vol. 33, no. 7, pp. 1–30, 2024

    work page 2024

  9. [9]

    Verifiably following complex robot instructions with foundation models,

    B. Quartey, E. Rosen, S. Tellex, and G. Konidaris, “Verifiably following complex robot instructions with foundation models,” arXiv preprint arXiv:2402.11498, 2024

    work page arXiv 2024

  10. [10]

    Instructed reinforcement learning control of safe au- tonomous j-turn vehicle maneuvers,

    A. Arab and J. Yi, “Instructed reinforcement learning control of safe au- tonomous j-turn vehicle maneuvers,” in 2021 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) . IEEE, 2021, pp. 1058–1063

    work page 2021

  11. [11]

    Dynamic behavior of the full-car model in the j-turn maneuver by considering the engine gyroscopic effects,

    A. Shahabi, A. H. Kazemian, S. Farahat, and F. Sarhaddi, “Dynamic behavior of the full-car model in the j-turn maneuver by considering the engine gyroscopic effects,” Communications-Scientific letters of the University of Zilina , vol. 23, no. 3, pp. B237–B249, 2021

    work page 2021

  12. [12]

    Carla: An open urban driving simulator,

    A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V . Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning . PMLR, 2017, pp. 1–16

    work page 2017

  13. [13]

    Large language models for robotics: A survey,

    F. Zeng, W. Gan, Y . Wang, N. Liu, and P. S. Yu, “Large language models for robotics: A survey,” arXiv preprint arXiv:2311.07226 , 2023

    work page arXiv 2023

  14. [14]

    Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,

    W. Huang, P. Abbeel, D. Pathak, and I. Mordatch, “Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,” inInternational conference on machine learning. PMLR, 2022, pp. 9118–9147

    work page 2022

  15. [15]

    Large lan- guage models are zero-shot reasoners,

    T. Kojima, S. S. Gu, M. Reid, Y . Matsuo, and Y . Iwasawa, “Large lan- guage models are zero-shot reasoners,” Advances in neural information processing systems, vol. 35, pp. 22 199–22 213, 2022

    work page 2022

  16. [16]

    Languagempc: Large language models as decision makers for autonomous driving

    H. Sha, Y . Mu, Y . Jiang, G. Zhan, L. Chen, C. Xu, P. Luo, S. E. Li, M. Tomizuka, W. Zhan, and M. Ding, “Languagempc: Large language models as decision makers for autonomous driving,” arXiv preprint arXiv:2310.03026, 2023

    work page arXiv 2023

  17. [17]

    Large language models for autonomous driving (llm4ad): Concept, benchmark, simulation, and real-vehicle experiment,

    C. Cui, Y . Ma, Z. Yang, Y . Zhou, P. Liu, J. Lu, L. Li, Y . Chen, J. H. Panchal, A. Abdelraouf et al., “Large language models for autonomous driving (llm4ad): Concept, benchmark, simulation, and real-vehicle experiment,” arXiv preprint arXiv:2410.15281 , 2024

    work page arXiv 2024

  18. [18]

    Autonomous lateral maneuvers for self-driving vehicles in complex traffic environment,

    Z. Li, J. Jiang, W.-H. Chen, and L. Sun, “Autonomous lateral maneuvers for self-driving vehicles in complex traffic environment,” IEEE Trans- actions on Intelligent Vehicles , vol. 8, no. 2, pp. 1900–1910, 2023

    work page 1900

  19. [19]

    Motion planning and control of autonomous aggressive vehicle maneuvers,

    A. Arab, K. Yu, J. Yu, and J. Yi, “Motion planning and control of autonomous aggressive vehicle maneuvers,” IEEE Transactions on Automation Science and Engineering , vol. 21, pp. 1488–1500, 2024

    work page 2024

  20. [20]

    Asynchronous large language model enhanced planner for autonomous driving,

    Y . Chen, Z. han Ding, Z. Wang, Y . Wang, L. Zhang, and S. Liu, “Asynchronous large language model enhanced planner for autonomous driving,” 2024. [Online]. Available: https://arxiv.org/abs/2406.14556

    work page arXiv 2024

  21. [21]

    Vlm-mpc: Model predictive controller augmented vision language model for autonomous driving,

    K. Long, H. Shi, J. Liu, C. Xiao, and X. Li, “Vlm-mpc: Model predictive controller augmented vision language model for autonomous driving,” arXiv preprint, 2024

    work page 2024

  22. [22]

    Rajamani, Vehicle dynamics and control

    R. Rajamani, Vehicle dynamics and control . Springer Science & Business Media, 2011

    work page 2011