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AnchorDrive: LLM Scenario Rollout with Anchor-Guided Diffusion Regeneration for Safety-Critical Scenario Generation

arXiv:2603.02542v1 Announce Type: new Abstract: Autonomous driving systems require comprehensive evaluation in safety-critical scenarios to ensure safety and robustness. However, such scenarios are rare and difficult to collect from real-world driving data, necessitating simulation-based synthesis. Yet, existing methods often exhibit limitations in both controllability and realism. From a capability perspective, LLMs excel at controllable generation guided by natural language instructions, while diffusion models are better suited for producing trajectories consistent with realistic driving distributions. Leveraging their complementary strengths, we propose AnchorDrive, a two-stage safety-critical scenario generation framework. In the first stage, we deploy an LLM as a driver agent within a closed-loop simulation, which reasons and iteratively outputs control commands under natural language constraints; a plan assessor reviews these commands and provides corrective feedback, enabling s

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Zhulin Jiang, Zetao Li, Cheng Wang, Ziwen Wang, Chen Xiong · · 1 min read · 14 views
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arXiv:2603.02542v1 Announce Type: new Abstract: Autonomous driving systems require comprehensive evaluation in safety-critical scenarios to ensure safety and robustness. However, such scenarios are rare and difficult to collect from real-world driving data, necessitating simulation-based synthesis. Yet, existing methods often exhibit limitations in both controllability and realism. From a capability perspective, LLMs excel at controllable generation guided by natural language instructions, while diffusion models are better suited for producing trajectories consistent with realistic driving distributions. Leveraging their complementary strengths, we propose AnchorDrive, a two-stage safety-critical scenario generation framework. In the first stage, we deploy an LLM as a driver agent within a closed-loop simulation, which reasons and iteratively outputs control commands under natural language constraints; a plan assessor reviews these commands and provides corrective feedback, enabling semantically controllable scenario generation. In the second stage, the LLM extracts key anchor points from the first-stage trajectories as guidance objectives, which jointly with other guidance terms steer the diffusion model to regenerate complete trajectories with improved realism while preserving user-specified intent. Experiments on the highD dataset demonstrate that AnchorDrive achieves superior overall performance in criticality, realism, and controllability, validating its effectiveness for generating controllable and realistic safety-critical scenarios.

Executive Summary

This article proposes AnchorDrive, a two-stage safety-critical scenario generation framework for autonomous driving systems, leveraging the strengths of Large Language Models (LLMs) and diffusion models. In the first stage, an LLM generates control commands under natural language constraints, while in the second stage, a diffusion model regenerates complete trajectories. Experiments on the highD dataset demonstrate AnchorDrive's effectiveness in generating controllable and realistic safety-critical scenarios. While the approach shows promise, its scalability and adaptability to diverse driving environments remain unexplored. The proposed framework has the potential to improve the safety and robustness of autonomous driving systems, but further research is needed to address its limitations.

Key Points

  • ▸ AnchorDrive is a two-stage safety-critical scenario generation framework for autonomous driving systems.
  • ▸ The framework leverages LLMs and diffusion models to generate controllable and realistic scenarios.
  • ▸ Experiments on the highD dataset demonstrate AnchorDrive's effectiveness and superiority over existing methods.

Merits

Strength in Controllability

AnchorDrive's use of LLMs enables semantically controllable scenario generation, allowing for the specification of user-defined constraints and intents.

Strength in Realism

The diffusion model regenerates complete trajectories with improved realism, preserving user-specified intent and leveraging the guidance terms extracted by the LLM.

Demerits

Limitation in Scalability

The proposed framework may not be scalable to diverse driving environments, requiring further research to adapt to varying conditions and complexities.

Limitation in Adaptability

AnchorDrive's reliance on LLMs and diffusion models may limit its adaptability to emerging driving scenarios and edge cases.

Expert Commentary

The proposed AnchorDrive framework demonstrates the potential for combining LLMs and diffusion models to generate realistic and controllable safety-critical scenarios. However, the scalability and adaptability of the framework remain unexplored, and further research is needed to address these limitations. As the autonomous driving industry continues to evolve, frameworks like AnchorDrive will play a crucial role in advancing the safety and robustness of these systems. Nonetheless, it is essential to consider the broader implications of such frameworks, including their potential impact on regulations, policies, and the overall safety of road users.

Recommendations

  • ✓ Further research is needed to explore the scalability and adaptability of AnchorDrive in diverse driving environments and scenarios.
  • ✓ Industry stakeholders and regulatory bodies should collaborate to develop guidelines and standards for the deployment and integration of frameworks like AnchorDrive in autonomous driving systems.

Sources

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