EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents
EvoDrive is a new framework designed for generating safety-critical scenarios in autonomous driving systems. It utilizes a simulator-grounded actor-critic architecture to enhance the generation process while maintaining realism and maximizing adversariality. The framework has shown promising results in expanding the Pareto frontier and producing valuable scenarios for policy training in autonomous vehicles.
- ▪EvoDrive is the first automated, LLM-based agentic evolution framework for multi-objective scenario generation.
- ▪The framework employs a memory-driven actor and a self-evolving world evaluator to optimize simulation budgets.
- ▪Benchmark results indicate that EvoDrive significantly expands the Pareto frontier across various generators.
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Computer Science > Artificial Intelligence arXiv:2606.03678 (cs) [Submitted on 2 Jun 2026] Title:EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents Authors:Tong Nie, Yuewen Mei, Yihong Tang, Junlin He, Jie Deng, Jian Sun, Wei Ma View a PDF of the paper titled EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents, by Tong Nie and 6 other authors View PDF Abstract:Generating safety-critical scenarios is essential for validating and improving autonomous driving systems, yet it inherently requires maximizing adversariality to expose failures while preserving realism.
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