EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents
arXiv:2606.03678v1 Announce Type: new 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. Existing methods usually manage this trade-off with handcrafted heuristics, confining generation to known priors and overlooking underexplored patterns. While recent open-ended agentic evolution can push this limit, unconstrained general agents lack strict simulator grounding and tend to collapse the multi-objective tension into single-scalar ma
The proliferation of advanced LLM capabilities combined with the critical need for robust validation in autonomous systems is driving research into more sophisticated scenario generation techniques.
Improving the safety and adversarial testing of autonomous driving systems is central to their widespread adoption and the future of transportation, directly impacting regulatory frameworks and public trust.
The ability to generate more realistic and adversarial safety-critical scenarios via self-improving LLM agents changes how autonomous vehicles are tested and validated, potentially accelerating their development cycles.
- · Autonomous vehicle developers
- · AI safety researchers
- · LLM developers
- · Companies relying on traditional simulation methods
- · Competitors with less advanced testing methodologies
Enhanced ability to find edge cases and vulnerabilities in autonomous driving systems.
Faster, more reliable deployment of autonomous vehicles as safety concerns are addressed more efficiently.
Broader application of self-improving LLM agents for safety validation in other critical AI domains beyond autonomous driving.
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Read at arXiv cs.AI