EvoDrive presents an LLM-based agentic evolution framework that generates diverse safety-critical autonomous driving scenarios by maintaining a Pareto archive of attack-realism trade-offs using simulator feedback.
Frea: Feasibility-guided generation of safety-critical scenarios with reason- able adversariality
3 Pith papers cite this work. Polarity classification is still indexing.
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PCASim uses LLMs to integrate knowledge, data, and adversarial methods for generating promptable safety-critical urban traffic scenarios, with RL training for vehicle behaviors, reporting 12% better DSL accuracy, 8% higher scenario success rate, and 30% improved obstacle avoidance.
SAGE reframes adversarial scenario generation as multi-objective preference alignment, using hierarchical group-based optimization and test-time linear interpolation of two expert policies to enable steerable control over adversariality-realism trade-offs.
citing papers explorer
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EvoDrive: Pareto Evolution for Safety-Critical Autonomous Driving via Self-Improving LLM Agents
EvoDrive presents an LLM-based agentic evolution framework that generates diverse safety-critical autonomous driving scenarios by maintaining a Pareto archive of attack-realism trade-offs using simulator feedback.
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PCASim: Promptable Closed-loop Adversarial Simulation for Urban Traffic Environment
PCASim uses LLMs to integrate knowledge, data, and adversarial methods for generating promptable safety-critical urban traffic scenarios, with RL training for vehicle behaviors, reporting 12% better DSL accuracy, 8% higher scenario success rate, and 30% improved obstacle avoidance.
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Steerable Adversarial Scenario Generation through Test-Time Preference Alignment
SAGE reframes adversarial scenario generation as multi-objective preference alignment, using hierarchical group-based optimization and test-time linear interpolation of two expert policies to enable steerable control over adversariality-realism trade-offs.