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.
Available: https://arxiv.org/abs/2406.10857
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LiloDriver uses LLMs and memory-augmented planning in a four-stage pipeline to outperform rule-based and learning-based methods on both common and rare scenarios in the nuPlan benchmark.
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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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LiloDriver: A Lifelong Learning Framework for Closed-loop Motion Planning in Long-tail Autonomous Driving Scenarios
LiloDriver uses LLMs and memory-augmented planning in a four-stage pipeline to outperform rule-based and learning-based methods on both common and rare scenarios in the nuPlan benchmark.