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arXiv · 2404.07569

Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?

Marcel Hallgarten·Julian Zapata·Martin Stoll·Katrin Renz·Andreas Zell
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Abstract

Real-world autonomous driving systems must make safe decisions in the face of rare and diverse traffic scenarios. Current state-of-the-art planners are mostly evaluated on real-world datasets like nuScenes (open-loop) or nuPlan (closed-loop). In particular, nuPlan seems to be an expressive evaluation method since it is based on real-world data and closed-loop, yet it mostly covers basic driving scenarios. This makes it difficult to judge a planner's capabilities to generalize to rarely-seen situations. Therefore, we propose a novel closed-loop benchmark interPlan containing several edge cases and challenging driving scenarios. We assess existing state-of-the-art planners on our benchmark and show that neither rule-based nor learning-based planners can safely navigate the interPlan scenarios. A recently evolving direction is the usage of foundation models like large language models (LLM) to handle generalization. We evaluate an LLM-only planner and introduce a novel hybrid planner that combines an LLM-based behavior planner with a rule-based motion planner that achieves state-of-the-art performance on our benchmark.

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Marcel Hallgarten, Julian Zapata, Martin Stoll, Katrin Renz, Andreas Zell. 2024-04-11. Can Vehicle Motion Planning Generalize to Realistic Long-tail Scenarios?. https://arxiv.org/abs/2404.07569

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