A Framework for Learning Scoring Rules in Autonomous Driving Planning Systems

• URL: http://arxiv.org/abs/2502.11352v1
• Date: Mon, 17 Feb 2025 02:06:57 GMT
• Title: A Framework for Learning Scoring Rules in Autonomous Driving Planning Systems
• Authors: Zikang Xiong, Joe Kurian Eappen, Suresh Jagannathan,
• Abstract summary: FLoRA is a framework that learns interpretable scoring rules represented in temporal logic. Our approach effectively learns to evaluate driving behavior even though the training data only contains positive examples. Evaluations in closed-loop planning simulations demonstrate that our learned scoring rules outperform existing techniques.
• Score: 2.4578723416255754
• License:
• Abstract: In autonomous driving systems, motion planning is commonly implemented as a two-stage process: first, a trajectory proposer generates multiple candidate trajectories, then a scoring mechanism selects the most suitable trajectory for execution. For this critical selection stage, rule-based scoring mechanisms are particularly appealing as they can explicitly encode driving preferences, safety constraints, and traffic regulations in a formalized, human-understandable format. However, manually crafting these scoring rules presents significant challenges: the rules often contain complex interdependencies, require careful parameter tuning, and may not fully capture the nuances present in real-world driving data. This work introduces FLoRA, a novel framework that bridges this gap by learning interpretable scoring rules represented in temporal logic. Our method features a learnable logic structure that captures nuanced relationships across diverse driving scenarios, optimizing both rules and parameters directly from real-world driving demonstrations collected in NuPlan. Our approach effectively learns to evaluate driving behavior even though the training data only contains positive examples (successful driving demonstrations). Evaluations in closed-loop planning simulations demonstrate that our learned scoring rules outperform existing techniques, including expert-designed rules and neural network scoring models, while maintaining interpretability. This work introduces a data-driven approach to enhance the scoring mechanism in autonomous driving systems, designed as a plug-in module to seamlessly integrate with various trajectory proposers. Our video and code are available on xiong.zikang.me/FLoRA.

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