自动驾驶规划系统中用于学习评分规则的框架
A Framework for Learning Scoring Rules in Autonomous Driving Planning Systems
AI总结：
本文提出 FLoRA 框架，从 NuPlan 正例演示中学习时序逻辑形式的可解释驾驶评分规则，在闭环仿真中优于专家规则和神经网络模型。
摘要：
在自动驾驶系统中，运动规划通常被实现为一个两阶段过程：首先，轨迹提议器生成多条候选轨迹，随后评分机制选择最适合执行的轨迹。对于这一关键选择阶段，基于规则的评分机制尤其具有吸引力，因为它们能够以形式化、人类可理解的格式显式编码驾驶偏好、安全约束和交通法规。然而，人工构建这些评分规则面临重大挑战：规则通常包含复杂的相互依赖关系，需要细致的参数调优，并且可能无法充分捕捉真实世界驾驶数据中存在的细微差别。本工作提出 FLoRA，这是一个通过学习以时序逻辑表示的可解释评分规则来弥合这一差距的新颖框架。我们的方法具有可学习的逻辑结构，可捕捉不同驾驶场景中的细微关系，并直接从 NuPlan 中收集的真实世界驾驶演示中同时优化规则和参数。即使训练数据仅包含正例（成功驾驶演示），我们的方法仍能有效学习评估驾驶行为。在闭环规划仿真中的评估表明，我们学习到的评分规则优于现有技术，包括专家设计的规则和神经网络评分模型，同时保持可解释性。本工作提出了一种数据驱动方法来增强自动驾驶系统中的评分机制，该方法被设计为插件模块，可与各种轨迹提议器无缝集成。我们的视频和代码可在 xiong.zikang.me/FLoRA 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
