Lab2Car让任意规划器安全落地真实道路，解决自动驾驶部署难题。
Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in Complex Real-world Environments
- 用优化方法将任意规划器的轨迹转化为可执行的安全轨迹
- 在拉斯维加斯实车测试中成功应对变道、超车等复杂场景
- 适合想快速验证新规划算法的研究者与工程师
人类级自动驾驶仍是遥不可及的目标，其中规划与决策——决定驾驶行为的认知功能——是最具挑战性的环节。尽管已有众多有前景的方法，但进展受限于实验性规划器在自然环境中的部署困难。本文提出 Lab2Car，一种基于优化的封装工具，可将任意运动规划器生成的轨迹草图转换为安全、舒适且动力学可行的可执行轨迹，使不具备此类保证的规划器也能在真实世界中安全测试与优化。我们通过在拉斯维加斯的自驾车中部署机器学习规划器和经典规划器，验证了 Lab2Car 的通用性。系统成功处理了包括突然切入、超车和让行在内的复杂城市场景，如赌场接送区。本工作为候选运动规划器在真实环境中的快速部署与评估铺平了道路，支持快速迭代，加速实现人类级自动驾驶。
原文摘要 · Abstract (English)
Human-level autonomous driving is an ever-elusive goal, with planning and decision making -- the cognitive functions that determine driving behavior -- posing the greatest challenge. Despite a proliferation of promising approaches, progress is stifled by the difficulty of deploying experimental planners in naturalistic settings. In this work, we propose Lab2Car, an optimization-based wrapper that can take a trajectory sketch from an arbitrary motion planner and convert it to a safe, comfortable, dynamically feasible trajectory that the car can follow. This allows motion planners that do not provide such guarantees to be safely tested and optimized in real-world environments. We demonstrate the versatility of Lab2Car by using it to deploy a machine learning (ML) planner and a classical planner on self-driving cars in Las Vegas. The resulting systems handle challenging scenarios, such as cut-ins, overtaking, and yielding, in complex urban environments like casino pick-up/drop-off areas. Our work paves the way for quickly deploying and evaluating candidate motion planners in realistic settings, ensuring rapid iteration and accelerating progress towards human-level autonomy.
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