基于学习的自动驾驶行为规划：现实世界集成与部署
Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
- RWTH Aachen University（亚琛工业大学）
- Technical University of Munich（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出结合机器学习与经典方法的混合规划架构，通过深度神经网络解读交通场景、优化监督层验证约束，经实车部署验证其在自动驾驶行为规划中的有效性。
AI中文摘要：
近期机器学习与深度学习研究表明，基于学习的运动规划方法具备改善自动驾驶车辆驾驶行为的潜力，尤其适用于复杂环境，但这类方法的复杂性与透明度不足会阻碍可解释性、可信度，并增加安全保障难度。针对这些挑战，本文提出一种混合规划架构，结合机器学习的优势与经典方法的可验证性和确定性。具体而言，我们开发了一个深度神经网络，用于解读复杂交通场景并提出驾驶行为方案；同时设置基于优化的监督层，验证该方案并强制执行明确的可驾驶性与安全约束。我们在真实城市数据上进行开环研究以评估学习规划器的驾驶行为，探讨稳定闭环运行的系统集成要点，并报告在研究车辆karl上进行现实世界部署的结果。
英文摘要：
Recent research in machine and deep learning has shown the potential of learningbased motion planning approaches to improve the driving behavior of automated vehicles, especially in complex environments. However, their complex nature and lack of transparency can hinder explainability and trustworthiness and complicate safety assurance. Motivated by these challenges, we propose a hybrid planning architecture that combines the advantages of machine learning with the verifiability and the determinism of classical approaches. Specifically, we developed a deep neural network to interpret complex traffic scenes and propose driving behavior, while an optimization-based supervision layer validates this proposal and enforces explicit drivability and safety constraints. We evaluate the learned planner's driving behavior in open-loop studies on real-world urban data, discuss system integration aspects for stable closed-loop operation, and report results from real-world deployment on our research vehicle karl..
