融合深度学习与经典规划，提升自动驾驶行为决策的可靠性与安全性。
Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
- 用神经网络理解复杂交通场景并生成驾驶行为
- 优化层验证行为并强制执行安全约束，保障可解释性
- 在真实城市道路和实车部署中验证有效性
近年来，机器学习与深度学习在自动化车辆行为规划中的应用展现出巨大潜力，尤其在复杂交通环境下。然而，其复杂性和缺乏透明度会影响可解释性与可信度，增加安全验证难度。为此，我们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及确定性。具体而言，设计一个深度神经网络以解析复杂交通场景并提出驾驶行为，同时引入基于优化的监督层对行为进行验证，并显式施加可行驶性与安全性约束。我们在真实城市数据上开展开环测试，探讨系统集成以实现稳定闭环运行，并报告了在研究车辆karl上的实车部署结果。
原文摘要 · Abstract (English)
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..
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