自动驾驶圈可以看看这篇：他们没让神经网络直接开车，而是给它加了安全监督层，还在自家车karl.上真跑了。
该论文提出一种混合规划架构，将深度神经网络与基于优化的监督层结合。神经网络负责解释复杂交通场景并提出驾驶行为，监督层则验证提议并施加可驾驶性和安全约束。作者在真实城市数据的开环研究中评估了学习规划器的行为，并讨论了闭环稳定运行的系统集成问题。研究团队还在其研究车辆karl.上完成了实际部署。
Learning-Based Behavior Planning for Automated Driving: Real-World Integration and Deployment
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..