百度 Apollo EM 运动规划器
Baidu Apollo EM Motion Planner
AI总结：
本文提出基于百度 Apollo 开源自动驾驶平台的实时运动规划系统，解决工业级4级运动规划问题，兼顾安全性、舒适性和可扩展性，通过分层结构实现多车道和单车道自动驾驶。
摘要：
本文介绍了一种基于百度 Apollo（开源）自动驾驶平台的实时运动规划系统。该系统旨在解决工业级4级运动规划问题，同时考虑安全性、舒适性和可扩展性。系统采用分层结构处理多车道和单车道自动驾驶：（1）系统顶层为多车道策略，通过并行计算的车道级轨迹进行比较以处理变道场景。（2）在车道级轨迹生成器中，基于弗伦兹框架迭代求解路径和速度优化。（3）对于路径和速度优化，提出结合动态规划和基于样条的二次规划的方法，构建可扩展且易于调节的框架，同时处理交通规则、障碍物决策和平滑性。该规划器可扩展至高速公路和低速城市驾驶场景。我们通过场景示例和道路测试结果展示了该算法。本文描述的系统自2017年9月Apollo v1.5发布以来已部署到数十辆百度Apollo自动驾驶车辆。截至2018年5月16日，该系统已在各种城市场景下进行了3,380小时和约68,000公里（42,253英里）的闭环自动驾驶测试。本文描述的算法可在https://github.com/ApolloAuto/apollo/tree/master/modules/planning上获得。
英文摘要：
In this manuscript, we introduce a real-time motion planning system based on the Baidu Apollo (open source) autonomous driving platform. The developed system aims to address the industrial level-4 motion planning problem while considering safety, comfort and scalability. The system covers multilane and single-lane autonomous driving in a hierarchical manner: (1) The top layer of the system is a multilane strategy that handles lane-change scenarios by comparing lane-level trajectories computed in parallel. (2) Inside the lane-level trajectory generator, it iteratively solves path and speed optimization based on a Frenet frame. (3) For path and speed optimization, a combination of dynamic programming and spline-based quadratic programming is proposed to construct a scalable and easy-to-tune framework to handle traffic rules, obstacle decisions and smoothness simultaneously. The planner is scalable to both highway and lower-speed city driving scenarios. We also demonstrate the algorithm through scenario illustrations and on-road test results. The system described in this manuscript has been deployed to dozens of Baidu Apollo autonomous driving vehicles since Apollo v1.5 was announced in September 2017. As of May 16th, 2018, the system has been tested under 3,380 hours and approximately 68,000 kilometers (42,253 miles) of closed-loop autonomous driving under various urban scenarios. The algorithm described in this manuscript is available at https://github.com/ApolloAuto/apollo/tree/master/modules/planning.
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。
