• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications
【24h】

Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications

机译：自主城市驾驶的实时轨迹规划：框架，算法和验证

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper focuses on the real-time trajectory planning problem for autonomous vehicles driving in realistic urban environments. To solve the complex navigation problem, we adopt a hierarchical motion planning framework. First, a rough reference path is extracted from the digital map using commands from the high-level behavioral planner. The conjugate gradient nonlinear optimization algorithm and the cubic B-spline curve are employed to smoothen and interpolate the reference path sequentially. To follow the refined reference path as well as handle both static and moving objects, the trajectory planning task is decoupled into lateral and longitudinal planning problems within the curvilinear coordinate framework. A rich set of kinematically feasible path candidates are generated to deal with the dynamic traffic both deliberatively and reactively. In the meanwhile, the velocity profile generation is performed to improve driving safety and comfort. After that, the generated trajectories are carefully evaluated by an objective function, which combines behavioral decisions by reasoning about the traffic situations. The optimal collision-free, smooth, and dynamically feasible trajectory is selected and transformed into commands executed by the low-level lateral and longitudinal controllers. Field experiments have been carried out with our test autonomous vehicle on the realistic inner-city roads. The experimental results demonstrated capabilities and effectiveness of the proposed trajectory planning framework and algorithms to safely handle a variety of typical driving scenarios, such as static and moving objects avoidance, lane keeping, and vehicle following, while respecting the traffic rules.
机译：本文着重于在现实的城市环境中驾驶自动驾驶车辆的实时轨迹规划问题。为了解决复杂的导航问题，我们采用了分层运动计划框架。首先，使用高级行为计划者的命令从数字地图中提取出大致的参考路径。共轭梯度非线性优化算法和三次B样条曲线被用来平滑和插值参考路径。为了遵循精炼的参考路径并处理静态对象和移动对象，轨迹规划任务在曲线坐标框架内被分解为横向和纵向规划问题。生成了丰富的运动学可行路径候选集，以有意和有反应地处理动态流量。同时，执行速度分布图生成以提高驾驶安全性和舒适性。之后，通过目标函数仔细评估生成的轨迹，该目标函数通过对交通状况进行推理来组合行为决策。选择最佳的无碰撞，平滑且动态可行的轨迹，并将其转换为由低级横向和纵向控制器执行的命令。我们的测试自动驾驶汽车已经在现实的市区道路上进行了现场试验。实验结果证明了所提出的轨迹规划框架和算法能够安全地处理各种典型驾驶场景，例如静态和移动物体回避，车道保持和车辆跟随，同时遵守交通规则。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号