论文标题
在动态城市环境中自动驾驶的知情采样轨迹规划师
Informed sampling-based trajectory planner for automated driving in dynamic urban environments
论文作者
论文摘要
城市环境是自动驾驶汽车最困难的领域之一。在各种动态的交通参与者（例如车辆，骑自行车的人和行人）以及各种环境条件下，车辆必须能够在具有挑战性的道路布局上计划安全的路线。挑战仍然是，运动计划者在计算上很快，并且会主动考虑其他道路使用者的未来运动。本文介绍了一个基于计算高效的采样轨迹规划师，可在城市环境中进行安全舒适的驾驶。策划者通过基于道路布局信息将初始探索分支添加到搜索树并重申先前的解决方案，从而改善了稳定的SPARSE-RRT算法。此外，轨迹规划师会说明其他交通参与者的预测动议，以便在城市交通中安全驾驶。模拟研究表明，该计划者能够在几个城市交通情况下计划无碰撞，舒适的轨迹。添加基于领域的探索分支，提高了对非常有趣的领域的探索效率，从而提高了整体计划绩效。
The urban environment is amongst the most difficult domains for autonomous vehicles. The vehicle must be able to plan a safe route on challenging road layouts, in the presence of various dynamic traffic participants such as vehicles, cyclists and pedestrians and in various environmental conditions. The challenge remains to have motion planners that are computationally fast and that account for future movements of other road users proactively. This paper describes an computationally efficient sampling-based trajectory planner for safe and comfortable driving in urban environments. The planner improves the Stable-Sparse-RRT algorithm by adding initial exploration branches to the search tree based on road layout information and reiterating the previous solution. Furthermore, the trajectory planner accounts for the predicted motion of other traffic participants to allow for safe driving in urban traffic. Simulation studies show that the planner is capable of planning collision-free, comfortable trajectories in several urban traffic scenarios. Adding the domain-knowledge-based exploration branches increases the efficiency of exploration of highly interesting areas, thereby increasing the overall planning performance.
