Driving heavy-duty vehicles, such as buses and tractor-trailer vehicles, is a difficult task in comparison to passenger cars. Most research on motion planning for autonomous vehicles has focused on passenger vehicles, and many unique challenges associated with heavy-duty vehicles remain open. However, recent works have started to tackle the particular difficulties related to on-road motion planning for buses and tractor-trailer vehicles using numerical optimization approaches. In this work, we propose a framework to design an optimization objective to be used in motion planners. Based on geometric derivations, the method finds the optimal trade-off between the conflicting objectives of centering different axles of the vehicle in the lane. For the buses, we consider the front and rear axles trade-off, whereas for articulated vehicles, we consider the tractor and trailer rear axles trade-off. Our results show that the proposed design strategy results in planned paths that considerably improve the behavior of heavy-duty vehicles by keeping the whole vehicle body in the center of the lane.
翻译:与客车相比,驾驶重型车辆(如公共汽车和拖拉机-拖拉机)是一项艰巨的任务,大多数关于自治车辆机动规划的研究都侧重于客车,与重型车辆有关的许多独特挑战仍然开放,然而,最近的工作已经开始解决与使用数字优化方法对公共汽车和拖拉机-拖车进行公路机动规划有关的特殊困难。在这项工作中,我们提出了一个框架,设计一个最佳目标,供机动规划人员使用。根据几何推算,该方法发现将车辆的不同轴集中在车道上的相互冲突的目标之间的最佳平衡。对于公共汽车,我们考虑前轴和后轴交换,而对于清楚的车辆,我们考虑拖拉机和拖车后轴交换。我们的结果显示,拟议的设计战略导致计划道路,通过将整个车辆体留在车道中心,大大改进重型车辆的行为。