One of the critical pieces of the self-driving puzzle is understanding the surroundings of a self-driving vehicle (SDV) and predicting how these surroundings will change in the near future. To address this task we propose MultiXNet, an end-to-end approach for detection and motion prediction based directly on lidar sensor data. This approach builds on prior work by handling multiple classes of traffic actors, adding a jointly trained second-stage trajectory refinement step, and producing a multimodal probability distribution over future actor motion that includes both multiple discrete traffic behaviors and calibrated continuous position uncertainties. The method was evaluated on large-scale, real-world data collected by a fleet of SDVs in several cities, with the results indicating that it outperforms existing state-of-the-art approaches.
翻译:自我驱动拼图的关键部分之一是了解自驾驶车(SDV)的周围环境,并预测这些周围环境在不久的将来将如何变化。为了完成这项任务,我们提议了MultiXNet,这是直接基于Lidar传感器数据的检测和运动预测的端对端方法。这一方法以先前的工作为基础,处理多类交通人员,增加一个经过联合培训的第二阶段轨迹改进步骤,并在未来的行为者运动中产生多式联运概率分布,其中包括多种离散交通行为和经校准的连续位置不确定性。该方法通过几个城市的SDV车队收集的大规模真实世界数据进行评估,其结果显示该方法优于现有的最新方法。