In this paper, we propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios that include traffic-light handling, yielding, and interactions with multiple road-users. Towards this goal, we design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations in the form of 3D detections and their future trajectories, as well as a cost volume defining the goodness of each position that the self-driving car can take within the planning horizon. We then sample a set of diverse physically possible trajectories and choose the one with the minimum learned cost. Importantly, our cost volume is able to naturally capture multi-modality. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America. Our experiments show that the learned cost volume can generate safer planning than all the baselines.

翻译：在本文中,我们提出一个神经运动规划师(NMP),用于学习在复杂的城市情景中自主驾驶,包括交通灯处理、产出和与多道路用户的互动。为了实现这一目标,我们设计了一个整体模型,作为输入原始LIDAR数据和一张HD地图,并以3D探测及其未来轨迹的形式,制作一个可解释的中间图,以及一个成本量,确定自驾驶汽车在规划视野中可以占据的每个位置的好坏。然后我们抽样一组不同的物理轨迹,并以最低的学习成本来选择一个。重要的是,我们的成本量能够自然地捕捉到多模式性。我们展示了我们在北美几个城市所捕捉到的现实世界驱动数据的方法的有效性。我们的实验表明,所学的成本量能够产生比所有基线都更安全的规划。