A self-driving vehicle must understand its environment to determine the appropriate action. Traditional autonomy systems rely on object detection to find the agents in the scene. However, object detection assumes a discrete set of objects and loses information about uncertainty, so any errors compound when predicting the future behavior of those agents. Alternatively, dense occupancy grid maps have been utilized to understand free-space. However, predicting a grid for the entire scene is wasteful since only certain spatio-temporal regions are reachable and relevant to the self-driving vehicle. We present a unified, interpretable, and efficient autonomy framework that moves away from cascading modules that first perceive, then predict, and finally plan. Instead, we shift the paradigm to have the planner query occupancy at relevant spatio-temporal points, restricting the computation to those regions of interest. Exploiting this representation, we evaluate candidate trajectories around key factors such as collision avoidance, comfort, and progress for safety and interpretability. Our approach achieves better highway driving quality than the state-of-the-art in high-fidelity closed-loop simulations.

翻译：摘要：自动驾驶车辆必须理解其周围环境以确定合适的行动。传统自主系统依赖目标检测来识别场景中的智能体。然而，目标检测假设存在离散的目标集合，会丢失关于不确定性的信息，因此任何误差在预测这些智能体未来行为时都会被放大。另一种方法利用密集占据栅格地图来理解自由空间。但为整个场景预测栅格地图效率低下，因为只有特定的时空区域是自动驾驶车辆可达且相关的。我们提出了一种统一、可解释且高效的自主框架，该框架摒弃了先感知、后预测、最后规划的级联模块范式。相反，我们转变范式，让规划器在相关的时空点查询占据状态，将计算限制在这些感兴趣区域。利用这一表示，我们围绕碰撞避免、舒适性、进度等关键因素评估候选轨迹，以确保安全性和可解释性。在高保真闭环仿真中，我们的方法实现了比现有技术更优的高速公路驾驶质量。