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.

自动驾驶汽车通过统一的、可解释的、高效的自主性框架，在关键因素（如碰撞避免、舒适性和进展）方面评估候选路径，从而实现了优于高保真闭环模拟中最先进水平的高速公路驾驶质量。