QuAD: Query-based Interpretable Neural Motion Planning for Autonomous
  Driving
Sourav Biswas Sergio Casas Quinlan Sykora Ben Agro Abbas Sadat Raquel Urtasun
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.