  A significant barrier to deploying autonomous vehicles (AVs) on a massive
scale is safety assurance. Several technical challenges arise due to the
uncertain environment in which AVs operate such as road and weather conditions,
errors in perception and sensory data, and also model inaccuracy. In this
paper, we propose a system architecture for risk-aware AVs capable of reasoning
about uncertainty and deliberately bounding the risk of collision below a given
threshold. We discuss key challenges in the area, highlight recent research
developments, and propose future research directions in three subsystems.
First, a perception subsystem that detects objects within a scene while
quantifying the uncertainty that arises from different sensing and
communication modalities. Second, an intention recognition subsystem that
predicts the driving-style and the intention of agent vehicles (and
pedestrians). Third, a planning subsystem that takes into account the
uncertainty, from perception and intention recognition subsystems, and
propagates all the way to control policies that explicitly bound the risk of
collision. We believe that such a white-box approach is crucial for future
adoption of AVs on a large scale.
