  Navigating safely in urban environments remains a challenging problem for
autonomous vehicles. Occlusion and limited sensor range can pose significant
challenges to safely navigate among pedestrians and other vehicles in the
environment. Enabling vehicles to quantify the risk posed by unseen regions
allows them to anticipate future possibilities, resulting in increased safety
and ride comfort. This paper proposes an algorithm that takes advantage of the
known road layouts to forecast, quantify, and aggregate risk associated with
occlusions and limited sensor range. This allows us to make predictions of risk
induced by unobserved vehicles even in heavily occluded urban environments. The
risk can then be used either by a low-level planning algorithm to generate
better trajectories, or by a high-level one to plan a better route. The
proposed algorithm is evaluated on intersection layouts from real-world map
data with up to five other vehicles in the scene, and verified to reduce
collision rates by 4.8x comparing to a baseline method while improving driving
comfort.
