  In transportation networks, where traffic lights have traditionally been used
for vehicle coordination, intersections act as natural bottlenecks. A
formidable challenge for existing automated intersections lies in detecting and
reasoning about uncertainty from the operating environment and human-driven
vehicles. In this paper, we propose a risk-aware intelligent intersection
system for autonomous vehicles (AVs) as well as human-driven vehicles (HVs). We
cast the problem as a novel class of Multi-agent Chance-Constrained Stochastic
Shortest Path (MCC-SSP) problems and devise an exact Integer Linear Programming
(ILP) formulation that is scalable in the number of agents' interaction points
(e.g., potential collision points at the intersection). In particular, when the
number of agents within an interaction point is small, which is often the case
in intersections, the ILP has a polynomial number of variables and constraints.
To further improve the running time performance, we show that the collision
risk computation can be performed offline. Additionally, a trajectory
optimization workflow is provided to generate risk-aware trajectories for any
given intersection. The proposed framework is implemented in CARLA simulator
and evaluated under a fully autonomous intersection with AVs only as well as in
a hybrid setup with a signalized intersection for HVs and an intelligent scheme
for AVs. As verified via simulations, the featured approach improves
intersection's efficiency by up to $200\%$ while also conforming to the
specified tunable risk threshold.
