Our goal is to develop an autonomous vehicle control strategy that adapts on-line to varying levels of congestion in the environment.
As autonomous vehicles populate the road, they need to interact with other autonomous and human drivers. In urban environments, these vehicles will need to navigate dense traffic situations, thus creating a need for planning in congestion. Our approach creates a dynamic control law that scales with the varying traffic density. We generate a cost function for the vehicle, which incorporates the density, occupancy, and risk level within the environment. By choosing vehicles actions only below a certain cost value, we demonstrate the vehicle remains safe while avoiding the common “freezing robot” problem.