Exploiting Beneficial Information Sharing Among Autonomous Vehicles

Abstract

As communication technologies develop, an autonomous vehicle will receive information not only from its own sensing system but also from infrastructures and other vehicles through communication. This paper discusses how to exploit a sequence of future information that is shared among autonomous vehicles, including the planned positions, the velocities and the lane numbers. A hybrid system model is constructed, and a control policy is designed to utilize shared sequence information for making navigation decisions. For the high-level discrete state transitions, the shared information is used to determine when to change lane, if lane changing will bring reward for the autonomous vehicle and there exists a feasible continuous state controller. For the low-level continuous state space controller generation, the shared information can relax the safety interval constraints in the existing model predictive control method. In the system level, the information sharing can increase the traffic flow and improve driving comfort. We demonstrate the advantages of information sharing in control and navigation in simulation.

Publication
In *2019 IEEE 58th Conference on Decision and Control *
Songyang Han
Songyang Han
PhD student | Computer Science and Engineering | Artificial Intelligence

My research interests include multi-agent reinforcement learning, connected autonomous vehicles.