Model-based deadlock prevention for traffic planning of autonomous vehicles

Abstract

Volvo Autonomous Solutions are developing a system for planning the routes of fleets of autonomous vehicles. Autonomous control creates several problems that must be solved; among these is the possibility for the policy of said vehicles to end up in deadlock. This thesis proposes new concepts to describe the problem and methods for preventing a vehicle fleet from deadlocking. As the action that led to deadlock might not be recent, the term implicit deadlock was introduced, which is a configuration of vehicle positions from which deadlock is inevitable. The methods developed successfully prevent deadlocks at several pilot and test sites. However, results indicate that time for computing implicit deadlocks grows exponentially in the size of the site and the number of vehicles in the fleet. A neural network model was also trained using data generated from preprocessing of deadlocks to approximate the process and enable deadlock predictions not discovered before.