Probability-Based Decision Making for Automated Highway Driving

Abstract

Real-time, rule-based guidance systems for autonomous vehicles on limited-access highways are investigated. The goal of these systems is to plan trajectories that are safe while satisfying the driver's requests based on stochastic information about the vehicle state and the surrounding traffic. A rule-based system is used for high-level planning. Given a stochastic model of the traffic situation driven by current measurements, the probable evolution of traffic and the best trajectory to follow are predicted. Simulation results assess the impact of uncertain knowledge of traffic on the performance of the guidance system, showing that uncertainty can and must be taken into account.