A Bayesian filter for modeling traffic at stop intersections

All-way stop intersections are widely used for traffic management in North America. Therefore, modeling and control of vehicle behavior at stop intersections is fundamental for driver assistance systems and autonomous driving. This paper presents a method to predict the maneuvers performed by vehicles at arbitrary all-way stop intersections, using noisy sensor data. This is required for an autonomous vehicle to decide when to enter the intersection, or for a driver assistance system to decide when to issue a collision warning to the driver. The problem is divided into two components. The first component estimates the maneuver intention of the drivers by means of a naïve Bayesian filter. The second component predicts the order in which the vehicles will enter the intersection by means of a kinematic feedback model. Both algorithms are evaluated using real world data collected with laser sensors mounted on a vehicle. The Bayesian filter is successfully applied to intersections of different sizes and geometries. We show that the filter identifies maneuvers earlier than a deterministic reference model.