Velocity-Based Driver Intent Inference at Urban Intersections in the Presence of Preceding Vehicles

Predicting turn and stop maneuvers of potentially errant drivers is a basic requirement for advanced driver assistance systems for urban intersections. Previous work has shown that an early estimate of the driver´s intent can be inferred by evaluating the vehicle´s speed during the intersection approach. In the presence of a preceding vehicle, however, the velocity profile might be dictated by car-following behavior rather than by the need to slow down before doing a left or right turn. To infer the driver´s intent under such circumstances, a simple, real-time capable approach using a parametric model to represent both car-following and turning behavior is proposed. The performance of two alternative parameterizations based on observations at an individual intersection and a generic curvature-based model is evaluated in combination with two different Bayes net classification algorithms. In addition, the driver model is shown to be capable of predicting the future trajectory of the vehicle.