Exact inference and learning in hybrid Bayesian Networks for lane change intention classification

Abstract

Determining the current intentions of other drivers is essential for correctly predicting or simulating their future actions. Especially unpredicted lane changes can result in very uncomfortable or even dangerous braking maneuvers for succeeding vehicles. Bayesian Networks (BN) allow for a physically motivated probabilistic representation of features influencing driver intentions. While features often take continuous values, e.g. velocity and distance, maneuver intentions are discrete, which results in hybrid BN. For efficient and exact inference, we implement an approach for hybrid nets into the original Bayes Net Toolbox. Furthermore, we extend the approach with a learning component to train a BN with simulated traffic data. Finally, we compare the classification performance for lane changes with a Deep Neural Network (DNN) classifier.