Prediction of unintentional lane departure using evidence theory

The scope of this paper is the development of algorithms for driving support systems for safe lane changing maneuvers (lane change assist system) and safe maintenance of the vehicle's path (lane/road departure warning system). The proposed algorithm can predict unintentional lane changes before they are performed by the driver using information from multiple sources. The prediction of unintentional lane changes comes from data produced by various sensors installed on the vehicle (inertial sensors, radar and camera). The decision fusion algorithm is based on Dempster-Shafer theory. The paper analyzes vehicle kinematics, extracts distributed decision components from sensor data and investigates several set of information sources and their mass functions to be utilized in the decision fusion system. Finally, uncertainties of each source are modeled and included in the Dempster-Shafer's theory as weights calculated adaptively and in real-time using heuristics and a-priori knowledge. The algorithms are validated in real world scenarios.