Use of contextual information by Bayesian Networks for multi-object tracking in scanning laser range data

This paper presents a new method to improve the perception of the environment of a vehicle. The aim here is to detect the vehicles using a scanning laser range finder and track the detected objects at each time. This contribution can be considered as an element of a global vehicle-to-vehicle (v2v) surveillance system where the on-board system receives warnings from the other local systems. It allows to extend the effective surveillance field and as a consequence to provide a faster reaction of the vehicle (collision avoidance or mitigation). To deal with this multi-target tracking problem, we focus on the Joint Probabilistic Data Association (JPDA) methods. Their particularity lies in their ability to take into account the probabilistic characteristics of the detector (detection and false alarm probabilities). Whereas in many works the detection probability is set up once, our contribution is to propose a method that dynamically estimates for each object the detection probability using the contextual information modeled by a Bayesian Network (BN-JPDAF).