Interactive risky behavior model for 3-car overtaking scenario using joint bayesian network

Abstract

In this paper, we propose a new model for 3-car interactive risky behavior of vehicles travelling in front and behind of a driver (overtaken) car. Following distance of vehicles moving in front and at rear end of the car in question plays an important role for overtaking scenario. Moreover, the distance between the car in front and the vehicle following it should be sufficiently long for preventing collision if overtaking is inevitable for the motorist behind the middle subject vehicle. Here, we consider the roles of the vehicles involved in such a scenario. We observe the behaviors of moving vehicles in front and the rear end of the subject car. To this end, front and rear car images are acquired by two cameras and subjected to vertical and horizontal optical flow edge map creation. In classification stage of the optical flow edge map clusters, a motion vector histogram thresholding method is utilized in conjunction with a decision assessment strategy based on the joint Bayesian belief network statistical model. In turn, not only the trajectories of the cars are captured but also joint behavior of three cars over-taken scenario is estimated using the proposed interactive risk model.