Traffic interactions: Estimate driving behavior's influence

In this paper, we propose a novel approach to deal influence of own vehicle behavior to driving scene which called “interactions” with surrounding traffic participants. Recently, various advanced driver-assistance systems (ADAS) have been proposed. In these ADAS, however, it is not sufficiently considering the influence of own vehicle behavior. With a novel driver assistance system based on the traffic interactions, each vehicle keeps not only own vehicle but also surrounding space in safety and comfortable, such as, Lane Change Assist for reducing traffic jam. We estimate the interactions from the behavior data of the traffic participants using Bayesian filtering techniques. Efficiency of the novel driving support with the interactions is evaluated in simple traffic simulations. In the simulated experiments, our approach improves traffic flow 140% smoother than without the driving support. Constructions of more detail traffic interaction models and demonstrations of effectiveness using real-vehicles are important feature works. It is also important that the development of the specific ADAS application based on traffic interaction.