Vehicle Trajectory Prediction at Intersections using Interaction based Generative Adversarial Networks

Vehicle trajectory prediction at intersections is both essential and challenging for autonomous vehicle navigation. This problem is aggravated when the traffic is predominantly composed of smaller vehicles that frequently disobey lane behavior as is the case in many developing countries. Existing macro approaches consider the trajectory prediction problem for lane-based traffic that cannot account when there is a high disparity in vehicle size and driving behavior among different vehicle types. Hence, we propose a vehicle trajectory prediction approach that models the interaction among different types of vehicles with vastly different driving styles. These interactions are encapsulated in the form of a social context embedded in a Generative Adversarial Network (GAN) to predict the trajectory of each vehicle at either a signalized or non-signalized intersection. The GAN model produces the most acceptable future trajectory among many choices that conform to past driving behavior as well as the trajectories of neighboring vehicles. We evaluate the proposed approach on aerial videos of intersections recorded in China where lane discipline is not followed by vehicles. The proposed GAN based approach demonstrates 6.4% relative improvement in predicting trajectories over state-of-the-art.