Lane-change social behavior generator for autonomous driving car by non-parametric regression in Reproducing Kernel Hilbert Space

Nowadays, self-driving cars are being applied to more complex urban scenarios including intersections, merging ramps or lane changes. It is, therefore, important for self-driving cars to behave socially with human-driven cars. In this paper, we focus on generating the lane change behavior for self-driving cars: perform a safe and effective lane change behavior once a lane-change command is received. Our method bridges the gap between higher-level behavior commands and the trajectory planner. There are two challenges in the task: 1) Analyzing the surrounding vehicles' mutual effects from their trajectories. 2) Estimating the proper lane change start point and end point according to the analysis of surrounding vehicles. We propose a learning-based approach to understand surrounding traffic and make decisions for a safe lane change. Our contributions and advantages of the approach are: 1 Considers the behavior generator as a continuous function in Reproducing Kernel Hilbert Space (RKHS) which contains a family of behavior generators; 2 Constructs the behavior generator function in RKHS by non-parametric regressions on training data; 3 Takes past trajectories of all related surrounding cars as input to capture mutual interactions and output continuous values to represent behaviors. Experimental results show that the proposed approach is able to generate feasible and human-like lane-change behavior (represented by start and end points) in multi-car environments. The experiments also verified that our suggested kernel outperforms the ones which were used in a previous method.