Naturalistic Lane Change Analysis for Human-Like Trajectory Generation

Human-like driving is of great significance for safety and comfort of autonomous vehicles, but existing trajectory planning methods for on-road vehicles rarely take the similarity with human behavior into consideration. From a representative trajectory-generation-based planning algorithm, this paper analyzes the systematic deviation of the generated trajectories from human trajectories, and proposes a new scheme of trajectory generation by compensating the deviation using a deviation profile learned from data. Experimental results show that the proposed trajectory generator is able to fit the human trajectories considerably better than the original one with only one additional degree of freedom. When used for online trajectory planning, with the same level of computational complexity, the proposed generator is able to generate trajectories that are more human-like than original generator does, which provides basis for autonomous vehicle to perform human-like trajectory planning.