Human-Like Trajectory Planning for Autonomous Vehicles Based on Spatiotemporal Geometric Transformation

Abstract

Human-driven vehicles and different levels of autonomous vehicles are expected to coexist on roads in the future. However, autonomous systems behave differently from their human-driver counterparts, these two behaviors are incompatible with one another, thereby negatively impacting traffic efficiency and safety. Herein, we present the construction of human-like trajectories for use in autonomous vehicles as a possible solution to this issue. We present a trajectory planning method based on the spatiotemporal geometric transformation of driving scenarios to generate human-like trajectories. Speed and safety redundancy data were collected through driving tests to understand human driving behaviors. Self-driving scenarios were abstracted as a Lorentz coordinate system under a three-dimensional Minkowski space-time. A surrounding-manifold tensor equation was established using differential geometry theory to depict the relationship between the trajectory constraints and the geometric spatiotemporal background. A metric tensor field can be solved from the equation to construct the corresponding “volcano space-time,” which is a three-dimensional general Riemannian space for placing the subject vehicle and the surroundings. The geodesics of the volcano space-time are solved using the geodesic equation and are projected back into the three-dimensional Minkowski space-time. Geodesic trajectories were fitted as Bézier curves in this study and corrected according to the vehicle dynamics constraints for trackability. In simulation and real vehicle tests, trajectories generated using the proposed algorithm exhibited collision avoidance and trackability, and the algorithm offered behaviors that were similar to those of human drivers under the same scenarios.