Guaranteed Safe Reachability-based Trajectory Design for a High-Fidelity Model of an Autonomous Passenger Vehicle

Abstract

Trajectory planning is challenging for autonomous cars since they operate in unpredictable environments with limited sensor horizons. To incorporate new information as it is sensed, planning is done in a loop, with the next plan being computed as the previous plan is executed. Reachability-based Trajectory Design (RTD) is a recent, provably safe, realtime algorithm for trajectory planning. RTD consists of an offline Forward Reachable Set (FRS) computation of the vehicle tracking parameterized trajectories; and online trajectory optimization using the FRS to map obstacles to constraints in a provably-safe way. In the literature, RTD has only been applied to small mobile robots. The contribution of this work is RTD on a passenger vehicle in CarSim, with a full powertrain model, chassis and tire dynamics. RTD operates the vehicle safely at up to 15 m/s on a two-lane road around randomly-placed obstacles only known to the vehicle when detected within its sensor horizon. RTD is compared with a Nonlinear Model-Predictive Control (NMPC) and a Rapidly-exploring Random Tree (RRT) approach. The experiment demonstrates RTD's ability to plan safe trajectories in real time, in contrast to the existing state-of-the-art approaches.