Abstract:
Trajectory planning is challenging for autonomous cars since they operate in unpredictable environments with limited sensor horizons. To incorporate new information as it...Show MoreMetadata
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.
Published in: 2019 American Control Conference (ACC)
Date of Conference: 10-12 July 2019
Date Added to IEEE Xplore: 29 August 2019
ISBN Information: