Guaranteed Safe Autonomous Driving
Real-Time, Provably Safe Trajectory Synthesis for Autonomous Vehicles in Unforeseen Environments
Shreyas Kousik, Sean Vaskov, Fan Bu, Matthew Johnson-Roberson, Ram Vasudevan
Trajectory planning for autonomous vehicles requires a mathematical model to describe how the vehicle moves through the world. However, models are imperfect, and accounting for model uncertainty is critical to ensuring safety. Furthermore, depending on model complexity, a trajectory planner may or may not be able to find solutions in real time. The proposed work uses low-complexity models to produce trajectories, and bounds the model error of the vehicle's ability to follow such trajectories. The range of states a vehicle can achieve in this framework is computed offline in a Forward Reachable Set (FRS), which is represented as a function that conservatively approximates the vehicle's states (in 2-D space) and its parameterized trajectories. The FRS is intersected with obstacles in the world at runtime to exclude unsafe trajectories; optimization over the remaining trajectories ensures that a trajectory is chosen that is safe for the vehicle to follow despite uncertainty. This method is demonstrated in simulated comparison against the Rapidly-exploring Random Trees (RRT) and Nonlinear Model Predictive Control (NMPC) approaches; and on a Segway RMP mobile robot and a Rover carlike robot.