RealTime Trajectory Planning for Automated Vehicle Safety and Performance in Dynamic Environments

Abstract

Safe trajectory planning for highperformance automated vehicles in an environment with both static and moving obstacles is a challenging problem. Part of the challenge is developing a formulation that can be solved in realtime while including the following set of specifications: minimum timetogoal, a dynamic vehicle model, minimum control effort, both static and moving obstacle avoidance, simultaneous optimization of speed and steering, and a short execution horizon. This paper presents a nonlinear model predictive controlbased trajectory planning formulation, tailored for a large, highspeed unmanned ground vehicle, that includes the above set of specifications. The ability to solve this formulation in realtime is evaluated using NLOptControl, an opensource, directcollocationbased optimal control problem solver in conjunction with the KNITRO nonlinear programming problem solver. The formulation is tested with various sets of specifications. A parametric study relating execution horizon and obstacle speed indicates that the moving obstacle avoidance specification is not needed for safety when the planner has a small execution horizon and the obstacles are moving slowly. However, a moving obstacle avoidance specification is needed when the obstacles are moving faster, and this specification improves the overall safety without, in most cases, increasing the solvetimes. The results indicate that (i) safe trajectory planners for highperformance automated vehicles should include the entire set of specifications mentioned above, unless a static or lowspeed environment permits a less comprehensive planner and (ii) the resulting formulation can be solved in realtime.