Abstract
This paper presents a hierarchical motion planner for autonomous racing. The long-term motion planner functions offline and formulates the optimal motion plan for the entire race track. The short-term collision avoidance planner functions online and formulates a motion plan for a limited horizon ahead of the autonomous car when an obstacle is detected in the path of the vehicle. The motion planners formulate the planning problems as optimal control problems and solve the resulting optimizations using an interior point optimizer (IPOPT). Simulation experiments show that an autonomous vehicle using the motion planner is able to race around the track with minimum lap time while avoiding unexpected obstacles.
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