Optimization problems for trajectory planning in autonomous vehicle racing are characterized by their nonlinearity and nonconvexity. Instead of solving these optimization problems, usually a convex approximation is solved instead to achieve a high update rate. The state of the art convexifies track constraints using sequential linearization (SL), which is a method of relaxing the constraints. Solutions to the relaxed optimization problem are not guaranteed to be feasible in the nonconvex optimization problem.
Sequential Convex Programming Methods for Real-time Optimal Trajectory Planning in Autonomous Vehicle Racing
2024-06-02
828174 byte
Conference paper
Electronic Resource
English
REAL-TIME TRAJECTORY OPTIMIZATION FOR AUTONOMOUS VEHICLE RACING USING SEQUENTIAL LINEARIZATION
British Library Conference Proceedings | 2018
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