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.


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    Title :

    Sequential Convex Programming Methods for Real-time Optimal Trajectory Planning in Autonomous Vehicle Racing




    Publication date :

    2024-06-02


    Size :

    828174 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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