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

IEEE Intelligent Vehicles Symposium, pp. 3144

Abstract

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.

Authors 4

1. Patrick Scheffe Aachen Chair of Embedded Software

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Embedded Software,Germany

2. Theodor Mario Henneken Aachen Chair of Embedded Software

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Embedded Software,Germany

3. Maximilian Kloock Aachen Chair of Embedded Software

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Chair of Embedded Software,Germany

4. Bassam Alrifaee

   Universität der Bundeswehr München

   Affiliation as printed

   University of the Bundeswehr,Department of Aerospace Engineering,Munich,Germany