Full-Dimensional Collision Avoidance of Autonomous Vehicles Using Sequence Convex Programming

A vehicle trajectory planning strategy for autonomous collision avoidance is proposed in this paper. First, a non-convex optimization model for vehicle trajectory planning is established. Using strong duality of convex optimization, the Lagrange dual transformation is performed, and the nondifferentiable collision avoidance constraints are transformed into smooth, differentiable constraints. Then, by using variable substitution and convex approximation, the nonlinear optimization problem is transformed into a convex optimization problem. After the discretization and relaxtion of the convex optimization subproblem, the sequential convex optimization (SCP) method is used to solve the problem. Finally, the effectiveness of this method is verified by numerical simulation. The results show that the SCP improves solution efficiency of the optimization problem under the premise of achieving similar performance. The algorithm shows a satisfying performance on the scenes of parallel parking and reverse parking compared with traditional approach.