Model Predictive Control for Autonomous Driving Based on Time Scaled Collision Cone

In this paper, we present a Model Predictive Control (MPC) framework based on path velocity decomposition paradigm for autonomous driving. The optimization underlying the MPC has a two layer structure wherein first, an appropriate path is computed for the vehicle followed by the computation of optimal forward velocity along it. The very nature of the proposed path velocity decomposition allows for seamless compatibility between the two layers of the optimization. A key feature of the proposed work is that it offloads most of the responsibility of collision avoidance to velocity optimization layer for which computationally efficient formulations can be derived. In particular, we extend our previously developed concept of time scaled collision cone (TSCC) constraints and formulate the forward velocity optimization layer as a convex quadratic programming problem. We perform validation on autonomous driving scenarios wherein proposed MPC repeatedly solves both the optimization layers in receding horizon manner to compute lane change, overtaking and merging maneuvers among multiple dynamic obstacles.Comment: 6 page

ROBOTIC MOTION PLANNING USING CONVEX OPTIMIZATION METHODS

Collision avoidance techniques tend to derive the robot away of the obstacles in minimal total travel distance. Most ofthe collision avoidance algorithms have trouble get stuck in a local minimum. A new technique is to avoid local minimum in convexoptimization-based path planning. Obstacle avoidance problem is considered as a convex optimization problem under system state andcontrol constraints. The idea is by considering the obstacles as a convex set of points which represents the obstacle that encloses inminimum volume ellipsoid, also the addition of the necessary offset distance and the modified motion path is presented. In the analysis,the results demonstrated the effectiveness of the suggested motion planning by using the convex optimization technique