Abstract
— 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.
