Coordination of Autonomous Vehicles using a Mixed-Integer LPV-MPC Planner

This work addresses the problem of coordinating multiple autonomous vehicles. In particular, an optimal planner based on Model Predictive Control (MPC) is designed for each vehicle, using the linear parameter-varying (LPV) representation of each vehicle model to provide feasible references that ensure constraint satisfaction. Meanwhile, mixed integer linear inequalities are embedded in the optimization problem to ensure collision avoidance. The proposed approach is evaluated in an aggressive driving scenario using a 1/10 scale electric car.

I. Introduction

Autonomous driving is a promising solution for current transportation systems that would provide sustainable traffic flow, efficient mobility, pollution reduction, etc. However, due to the interaction of multiple vehicles, there are several engineering challenges, such as vehicle-to-vehicle communication and critical decision making. In this latter, the motion planning algorithm is the key point. Indeed, it is responsible for providing each vehicle with a safe and collision-free trajectory to its final destination, taking into account the vehicle dynamics and maneuvering capabilities in the presence of obstacles, along with adherence to traffic rules and road boundaries [1]. The existing motion planning algorithms in the literature are based on graph search, sampling, interpolation curves, optimization, and learning. Optimization-based algorithms, especially those using MPC, have been successfully used to coordinate multiple vehicles due to the advantage of handling hard-constrained dynamics, in addition to posing motion planning as a multi-objective problem [2], [3].