Quadratic approximation based heuristic for optimization-based coordination of automated vehicles in confined areas

TL;DR

This paper presents a heuristic approach combining MIQP and NLP to efficiently coordinate multiple automated vehicles in confined areas, avoiding collisions in complex scenarios.

Contribution

Introduces a novel two-stage heuristic method for approximate solutions to complex AV coordination problems involving mixed integer and nonlinear programming.

Findings

The heuristic effectively coordinates AVs in simulated complex scenarios.

The method reduces computational complexity compared to exact solutions.

Simulation results demonstrate practical applicability and efficiency.

Abstract

We investigate the problem of coordinating multiple automated vehicles (AVs) in confined areas. This problem can be formulated as an optimal control problem (OCP) where the motion of the AVs is optimized such that collisions are avoided in cross-intersections, merge crossings, and narrow roads. The problem is combinatorial and solving it to optimality is prohibitively difficult for all but trivial instances. For this reason, we propose a heuristic method to obtain approximate solutions. The heuristic comprises two stages: In the first stage, a Mixed Integer Quadratic Program (MIQP), similar in construction to the Quadratic Programming (QP) sub-problems in Sequential Quadratic Programming (SQP), is solved for the combinatorial part of the solution. In the second stage, the combinatorial part of the solution is held fixed, and the optimal state and control trajectories for the vehicles are obtained by solving a Nonlinear Program (NLP). The performance of the algorithm is demonstrated by a simulation of a non-trivial problem instance.