Optimal cooperative motion planning for vehicles at intersections

TL;DR

This paper introduces a mathematical framework for cooperative intersection management that combines continuous optimization and scheduling to achieve collision-free, deadlock-free, and near-optimal vehicle coordination.

Contribution

It proposes a novel decomposition approach linking vehicle priority with scheduling, leading to a constructive locally optimal algorithm for intersection motion planning.

Findings

Framework effectively balances collision avoidance and optimality.

Algorithm demonstrates computational efficiency in complex scenarios.

Connects vehicle priority with scheduling in a new formal way.

Abstract

We consider the problem of cooperative intersection management. It arises in automated transportation systems for people or goods but also in multi-robots environment. Therefore many solutions have been proposed to avoid collisions. The main problem is to determine collision-free but also deadlock-free and optimal algorithms. Even with a simple definition of optimality, finding a global optimum is a problem of high complexity, especially for open systems involving a large and varying number of vehicles. This paper advocates the use of a mathematical framework based on a decomposition of the problem into a continuous optimization part and a scheduling problem. The paper emphasizes connections between the usual notion of vehicle priority and an abstract formulation of the scheduling problem in the coordination space. A constructive locally optimal algorithm is proposed. More generally, this work opens up for new computationally efficient cooperative motion planning algorithms.