A Semi-Distributed Interior Point Algorithm for Optimal Coordination of Automated Vehicles at Intersections

TL;DR

This paper introduces a semi-distributed interior point algorithm for optimal vehicle coordination at intersections, achieving near-centralized performance with significantly reduced communication.

Contribution

It presents a novel semi-distributed primal-dual interior-point method that splits linear algebra steps, enabling parallel computation similar to centralized algorithms.

Findings

Reduces data exchange by up to 99% with less than 1% suboptimality.

Demonstrates effectiveness in realistic intersection scenarios.

Maintains centralized algorithm steps in a distributed framework.

Abstract

In this paper, we consider the optimal coordination of automated vehicles at intersections under fixed crossing orders. We formulate the problem using direct optimal control and exploit the structure to construct a semi-distributed primal-dual interior-point algorithm to solve it by parallelizing most of the computations. Differently from standard distributed optimization algorithms, where the optimization problem is split, in our approach we split the linear algebra steps, such that the algorithm takes the same steps as a fully centralized one, while still performing computations in a distributed fashion. We analyze the communication requirements of the algorithm, and propose an approximation scheme which can significantly reduce the data exchange. We demonstrate the effectiveness of the algorithm in hard but realistic scenarios, which show that the approximation leads to reductions in communicated data of almost 99\% of the exact formulation, at the expense of less than 1\% suboptimality.