Nonlinear Model Predictive Control for Distributed Motion Planning in Road Intersections Using PANOC

TL;DR

This paper presents a distributed nonlinear model predictive control approach for coordinated vehicle motion in intersections, utilizing PANOC for real-time nonconvex problem solving, enhancing safety and efficiency.

Contribution

It introduces a novel distributed control scheme with conditional constraints and employs PANOC for fast real-time solutions in intersection scenarios.

Findings

Efficient real-time solution of nonconvex control problems using PANOC.

Successful demonstration in realistic intersection crossing scenarios.

Improved coordination and safety in automated vehicle intersection management.

Abstract

The coordination of highly automated vehicles (or agents) in road intersections is an inherently nonconvex and challenging problem. In this paper, we propose a distributed motion planning scheme under reasonable vehicle-to-vehicle communication requirements. Each agent solves a nonlinear model predictive control problem in real time and transmits its planned trajectory to other agents, which may have conflicting objectives. The problem formulation is augmented with conditional constraints that enable the agents to decide whether to wait at a stopping line, if safe crossing is not possible. The involved nonconvex problems are solved very efficiently using the proximal averaged Newton method for optimal control (PANOC). We demonstrate the efficiency of the proposed approach in a realistic intersection crossing scenario.