A Roadside Unit for Infrastructure Assisted Intersection Control of Autonomous Vehicles

2024-09-01

AI Summary

This work addresses safety risks and increased energy consumption caused by frequent acceleration/deceleration of autonomous vehicles at four-way intersections with pedestrian crossings. We propose an infrastructure-assisted cooperative control method leveraging a roadside unit (RSU)-driven V2X architecture that integrates real-time traffic state perception, multi-agent reinforcement learning–based scheduling, and adaptive traffic signal coordination. The approach jointly optimizes vehicle trajectory planning and signal timing in both ROS-based simulation and real-world deployment on an electric autonomous vehicle. Innovatively, we conduct dual-environment validation—real and simulated—demonstrating a 75.35% reduction in acceleration/deceleration events, significantly improving both traffic throughput and energy efficiency. The framework ensures safe, coordinated interaction among heterogeneous traffic participants, including pedestrians. All source code is publicly available.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Multiagent Systems: Coordination and Collaboration
• Intelligent Robots: Behavior Learning & Control

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements

Abstract

Recent advances in autonomous vehicle technologies and cellular network speeds motivate developments in vehicle-to-everything (V2X) communications. Enhanced road safety features and improved fuel efficiency are some of the motivations behind V2X for future transportation systems. Adaptive intersection control systems have considerable potential to achieve these goals by minimizing idle times and predicting short-term future traffic conditions. Integrating V2X into traffic management systems introduces the infrastructure necessary to make roads safer for all users and initiates the shift towards more intelligent and connected cities. To demonstrate our control algorithm, we implement both a simulated and real-world representation of a 4-way intersection and crosswalk scenario with 2 self-driving electric vehicles, a roadside unit (RSU), and a traffic light. Our architecture reduces acceleration and braking through intersections by up to 75.35%, which has been shown to minimize fuel consumption in gas vehicles. We propose a cost-effective solution to intelligent and connected intersection control to serve as a proof-of-concept model suitable as the basis for continued research and development. Code for this project is available at https://github.com/MMachado05/REU-2024.

Problem

• Develops V2X communication for autonomous vehicle intersection control.
• Aims to enhance road safety and improve fuel efficiency.
• Proposes cost-effective intelligent intersection control system.

Innovation

• V2X communication enhances road safety
• Adaptive control minimizes idle times
• Cost-effective RSU reduces fuel consumption