Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function

• Published in: Applied sciences Vol. 15; no. 5; p. 2315
• Main Authors: Song, Jie, Svinin, Mikhail, Wakamiya, Naoki
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2025
• Subjects: Accident prevention; adaptive control; adaptive control barrier function (aCBF); Algorithms; Autonomous vehicles; Control algorithms; control barrier function (CBF); Controllers; Decision making; Fuzzy logic; Methods; multi-agent systems (MASs); Neural networks; Optimization; Parameter estimation; Robotics industry; Sensors; Traffic accidents & safety
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app15052315

Autonomous intersection management (AIM) is gaining increasing attention due to its crucial role in ensuring safety and efficiency. Various methods have been proposed in the literature to address the AIM problem, including traffic light optimization, connected vehicle optimization based on vehicle-to-anything (V2X) communication technology, and multi-agent autonomous systems. However, each of these approaches has its own limitations, such as parking delays, communication latency, or the lack of guaranteed collision avoidance. This paper presents a novel approach to AIM using adaptive control barrier functions (aCBFs). The proposed aCBF first estimates the power transmission efficiency and incorporates it into the CBF design to ensure collision-free operation. Compared to existing methods, the aCBF approach offers several advantages. Firstly, it eliminates parking delays caused by traffic light systems. Secondly, it can be deployed in intersections with limited network coverage, unlike IoT-based solutions that rely heavily on connectivity. Thirdly, it ensures guaranteed collision-free agent movement at intersections. Specifically, our method guarantees that the minimum distance between agents remains no less than the safe distance at all times, significantly enhancing safety. Furthermore, compared to the TriPField algorithm, our approach achieves a 95% success rate in collision avoidance, demonstrating reliability in autonomous intersection management. The effectiveness of the proposed aCBF-based AIM algorithm has been validated through simulations and experiments with multiple autonomous agent-like robots.