Cooperative Cruising: Reinforcement Learning-Based Time-Headway Control for Increased Traffic Efficiency

• URL: http://arxiv.org/abs/2412.02520v3
• Date: Sun, 02 Feb 2025 08:49:21 GMT
• Title: Cooperative Cruising: Reinforcement Learning-Based Time-Headway Control for Increased Traffic Efficiency
• Authors: Yaron Veksler, Sharon Hornstein, Han Wang, Maria Laura Delle Monache, Daniel Urieli,
• Abstract summary: This paper proposes a novel AI system that is the first to improve highway traffic efficiency compared with human-like traffic. At the core of our approach is a reinforcement learning based controller that communicates time-headways to automated vehicles.
• Score: 4.982603129041808
• License:
• Abstract: The proliferation of connected automated vehicles represents an unprecedented opportunity for improving driving efficiency and alleviating traffic congestion. However, existing research fails to address realistic multi-lane highway scenarios without assuming connectivity, perception, and control capabilities that are typically unavailable in current vehicles. This paper proposes a novel AI system that is the first to improve highway traffic efficiency compared with human-like traffic in realistic, simulated multi-lane scenarios, while relying on existing connectivity, perception, and control capabilities. At the core of our approach is a reinforcement learning based controller that dynamically communicates time-headways to automated vehicles near bottlenecks based on real-time traffic conditions. These desired time-headways are then used by adaptive cruise control (ACC) systems to adjust their following distance. By (i) integrating existing traffic estimation technology and low-bandwidth vehicle-to-infrastructure connectivity, (ii) leveraging safety-certified ACC systems, and (iii) targeting localized bottleneck challenges that can be addressed independently in different locations, we propose a potentially practical, safe, and scalable system that can positively impact numerous road users.

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