773 research outputs found
Large network multi-level control for CAV and Smart Infrastructure: AI-based Fog-Cloud collaboration
SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control
Traffic signal control is safety-critical for our daily life. Roughly
one-quarter of road accidents in the U.S. happen at intersections due to
problematic signal timing, urging the development of safety-oriented
intersection control. However, existing studies on adaptive traffic signal
control using reinforcement learning technologies have focused mainly on
minimizing traffic delay but neglecting the potential exposure to unsafe
conditions. We, for the first time, incorporate road safety standards as
enforcement to ensure the safety of existing reinforcement learning methods,
aiming toward operating intersections with zero collisions. We have proposed a
safety-enhanced residual reinforcement learning method (SafeLight) and employed
multiple optimization techniques, such as multi-objective loss function and
reward shaping for better knowledge integration. Extensive experiments are
conducted using both synthetic and real-world benchmark datasets. Results show
that our method can significantly reduce collisions while increasing traffic
mobility.Comment: Accepted by AAAI 2023, appendix included. 9 pages + 5 pages appendix,
12 figures, in Proceedings of the Thirty-Seventh AAAI Conference on
Artificial Intelligence (AAAI'23), Feb 202
Intersection control with connected and automated vehicles: a review
Purpose: This paper aims to review the studies on intersection control with connected and automated vehicles (CAVs). Design/methodology/approach: The most seminal and recent research in this area is reviewed. This study specifically focuses on two categories: CAV trajectory planning and joint intersection and CAV control. Findings: It is found that there is a lack of widely recognized benchmarks in this area, which hinders the validation and demonstration of new studies. Originality/value: In this review, the authors focus on the methodological approaches taken to empower intersection control with CAVs. The authors hope the present review could shed light on the state-of-the-art methods, research gaps and future research directions
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