CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2201.13143v1
• Date: Mon, 31 Jan 2022 11:40:13 GMT
• Title: CoTV: Cooperative Control for Traffic Light Signals and Connected Autonomous Vehicles using Deep Reinforcement Learning
• Authors: Jiaying Guo and Long Cheng and Shen Wang
• Abstract summary: This paper presents a multi-agent deep reinforcement learning (DRL) system called CoTV. CoTV Cooperatively controls both Traffic light signals and connected autonomous Vehicles (CAV) In the meantime, CoTV can also be easy to deploy by cooperating with only one CAV that is the nearest to the traffic light controller on each incoming road.
• Score: 6.54928728791438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The target of reducing travel time only is insufficient to support the development of future smart transportation systems. To align with the United Nations Sustainable Development Goals (UN-SDG), a further reduction of fuel and emissions, improvements of traffic safety, and the ease of infrastructure deployment and maintenance should also be considered. Different from existing work focusing on the optimization of the control in either traffic light signal (to improve the intersection throughput), or vehicle speed (to stabilize the traffic), this paper presents a multi-agent deep reinforcement learning (DRL) system called CoTV, which Cooperatively controls both Traffic light signals and connected autonomous Vehicles (CAV). Therefore, our CoTV can well balance the achievement of the reduction of travel time, fuel, and emission. In the meantime, CoTV can also be easy to deploy by cooperating with only one CAV that is the nearest to the traffic light controller on each incoming road. This enables more efficient coordination between traffic light controllers and CAV, thus leading to the convergence of training CoTV under the large-scale multi-agent scenario that is traditionally difficult to converge. We give the detailed system design of CoTV, and demonstrate its effectiveness in a simulation study using SUMO under various grid maps and realistic urban scenarios with mixed-autonomy traffic.

Related papers

• Towards Interactive and Learnable Cooperative Driving Automation: a Large Language Model-Driven Decision-Making Framework [79.088116316919]
  Connected Autonomous Vehicles (CAVs) have begun to open road testing around the world, but their safety and efficiency performance in complex scenarios is still not satisfactory. This paper proposes CoDrivingLLM, an interactive and learnable LLM-driven cooperative driving framework.
  arXiv  Detail & Related papers  (2024-09-19T14:36:00Z)
• A Holistic Framework Towards Vision-based Traffic Signal Control with Microscopic Simulation [53.39174966020085]
  Traffic signal control (TSC) is crucial for reducing traffic congestion that leads to smoother traffic flow, reduced idling time, and mitigated CO2 emissions. In this study, we explore the computer vision approach for TSC that modulates on-road traffic flows through visual observation. We introduce a holistic traffic simulation framework called TrafficDojo towards vision-based TSC and its benchmarking.
  arXiv  Detail & Related papers  (2024-03-11T16:42:29Z)
• Convergence of Communications, Control, and Machine Learning for Secure and Autonomous Vehicle Navigation [78.60496411542549]
  Connected and autonomous vehicles (CAVs) can reduce human errors in traffic accidents, increase road efficiency, and execute various tasks. Reaping these benefits requires CAVs to autonomously navigate to target destinations. This article proposes solutions using the convergence of communication theory, control theory, and machine learning to enable effective and secure CAV navigation.
  arXiv  Detail & Related papers  (2023-07-05T21:38:36Z)
• DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback [109.84667902348498]
  Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network. Most prior TSC methods leverage deep reinforcement learning to search for a control policy. We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
  arXiv  Detail & Related papers  (2023-06-13T05:58:57Z)
• HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control [3.402002554852499]
  We present HumanLight, a novel decentralized adaptive traffic signal control algorithm. Our proposed controller is founded on reinforcement learning with the reward function embedding the transportation-inspired concept of pressure at the person-level. By rewarding HOV commuters with travel time savings for their efforts to merge into a single ride, HumanLight achieves equitable allocation of green times.
  arXiv  Detail & Related papers  (2023-04-05T17:42:30Z)
• COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles [54.61668577827041]
  We introduce COOPERNAUT, an end-to-end learning model that uses cross-vehicle perception for vision-based cooperative driving. Our experiments on AutoCastSim suggest that our cooperative perception driving models lead to a 40% improvement in average success rate.
  arXiv  Detail & Related papers  (2022-05-04T17:55:12Z)
• Real-time Cooperative Vehicle Coordination at Unsignalized Road Intersections [7.860567520771493]
  Cooperative coordination at unsignalized road intersections aims to improve the safety driving traffic throughput for connected and automated vehicles. We introduce a model-free Markov Decision Process (MDP) and tackle it by a Twin Delayed Deep Deterministic Policy (TD3)-based strategy in the deep reinforcement learning framework. We show that the proposed strategy could achieve near-optimal performance in sub-static coordination scenarios and significantly improve control in the realistic continuous flow.
  arXiv  Detail & Related papers  (2022-05-03T02:56:02Z)
• Learning to Help Emergency Vehicles Arrive Faster: A Cooperative Vehicle-Road Scheduling Approach [24.505687255063986]
  Vehicle-centric scheduling approaches recommend optimal paths for emergency vehicles. Road-centric scheduling approaches aim to improve the traffic condition and assign a higher priority for EVs to pass an intersection. We propose LEVID, a cooperative VehIcle-roaD scheduling approach including a real-time route planning module and a collaborative traffic signal control module.
  arXiv  Detail & Related papers  (2022-02-20T10:25:15Z)
• A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways [0.0]
  Connected and automated vehicles (CAVs) have the potential to address congestion, accidents, energy consumption, and greenhouse gas emissions. We propose a framework for coordinating CAVs such that stop-and-go driving is eliminated. We demonstrate the coordination of CAVs through numerical simulations and show that a smooth traffic flow is achieved by eliminating stop-and-go driving.
  arXiv  Detail & Related papers  (2021-09-23T22:26:52Z)
• Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World [49.80905654161763]
  The Flatland competition aimed at finding novel approaches to solve the vehicle re-scheduling problem (VRSP) The VRSP is concerned with scheduling trips in traffic networks and the re-scheduling of vehicles when disruptions occur. The ever-growing complexity of modern railway networks makes dynamic real-time scheduling of traffic virtually impossible.
  arXiv  Detail & Related papers  (2021-03-30T17:13:29Z)
• A Multi-intersection Vehicular Cooperative Control based on End-Edge-Cloud Computing [25.05518638792962]
  We propose a Multi-intersection Vehicular Cooperative Control (MiVeCC) to enable cooperation among vehicles in a large area with multiple intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. To deal with high-density traffic, vehicle selection methods are proposed to reduce the state space and accelerate algorithm convergence without performance degradation.
  arXiv  Detail & Related papers  (2020-12-01T14:15:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.