Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles -- A Deep Reinforcement Learning Approach

• URL: http://arxiv.org/abs/2005.08245v1
• Date: Sun, 17 May 2020 12:46:22 GMT
• Title: Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles -- A Deep Reinforcement Learning Approach
• Authors: Liming Jiang, Yuanchang Xie, Danjue Chen, Tienan Li, Nicholas G. Evans
• Abstract summary: This study adopts reinforcement learning to control the behavior of CAV and put a single CAV at the 2nd position of a vehicle fleet. The result show that our controller could decrease the spped oscillation of the CAV by 54% and 8%-28% for those following human-driven vehicles.
• Score: 5.6872893893453105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stop-and-go traffic poses many challenges to tranportation system, but its formation and mechanism are still under exploration.however, it has been proved that by introducing Connected Automated Vehicles(CAVs) with carefully designed controllers one could dampen the stop-and-go waves in the vehicle fleet. Instead of using analytical model, this study adopts reinforcement learning to control the behavior of CAV and put a single CAV at the 2nd position of a vehicle fleet with the purpose to dampen the speed oscillation from the fleet leader and help following human drivers adopt more smooth driving behavior. The result show that our controller could decrease the spped oscillation of the CAV by 54% and 8%-28% for those following human-driven vehicles. Significant fuel consumption savings are also observed. Additionally, the result suggest that CAVs may act as a traffic stabilizer if they choose to behave slightly altruistically.

Related papers

• CAV-AHDV-CAV: Mitigating Traffic Oscillations for CAVs through a Novel Car-Following Structure and Reinforcement Learning [8.63981338420553]
  Connected and Automated Vehicles (CAVs) offer a promising solution to the challenges of mixed traffic with both CAVs and Human-Driven Vehicles (HDVs) While HDVs rely on limited information, CAVs can leverage data from other CAVs for better decision-making. We propose a novel "CAV-AHDV-CAV" car-following framework that treats the sequence of HDVs between two CAVs as a single entity.
  arXiv  Detail & Related papers  (2024-06-23T15:38:29Z)
• Traffic Smoothing Controllers for Autonomous Vehicles Using Deep Reinforcement Learning and Real-World Trajectory Data [45.13152172664334]
  We design traffic-smoothing cruise controllers that can be deployed onto autonomous vehicles. We leverage real-world trajectory data from the I-24 highway in Tennessee. We show that at a low 4% autonomous vehicle penetration rate, we achieve significant fuel savings of over 15% on trajectories exhibiting many stop-and-go waves.
  arXiv  Detail & Related papers  (2024-01-18T00:50:41Z)
• 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)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• Unified Automatic Control of Vehicular Systems with Reinforcement Learning [64.63619662693068]
  This article contributes a streamlined methodology for vehicular microsimulation. It discovers high performance control strategies with minimal manual design. The study reveals numerous emergent behaviors resembling wave mitigation, traffic signaling, and ramp metering.
  arXiv  Detail & Related papers  (2022-07-30T16:23:45Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• 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)
• 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)
• Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion [2.0010674945048468]
  We present an RL-based multi-agent CAV control model to operate in mixed traffic. The results suggest that even at CAV percent share of corridor traffic as low as 10%, CAVs can significantly mitigate bottlenecks in highway traffic.
  arXiv  Detail & Related papers  (2020-10-12T03:52:10Z)
• Driver Intention Anticipation Based on In-Cabin and Driving Scene Monitoring [52.557003792696484]
  We present a framework for the detection of the drivers' intention based on both in-cabin and traffic scene videos. Our framework achieves a prediction with the accuracy of 83.98% and F1-score of 84.3%.
  arXiv  Detail & Related papers  (2020-06-20T11:56:32Z)
• Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-based Traffic Congestion Control Systems [16.01681914880077]
  We explore the backdooring/trojanning of DRL-based AV controllers. Malicious actions include vehicle deceleration and acceleration to cause stop-and-go traffic waves to emerge. Experiments show that the backdoored model does not compromise normal operation performance.
  arXiv  Detail & Related papers  (2020-03-17T08:20:43Z)

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.