Leveraging vehicle connectivity and autonomy for highway bottleneck congestion mitigation using reinforcement learning

Ha, Paul; Chen, Sikai; Dong, Jiqian; Labi, Samuel

doi:10.1080/23249935.2023.2215338

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…Jiang [44] used DRL to control the CAVs platoon and reduced the velocity turbulence caused by HDV, and improved the altruism. Ha [45] used the graph convolutional network and the DDPG to control the CAVs platoon by the multi‐agent method and improved the road capacity. Liu [46] combined deep learning with RL, and used the long short‐term memory (LSTM) to predict the longitudinal and the lateral movements of vehicles.…”

Section: Related Workmentioning

confidence: 99%

Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning

Cai

Chen

et al. 2023

IET Intelligent Trans Sys

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Cooperative adaptive cruise control (CACC) realizes efficient, intelligent control of vehicle acceleration, deceleration, and steering, through inter‐vehicle communication and cooperative control. However, the close combination of the platoon makes it difficult for other vehicles to cut‐in, which can lead to severe traffic jams on certain sections of the road. The control effect of the CACC depends on the platoon penetration rate, which is the percentage of connected and autonomous vehicles (CAVs) in the total number of platoon members. There is no quantitative control method for different penetration rates, and it is difficult to quantify the impact of CACC vehicles on traffic. Therefore, this paper proposes an innovative CACC control method based on deep reinforcement learning (DRL). First, the altruism control and the quantitative control of the car‐following strategy are realized by the virtual car‐following distance method to reduce the exclusivity of the CACC platoon or improve the road utilization efficiency. Second, a more appropriate platoon reward function and collision avoidance method are proposed. Finally, the Car Learning to Act (CARLA) simulator is used. The obtained results confirm that the CACC control of CAVs based on DRL can absorb speed oscillation and improve fuel economy.

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Section: Related Workmentioning

confidence: 99%

Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning

Cai

Chen

et al. 2023

IET Intelligent Trans Sys

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“…Zhao & Liu, 2022;Zheng et al, 2021). This increased vehicle connectivity provides essential real-time traffic data crucial for congestion control efforts (Guériau et al, 2016;Ha et al, 2023;Mousavi et al, 2021).…”

Section: Urban Congestion Mitigation Strategiesmentioning

confidence: 99%

Dynamic urban traffic rerouting with fog‐cloud reinforcement learning

Du,

Chen,

Dong

et al. 2023

Computer aided Civil Eng

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Dynamic rerouting has been touted as a solution for urban traffic congestion. However, its implementation is stymied by the complexity of urban traffic. To address this, recent studies suggest the efficacy of novel technologies like fog computing and deep reinforcement learning. However, there exist significant challenges in this regard: (1) sorting massive amounts of data associated with large urban networks, (2) large action space that hinders learning efficiency, (3) impairment of rerouting efficacy due to overreliance on regional/local information, and (4) the issue of congestion shifting. To overcome these challenges, this paper presents a novel two‐step approach that integrates graph attention Q network (GAQ) with entropy‐balanced k shortest path (EBkSP) via fog‐cloud information framework to carry out vehicle rerouting in dynamic traffic environments typical of large cities. The first challenge is addressed by GAQ that uses attention mechanism to evaluate traffic and assign region indices based on the relative importance of information. The second challenge is addressed using fog nodes that reduce the action space, and the third is addressed using fog computing combined with cloud computing to facilitate the computation of global optimal information in a centralized planning decentralized execution pattern. The fourth challenge is addressed using EBkSP, which identifies vehicle optimal routes based on route popularity and vehicle priority. After conducting a numerical experiment involving two other cutting‐edge vehicle rerouting models as baselines, it was established that the proposed model delivers results that are 14%–54% superior from perspectives of the mean travel speed and congestion. The superior efficacy of the proposed learning‐based (compared to rule‐based) models is pronounced when rerouting ratios are low. As the rerouting ratio increases, both the learning‐based and rule‐based model outcomes exhibit reduced likelihoods of severe congestion. However, the learning‐based model consistently outperforms the rule‐based model across all scenarios. The proposed model can effectively reroute vehicles under different situations of rerouting ratio and total count of vehicles and can help improve traffic mobility and reduce congestion in urban areas. It can be implemented easily by urban road agencies.

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“…Over the past decade, extensive research has been conducted on trajectory control for CAVs. A variety of control strategies have been developed, including adaptive cruise control (ACC) [8], cooperative adaptive cruise control (CACC) [9,10], model predictive control (MPC) [11][12][13], and deep reinforcement learning (DRL) control [14][15][16][17], are developed to optimize the trajectories of CAVs.…”

Section: Introductionmentioning

confidence: 99%

Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method

Li,

Dong,

et al. 2024

Journal of Advanced Transportation

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One of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right-of-way reasonably at unsignalized intersections can effectively avoid collisions and reduce traffic delays. Proposed here is a new cooperative driving strategy for CAVs at unsignalized intersections based on distributed Monte Carlo tree search (MCTS). A task-area partition framework is also proposed to decompose the mission of cooperative driving into three main tasks: vehicle information sharing, passing order optimization, and trajectory control. Based on the schedule tree of the vehicle passing order, the root parallelization of MCTS combined with the majority voting rule is used to explore as many feasible passing orders (leaf nodes) as possible in a distributed way and find a nearly global-optimal passing order within the limited planning time. The aim is for CAVs to perform proper trajectory adjustments based on the obtained passing order to minimize traffic delays while making the slightest acceleration adjustments. A coupled simulation platform integrating SUMO and Python is developed to construct the unsignalized intersection scenarios and generate the proposed distributed cooperative driving strategy. Comparative analysis with conventional driving strategies demonstrates that the proposed strategy significantly enhances efficiency, safety, comfort, and emission, aligning well with innovative and environmentally friendly urban mobility aspirations.

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Leveraging vehicle connectivity and autonomy for highway bottleneck congestion mitigation using reinforcement learning

Cited by 8 publications

References 59 publications

Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning

Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning

Dynamic urban traffic rerouting with fog‐cloud reinforcement learning

Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method

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