COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning

Li, Duowei; Zhu, Feng; Chen, Tianyi; Wong, Yiik Diew; Zhu, Chunli; Wu, Jianping

doi:10.1016/j.trc.2022.103933

Cited by 19 publications

(6 citation statements)

References 37 publications

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“…Similarly, Deng et al [21] devised a traffic schedule model to coordinate platoons, introducing platoon formation decision variables. Similar research endeavors can be found in the literature [19,22,23].…”

Section: Introductionmentioning

confidence: 55%

“…For example, Zhao et al [17] developed criteria to identify compatible platoons in entrance lanes. Similarly, Li et al [18,19] utilized the deep Q-network method to determine the optimal platoon size before the scheduling process. Jiang et al [20] formulated a mixed-integer linear programming model to optimize the passing order and size of platoons.…”

Section: Introductionmentioning

confidence: 99%

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Game Scheduling for Vehicle Platoons at Autonomous Intersection Accommodating Priority Passage Requests

Hu,

Peng

2024

IEEE Access

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Addressing the human-centric needs in vehicle scheduling at intersections poses a significant challenge. In this paper, we propose a game scheduling method to address the problem of platoon scheduling while considering priority passage requests. Firstly, we construct a coalition game-based platoon scheduling model. Within this model, a kernel function searches for the optimal coalition group within the kernel set to ensure that participants, such as platoons, gain higher payoffs by joining the coalition than they would individually. The coalition payoff function determines the optimal passage order. Secondly, we design a baseline strategy where the payoff and action obtained by the priority passage platoon in the coalition game serve as the baseline values. Finally, based on this strategy, we develop a bargaining game model where the priority passage platoon acts as the bargainer, while other platoons act as the other party, with baseline payoff and action serving as constraints. The Nash equilibrium identifies the optimal passage order for the priority passage platoon. We conduct 3 experiments: experiment 1 evaluates the effectiveness of the coalition game model; experiment 2 tests the effectiveness of the bargaining game model; and experiment 3 assesses the effectiveness of the game scheduling method in reducing vehicle travel costs and compares it with existing methods. The results demonstrate that our proposed game scheduling method not only reduces the travel costs of the priority passage platoon but also minimizes its impact on other vehicles to the greatest extent possible.INDEX TERMS Autonomous intersection, baseline payoff, game scheduling, priority passage request, vehicle platoon.

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Section: Introductionmentioning

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Game Scheduling for Vehicle Platoons at Autonomous Intersection Accommodating Priority Passage Requests

Hu,

Peng

2024

IEEE Access

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“…Intersections are key areas for vehicle convergence and evacuation [1]. To improve the efficiency of vehicle traffic and effectively reduce traffic congestion, it is necessary to carry out traffic management on intersections and formulate a reasonable traffic signal control scheme.…”

Section: Introductionmentioning

confidence: 99%

“…In terms of the traffic state, in the existing research, it is mainly transformed into a clustering problem through the extraction of the feature vectors of the traffic state from traffic data, such as speed, flow rate, time occupancy, etc., and then artificial intelligence algorithms are used to solve the problem, such as the spectral clustering algorithm [3], k-means algorithm [4], DBSCAN algorithm [5], etc. The shortcomings of the existing research are as follows: (1) The object of many studies is the traffic state of road sections, not the traffic state of intersections. The difference is that the feature dimension of the road section traffic data is generally low, and the traffic flow generates new feature parameters such as queue length, number of stops, flow ratio, etc., at signal-controlled intersections.…”

Section: Introductionmentioning

confidence: 99%

A Study of Adjacent Intersection Correlation Based on Temporal Graph Attention Network

Li,

Dong,

2024

Entropy

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Traffic state classification and relevance calculation at intersections are both difficult problems in traffic control. In this paper, we propose an intersection relevance model based on a temporal graph attention network, which can solve the above two problems at the same time. First, the intersection features and interaction time of the intersections are regarded as input quantities together with the initial labels of the traffic data. Then, they are inputted into the temporal graph attention (TGAT) model to obtain the classification accuracy of the target intersections in four states—free, stable, slow moving, and congested—and the obtained neighbouring intersection weights are used as the correlation between the intersections. Finally, it is validated by VISSIM simulation experiments. In terms of classification accuracy, the TGAT model has a higher classification accuracy than the three traditional classification models and can cope well with the uneven distribution of the number of samples. The information gain algorithm from the information entropy theory was used to derive the average delay as the most influential factor on intersection status. The correlation from the TGAT model positively correlates with traffic flow, making it interpretable. Using this correlation to control the division of subareas improves the road network’s operational efficiency more than the traditional correlation model does. This demonstrates the effectiveness of the TGAT model’s correlation.

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“…Firstly, how is state space represented in fewer dimensions? The current research mainly focuses on three points: pixel matrix representation based on images [10][11][12][13], DTSE (discrete traffic state encoding) based on each lane [14], and vector representation based on each lane [15,16]. The indexes involved mainly include queue length [17], vehicle location parameters [18], density, speed [19], and current signal phase time [20], etc.…”

Section: Introductionmentioning

confidence: 99%

A Bus Signal Priority Control Method Based on Deep Reinforcement Learning

Shen

Zou

Deng³

et al. 2023

Applied Sciences

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To investigate the issue of multi-entry bus priority at intersections, an intelligent priority control method based on deep reinforcement learning was constructed in the bus network environment. Firstly, a dimension reduction method for the state vector based on the key lane was proposed, which contains characteristic parameters such as the bus states, the flow states, and the signal timing. Secondly, a control action method that can adjust phase sequence and phase green time at the same time was proposed under the constraints of maximum green and minimum green. Furthermore, a reward function, which can be uniformly converted into the number of standard cars, was established focusing on the indexes such as the busload and maximum waiting time. Finally, through building an experimental environment based on SUMO simulation, a real-time bus signal priority control method based on deep reinforcement learning was constructed. The results show that the algorithm can effectively reduce the waiting time of buses without affecting overall traffic efficiency. The findings can provide a theoretical basis for the signal control method considering bus priority and improve the operation efficiency of public transport.

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COOR-PLT: A hierarchical control model for coordinating adaptive platoons of connected and autonomous vehicles at signal-free intersections based on deep reinforcement learning

Cited by 19 publications

References 37 publications

Game Scheduling for Vehicle Platoons at Autonomous Intersection Accommodating Priority Passage Requests

Game Scheduling for Vehicle Platoons at Autonomous Intersection Accommodating Priority Passage Requests

A Study of Adjacent Intersection Correlation Based on Temporal Graph Attention Network

A Bus Signal Priority Control Method Based on Deep Reinforcement Learning

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