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Markov chain-based platoon recognition model in mixed traffic with human-driven and connected and autonomous vehicles

网联自动驾驶环境下基于马尔可夫链理论的混合交通流车队识别模型

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Abstract

Many vehicle platoons are interrupted while traveling on roads, especially at urban signalized intersections. One reason for such interruptions is the inability to exchange real-time information between traditional human-driven vehicles and intersection infrastructure. Thus, this paper develops a Markov chain-based model to recognize platoons. A simulation experiment is performed in Vissim based on field data extracted from video recordings to prove the model’s applicability. The videos, recorded with a high-definition camera, contain field driving data from three Tesla vehicles, which can achieve Level 2 autonomous driving. The simulation results show that the recognition rate exceeds 80% when the connected and autonomous vehicle penetration rate is higher than 0.7. Whether a vehicle is upstream or downstream of an intersection also affects the performance of platoon recognition. The platoon recognition model developed in this paper can be used as a signal control input at intersections to reduce the unnecessary interruption of vehicle platoons and improve traffic efficiency.

摘要

鉴于车队在城市道路信号交叉口通行时常被交叉口信号灯所打断,造成不必要的停车,致使交叉口通行能力下降、尾气排放增加以及燃油消耗升高,本文面向未来网联自动驾驶环境,基于马尔可夫链理论,构建了混合交通流下的车队识别模型。此外,本文借助能够实现L2级自动驾驶技术的特斯拉车辆,设计并组织实施了网联自动驾驶环境下的全样本交通流视频采集实验,并利用视频识别技术提取车辆轨迹数据。最后,利用实测数据和交通仿真软件Vissim,进行网联自动驾驶环境下的车队识别仿真实验。仿真结果表明,当网联自动驾驶车辆渗透率高于0.7 时,车队识别率超过80%。另外,仿真结果还表明,车队识别位置同样影响车队识别率,例如在交叉口上游与下游分别进行车队识别时模型性能不同。本文所构建的车队识别模型能够为未来网联自动驾驶环境下的信号协调控制提供输入参数,以减少城市干线车队的不必要停车,进而提高城市交通运行效率。

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Authors and Affiliations

  1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Ministry of Transport, Beijing Jiaotong University, Beijing, 100044, China

    Shen-zhen Ding  (丁深圳), Xu-mei Chen  (陈旭梅) & Lei Yu  (于雷)

  2. School of Intelligent Transportation, Xuchang University, Xuchang, 461000, China

    Xu-mei Chen  (陈旭梅) & Lei Yu  (于雷)

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  1. Shen-zhen Ding  (丁深圳)
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  2. Xu-mei Chen  (陈旭梅)
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  3. Lei Yu  (于雷)
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Contributions

DING Shen-zhen conducted the literature review and wrote the first draft of the manuscript. DING Shen-zhen and CHEN Xu-mei analyzed the measured data and the calculated results. DING Shen-zhen, CHEN Xu-mei, and YU Lei edited the draft of the manuscript. All authors reviewed the results and approved the final version of the manuscript.

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Correspondence to Xu-mei Chen  (陈旭梅).

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DING Shen-zhen, CHEN Xu-mei, and YU Lei declare that they have no conflict of interest.

Foundation item: Project(71871013) supported by the National Natural Science Foundation of China

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Ding, Sz., Chen, Xm. & Yu, L. Markov chain-based platoon recognition model in mixed traffic with human-driven and connected and autonomous vehicles. J. Cent. South Univ. 29, 1521–1536 (2022). https://doi.org/10.1007/s11771-022-5023-8

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