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A game theory-based controller approach for identifying incidents caused by aberrant lane changing behavior

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  • Sheikh, Muhammad Sameer
  • Wang, Ji
  • Regan, Amelia

Abstract

Aggressive driving is a key contributor to traffic incidents which deteriorate traffic flow, increase traffic congestion, and pose serious threats to driver and passenger safety. This paper presents a methodology for the estimation of driver aggressiveness and detection of traffic incidents using a game-theory based controller. We first present a game theory-based controlling mechanism, in which a witness vehicle (vehicle A) interacts with an aggressive vehicle (vehicle B) to estimate the aggressiveness of and to predict the future behavior of vehicle B. Second, we use a probe vehicle framework to detect incidents. Third, we apply shockwave theory to identify the location of the incident. Results show that the proposed method can estimate the aggressiveness of vehicle B with a high degree of accuracy. Numerical results obtained through simulation show that the proposed method obtains a better incident detection rate with more than 90% of the incidents detected, on average, with a nearly 91% classification rate and lower false alarm rate than three commonly used methods. It also requires less time to clear the traffic incident. The information obtained from the proposed system can be used to reduce traffic accidents caused by aggressive driving, thereby improving the safety of both drivers and passengers.

Suggested Citation

  • Sheikh, Muhammad Sameer & Wang, Ji & Regan, Amelia, 2021. "A game theory-based controller approach for identifying incidents caused by aberrant lane changing behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 580(C).
  • Handle: RePEc:eee:phsmap:v:580:y:2021:i:c:s0378437121004350
    DOI: 10.1016/j.physa.2021.126162
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    References listed on IDEAS

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    1. Kita, Hideyuki, 1999. "A merging-giveway interaction model of cars in a merging section: a game theoretic analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(3-4), pages 305-312, April.
    2. Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
    3. Zheng, Zuduo, 2014. "Recent developments and research needs in modeling lane changing," Transportation Research Part B: Methodological, Elsevier, vol. 60(C), pages 16-32.
    4. Gipps, P. G., 1986. "A model for the structure of lane-changing decisions," Transportation Research Part B: Methodological, Elsevier, vol. 20(5), pages 403-414, October.
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    Cited by:

    1. Guo, Wenfeng & Song, Xiaolin & Cao, Haotian & Zhao, Song & Yi, Binlin & Wang, Jianqiang, 2023. "Human-centered driving authority allocation for driver-automation shared control: A two-layer game-theoretic approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    2. Li, Sutong & Kang, Leilei & Huang, Hao & Liu, Lan, 2023. "A perimeter control model of urban road network based on cooperative-noncooperative two-stage game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    3. Bowen Gong & Zhipeng Xu & Ruixin Wei & Tao Wang & Ciyun Lin & Peng Gao, 2023. "Reinforcement Learning-Based Lane Change Decision for CAVs in Mixed Traffic Flow under Low Visibility Conditions," Mathematics, MDPI, vol. 11(6), pages 1-24, March.

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