Exploring Cooperative Lane Change Decisions in Vehicle-to-Infrastructure – A Potential Conflict Analysis Approach
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Vehicle-to-infrastructure (V2I) technology enables information interaction between vehicles and among vehicles and infrastructure, significantly enhancing the efficiency of lane-changing processes and stabilising traffic flow. Current research primarily focuses on single lane-changing events in fixed micro-level scenarios or studies involving small-scale vehicle fleets, neglecting the randomness of lane-changing vehicle arrivals and potential conflicts during lane-changing. This paper proposes a lane-changing decision model based on potential conflict analysis, specifically tailored to mandatory lane-changing requirements in high-density traffic conditions. The model comprises sub-models for lane-changing decision triggering, influence range calculation and lane-changing priority determination, capable of dynamically adjusting the lane-changing sequence, mitigating lane-changing conflicts, and improving driving safety and traffic efficiency. Simulation experiments indicate that, when compared to lane-changing patterns in real-world traffic scenarios, this model reduces travel time by 23.30%, delays by 21.95% and the number of stops by 23.84%, thereby providing a novel approach for lane-changing decision-making and control in V2I environments.
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Gipps P. A model for the structure of lane-changing decision. Transportation Research Part B Methodological. 1986;20(5):403-414. DOI: 10.1016/0191-2615(86)90012-3
Yang Q, Koutsopoulos H. A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transportation Research C. 1996;4(3):113-129. DOI: 10.1016/S0968-090X(96)00006-X
Jula H, Kosmatopoulos E, Ioannou P. Collision avoidance analysis for lane changing and merging. IEEE Transactions on Vehicular Technology, 2000;49(6):2295-2308. DOI: 10.1109/25.901899
Wang X, Zhang Y, Jiao J. A state dependent mandatory lane-changing model for urban arterials with hidden markov model method. International Journal of Transportation Science & Technology. 2019;8(2):219-230. DOI: 10.1016/j.ijtst.2018.11.005
Deng J, et al. Analysis and Classification of Vehicle-Road Collaboration Application Scenarios. Procedia Computer Science. 2022;208:111-117. DOI: 10.1016/j.procs.2022.10.018
Yu Y, et al. A dynamic lane-changing decision and trajectory planning model of autonomous vehicles under mixed autonomous vehicle and human-driven vehicle environment. Physica A: Statistical Mechanics and its Applications. 2023;609:128361. DOI: 10.1016/j.physa.2022.128361
Chen C, et al. Infrastructure sensor-based cooperative perception for early stage connected and automated vehicle deployment. Journal of Intelligent Transportation Systems. 2024;28(6):956-970. DOI: 10.1080/15472450.2023.2257596
Ali Y, Zheng Z, Haque M. Modelling lane-changing execution behaviour in a connected environment: A grouped random parameters with heterogeneity-in-means approach. Communications in Transportation Research. 2021;1:100009. DOI: 10.1016/j.commtr.2021.100009
Arbis D, Dixit VV. Game theoretic model for lane changing: Incorporating conflict risks. Accident Analysis & Prevention. 2019;125:158-164. DOI: 10.1016/j.aap.2019.02.007
Tian T, et al. Traffic behavior analysis of the urban expressway ramp based on continuous cellular automata. Physica A: Statistical Mechanics and its Applications. 2024;633(1):129418. DOI: 10.1016/j.physa.2023.129418
Zhao H, et al. Two-lane mixed traffic flow model considering lane changing. Journal of Computational Science. 2022;61:101635. DOI: 10.1016/j.jocs.2022.101635
Gokasar I, et al. Metaverse integration alternatives of connected autonomous vehicles with self-powered sensors using fuzzy decision making model. Information Sciences. 2023;642:119192. DOI: 10.1016/j.ins.2023.119192
Xie D, et al. A data-driven lane-changing model based on deep learning. Transportation Research Part C: Emerging Technologies. 2019;106:41-60. DOI: DOI.ORG/10.1016/j.trc.2019.07.002
Ma Y, et al. Collision-avoidance lane change control method for enhancing safety for connected vehicle platoon in mixed traffic environment. Accident Analysis & Prevention. 2023;184:106999. DOI: 10.1016/j.aap.2023.106999
Ali Y, et al. A hazard-based duration model to quantify the impact of connected driving environment on safety during mandatory lane-changing. Transportation Research Part C: Emerging Technologies. 2019;106:113-131. DOI: 10.1016/j.trc.2019.07.015
Du H, et al. A lane-changing trajectory re-planning method considering conflicting traffic scenarios. Engineering Applications of Artificial Intelligence. 2024;127:107264. DOI: 10.1016/j.engappai.2023.107264
Chen X, et al. A macro-micro approach to reconstructing vehicle trajectories on multi-lane freeways with lane changing. Transportation Research Part C: Emerging Technologies. 2024;160:104534. DOI: 10.1016/j.trc.2024.104534
Bharathi D, Vanajakshi L, Subramanian SC. Spatio-temporal modelling and prediction of bus travel time using a higher-order traffic flow model. Physica A: Statistical Mechanics and its Applications. 2022;596:127086. DOI: 10.1016/j.physa.2022.127086
Albeaik S, et al. Limitations and improvements of the intelligent driver model. Siam Journal on Applied Dynamical Systems. 2022;21(3):1862-1892. DOI: 10.1137/21m1406477
Xiao X, et al. A novel car-following model considering conditional heteroskedasticity of acceleration fluctuation and driving force. Journal of Intelligent & Fuzzy Systems. 2018;34(4):2301-2311. DOI: 10.3233/JIFS-171351
Longford NT. Inference with the lognormal distribution. Journal of Statistical Planning and Inference. 2009;139(7):2329-2340. DOI: 10.1016/j.jspi.2008.10.015
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