Volume 41 Issue 4
Aug.  2023
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CUI Bingyan, LI He, CUI Zhe, JI Haojie, GUAN Yuxin. A Review of Safety Studies on Lane Change Decision-makings for Connected Automated Vehicles[J]. Journal of Transport Information and Safety, 2023, 41(4): 1-13. doi: 10.3963/j.jssn.1674-4861.2023.04.001
Citation: CUI Bingyan, LI He, CUI Zhe, JI Haojie, GUAN Yuxin. A Review of Safety Studies on Lane Change Decision-makings for Connected Automated Vehicles[J]. Journal of Transport Information and Safety, 2023, 41(4): 1-13. doi: 10.3963/j.jssn.1674-4861.2023.04.001

A Review of Safety Studies on Lane Change Decision-makings for Connected Automated Vehicles

doi: 10.3963/j.jssn.1674-4861.2023.04.001
  • Received Date: 2023-03-13
    Available Online: 2023-11-23
  • Safety of lane change decision-makings for connected automated vehicles (CAVs) is a key task to improve traffic safety and enhance road mobility. In this paper, the safety issues related to lane changing of CAVs are investigated. From the perspective of driving safety, the adverse impacts of extreme lane-changing behavior and emergency lane-changing behavior on traffic safety are analyzed, emphasizing the importance of risk assessment. The various risk assessment methods of lane changing are reviewed, including the use of environmental sensors, traffic conflict indicators, and vehicle-level micro-trajectory data. Identifying risks through risk assessment and taking corresponding measures can significantly reduce traffic accidents caused by dangerous lane-changing behavior. Furthermore, the methods for CAVs to make lane-changing decisions by obtaining environmental information in both traditional and vehicle to everything (V2X) environments are elaborated. Particularly focusing on the CAVs in V2X environment, the decision-making through the environment perception and recognition, targets detection, and data processing is analyzed. Reasonable recommendations are proposed for achieving safe decision-making by CAVs in V2X environment in the future. Then the existing models for decision-making of lane-changing are analyzed and categorized into four types: rule-based models, discrete choice models, artificial intelligence models, and game theory models. The status of research and application, existing problems, and prospects of decision-making models in the field of road traffic safety are systematically summarized, both domestically and internationally. In summary, despite significant research achievements in lane-changing technologies for CAVs, there are still many challenges ahead. To tackle the existing problems in research, such as ensuring safe and reliable decisions in low-level automated driving environments, making more efficient and intelligent driving decisions for CAVs in low-penetration scenarios, achieving safe decision-making in situations with incomplete information, and improving the optimization of the algorithms for lane-changing decision-making, feasible solutions are proposed accordingly.


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