秦增科, 郭烈, 马跃, 岳明. 基于人机协同的车道保持辅助系统研究进展[J]. 工程科学学报, 2021, 43(3): 355-364. DOI: 10.13374/j.issn2095-9389.2020.10.13.001
引用本文: 秦增科, 郭烈, 马跃, 岳明. 基于人机协同的车道保持辅助系统研究进展[J]. 工程科学学报, 2021, 43(3): 355-364. DOI: 10.13374/j.issn2095-9389.2020.10.13.001
QIN Zeng-ke, GUO Lie, MA Yue, YUE Ming. Overview of lane-keeping assist system based on human–machine cooperative control[J]. Chinese Journal of Engineering, 2021, 43(3): 355-364. DOI: 10.13374/j.issn2095-9389.2020.10.13.001
Citation: QIN Zeng-ke, GUO Lie, MA Yue, YUE Ming. Overview of lane-keeping assist system based on human–machine cooperative control[J]. Chinese Journal of Engineering, 2021, 43(3): 355-364. DOI: 10.13374/j.issn2095-9389.2020.10.13.001

基于人机协同的车道保持辅助系统研究进展

Overview of lane-keeping assist system based on human–machine cooperative control

  • 摘要: 基于人机动态协同控制的车道保持辅助系统有助于兼顾汽车的安全性与驾驶员的舒适性,分析了该系统在车道偏离决策模型、驾驶权动态分配及性能评估等方面的研究现状和发展趋势。在车道偏离决策模型方面,应根据驾驶员的状态制定不同的决策模型,既可以建立自适应调节的决策模型,又应允许驾驶员根据自己的喜好和外部驾驶环境手动调整决策模型中预设的参数;在驾驶权分配方面,应探索更加合理的驾驶权动态分配方式,设计智能的优化算法或控制模型;在性能评估指标方面,应加入与降低人机冲突及减少驾驶员控制量相关的评估指标,制定科学完善的主观评估体系。未来研究应该深度融合驾驶员因素,实时发出警报与主动干预,并能够对系统进行完善的测试与评估。

     

    Abstract: As the final stage of intelligent vehicle, traffic accidents can be effectively reduced by automatic driving. However, neither the technology nor the regulations are mature for autonomous driving. The lane-keeping assist system is one of the important components of the advanced driver-assistance system. When driver fatigue or inattention is detected, the system can effectively prevent the vehicle departure from the lane. Information such as vehicle status, driver status, and external environment can be used by the lane-keeping assist system based on human–machine dynamic cooperative control, thereby smoothly changing the driving rights between the driver and the automatic controller. The system can keep the vehicle in the lane while complying with the driver's intention, thereby ensuring vehicle safety and driver comfort. The research status and future development suggestions on lane-departure decision models, dynamic allocation of driving rights, and performance evaluation were analyzed in this paper. Regarding lane-departure decision models, different decision models considering the driver's state should be developed. The decision model can be established as an adaptive adjustment model and also should allow the manual adjustment of the preset parameters according to the driver’s preferences and the external driving environment. Concerning the allocation of driving rights, a more reasonable dynamic allocation of driving rights should be explored, and intelligent optimization algorithms or control models should be designed. Regarding performance evaluation indicators, evaluation indicators related to the reduction of human–machine conflict and the amount of control effort should be added. A scientific and complete subjective evaluation system should be developed. Future studies on lane-keeping assist system based on human–machine cooperative control should deeply integrate driver factors, issue real-time warnings and active intervention, and perform complete testing and evaluation of the system.

     

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