自动驾驶接管影响因素分析与研究进展

doi:10.16097/j.cnki.1009-6744.2022.02.008

摘要/Abstract

摘要： 部分或有条件自动驾驶车辆允许驾驶员将驾驶任务移交给自动驾驶系统，但驾驶员仍需对驾驶环境进行监测，若发生紧急事件或驾驶环境超出系统运行设计域等情况，驾驶员需要及时接管车辆。影响驾驶接管过程的因素主要包括：人因、交通环境以及人-机交互系统。本文分析了驾驶员认知负荷特性等人因对接管过程和接管时间预算的影响。分析发现，驾驶员长时间脱离驾驶任务会导致其陷入被动疲劳或驾驶分心状态，从而降低接管绩效。适当的非驾驶任务可以使驾驶员保持一定的认知负荷，降低驾驶员的被动疲劳水平。结合网联技术的应用可以多次发出预警信号，提高接管绩效。本文讨论了交通密度、道路条件等交通环境对接管时驾驶员感知、认知及决策的影响，探讨混合交通下过渡区智能网联车辆控制权切换 (Transitions of Control, ToC) 的管理问题。在复杂道路交通下，驾驶员需要更多时间恢复对环境的感知，且驾驶员在弯道接管车辆时更容易出现较大横向偏差。在混合交通环境中，为防止过渡区出现集中的ToC，可以制定相应交通管理措施，以降低过渡区域中车辆之间的相互干扰。本文还分析了视觉、听觉、触觉、嗅觉及其组合类型交互方式的优、缺点，讨论网联环境下人-机交互系统设计以及ToC形式。单个的交互方式有其自身的优、缺点，多种类型相结合的交互形式能形成优势互补，及时地将接管信息传递给驾驶员，并将其注意力集中于对环境的感知。网联技术发展使得可利用的行车信息的数量和种类都有所提高，网联信息需要更好地呈现策略，以保证人-机交互界面具有较高的可用性和接受性，为驾驶员提供更加准确的交互信息。同时，利用驾驶员状态识别技术实时监测驾驶员所处状态，并通过人-机交互系统提醒驾驶员，使其保持警觉，提高接管绩效。未来研究应该重点关注非驾驶任务对驾驶员认知特性的影响，结合接管时的驾驶环境，遵循预测算法辅助驾驶员实现控制权的平稳过渡。随着网联技术的不断应用，逐步改进现有人-机交互系统的设计和性能，对过渡区域ToC的管理问题展开深入研究。

关键词: 智能交通, 自动驾驶车辆, 网联环境, 驾驶接管, 人因, 人机交互

Abstract: Partial or conditional autonomous vehicles allow the driver to delegate driving tasks to the autonomous driving system. The driver must, however, continue to monitor the driving environment. In the event of an emergency or if the driving environment exceeds the operation design domain of the autonomous driving system, the driver must immediately take control of the vehicle. Human factors, traffic environment, and human-machine interaction are the main factors that influence the driving takeover process. This paper investigates the impact of driver's cognitive loadcharacteristics and other human factors on the takeover process and time budget. Disengaging from the driving task for a long time will cause the driver to experience passive fatigue or distraction, thereby reducing the driver's takeover performance. The appropriate non-driving related task can keep the driver's cognitive load and reduce the driver's passive fatigue level. With the application of connected technology, the early warning signal can be provided in two stages to improve the driver's takeover performance. The influence of the traffic environment such as traffic density and road conditions on the driver's perception, cognition, and decision- making was discussed. The transitions of control (ToC) management of connected vehicles at the transition area were investigated. In complex road traffic, the driver requires more time to recover the perception of the environment, and the driver is prone to large lateral deviation when taking over the vehicle in a curve road. In mixed traffic, to prevent concentrated ToC in the transition area, corresponding traffic management measures can be formulated to alleviate the mutual interference between vehicles. The advantages and disadvantages of visual, auditory, tactile, olfactory interaction and their combination modes as well as the method of ToC were summarized. Furthermore, the design of human-machine interaction systems underconnected environments and the ToC form was discussed. Each modality has its advantages and disadvantages. The combination of multiple types of modality can complement each other's advantages, and timely transfer the takeover information to the driver. The development of connected technology has increased the amount and types of available driving information. Connected information needs a better presentation strategy to ensure that the human-machine interface has higher usability and acceptability, and provides more accurate information for drivers. Meanwhile, the driver's state recognition technology can be also used to monitor the driver's state in real-time, and remind them to keep vigilant using the human-computer interaction system to improve takeover performance. Future researchers should pay more attention to the impact of non-driving tasks on cognitive characteristics. Combining with the driving environment and following the predictive algorithm helps to assist the driver in completing smooth ToC. With the continuous application of connected technologies, the performance of existing human-machine interaction systems needs to be improved gradually and further research on the management of TOC in the transition area is necessary.

Key words: intelligent transportation, automated driving vehicles, connected environment, takeover, human factors; human-machine interaction

中图分类号:

U495

郭烈, 胥林立, 秦增科, 王旭. 自动驾驶接管影响因素分析与研究进展[J]. 交通运输系统工程与信息, 2022, 22(2): 72-90.

GUO Lie , XU Lin-li, QIN Zeng-ke, WANG Xu. Analysis and Overview of Influencing Factors on Autonomous Driving Takeover[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 72-90.

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http://www.tseit.org.cn/CN/Y2022/V22/I2/72

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