文章编号： 1672-5328（2019）03-0096-09

邢慧宁1，秦华1, 2，钮建伟3
(1.北京建筑大学机电与车辆工程学院，北京100044；2.北京市建筑安全监测工程技术研究中心，北京100044； 3.北京科技大学机械工程学院，北京100083)

摘要： 根据中国城市道路的特点，选取6 种典型的城市道路场景进行实验，研究不同驾驶场景下自 动驾驶车辆驾驶人的信息需求。在参试者实施任务的过程中，使用眼动仪记录其眼球注视点和注视 时间，并采用因子分析方法对数据进行分析。结果表明：1)无论驾驶人的注意力是否在监控驾驶任 务上，一些重要的信息都必不可少，例如当前行驶速度、制动距离等车辆自身信息，周围路况、车 况等汽车周围环境信息，以及辅助系统处理当前场景的能力等辅助系统相关信息；2)紧急危险场景 中，驾驶人更需要周围车辆行驶速度、与两侧车辆距离以及车头时距等信息，以便采取换道、制动 等措施；3)在行驶缓慢的拥堵场景中，驾驶人更需要道路相关信息，例如交通标志、车道条数等。

关键词： 交通工程；仿真实验；因子分析；信息需求；自动驾驶车辆；第二任务

中图分类号： U491

文献标识码：A

Characteristics of Drivers' Information Demand in Autonomous Vehicles

Xing Huining1, Qin Hua1, 2, Niu Jianwei3
(1.School of Mechanical-Electronic and Automobile Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China; 2.Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing 100044, China; 3.School of Mechanical Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract： According to the characteristics of urban roads in china, this paper selects 6 representative urban road scenarios to study the drivers' information demand when driving autonomous vehicles. The eye fixation area and fixation time are recorded by the eye tracker while participants are on-going the tasks. Then the factor analysis method is used to explore the data, the results show that: 1) Whether the driver's attention is on the driving task or not, the information, including vehicle information (e.g. travel speed, braking distance of vehicle), environmental information (e.g. the road condition, car condition around driver) and autonomous driving system related information (the ability of handling the risky condition) are essential; 2) In the scenario of emergency, the information (e.g. travel speed, the distance with other vehicles) are necessary to support drivers to make a decision about braking and changing the lane; 3) In the congestion scenario in which cars move slowly, drivers need more road information (e.g. traffic signs, number of lanes).

Keywords： traffic engineering; simulation; factor analysis; information demand; autonomous vehicles; secondary task