交通环境及驾驶经验对驾驶员眼动和工作负荷影响的研究
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摘要
在道路交通事故的致因中有90％与人的因素有关，而在人的因素中起主导作用的是人的感知因素，有学者认为驾驶员对信息的感知有90％来自视觉。可见，视觉在驾驶任务和事故控制中的重要地位。对眼动这一基本视觉行为的研究，有助于揭示和了解驾驶员目标搜索以及潜在危险感知过程和规律，对改进道路和车辆的设计准则，完善驾驶员培训考试规程，设计新型驾驶辅助系统和危险预警系统，均有着重要的理论指导意义。
本文选取了24名具有不同驾驶经验的驾驶员样本，在城市道路、普通公路、山区公路、城乡结合道路四种典型交通环境中进行驾驶试验，利用高速眼动追踪系统(眼动仪)记录驾驶员在驾驶过程中的注视时间、注视目标、扫视时间、扫视速度等动态眼动数据；利用生理测试仪记录驾驶过程中驾驶员的心电、皮电和呼吸等生理信号；利用GPS记录试验过程中车辆的运行速度。
运用统计学方法对采集的数据进行处理和分析。首先统计分析驾驶员的注视时间、视觉搜索广度、扫视速度和扫视幅度随道路条件的变化规律；其次，运用注视目标逐一统计方法，分析驾驶员对各类目标的注视时间和频次，依据目标的位置进行注视区域划分，分析熟练与非熟练驾驶员群体在不同交通环境中对各类目标的注视频次和不同区域注视点分布的差异；运用动态聚类方法将驾驶员的视野平面划分为6个注视区域，应用马尔可夫链理论求解驾驶员在不同注视区域间的一步转移概率，求解各注视区域的马尔可夫平稳分布，研究驾驶员的注视转移模式；此外，用心率增长率和心率变异性指标表征驾驶员的工作负荷，分析交通环境和熟练程度对驾驶员工作负荷的影响，并进行眼动行为与工作负荷的相关性分析；最后，分别选取8个注视转移模式指标和5个综合指标，运用主成分分析方法建立了两个驾驶熟练程度评价模型，并分析各指标对驾驶熟练程度的影响度。
研究结果表明，不同交通环境和驾驶经验对驾驶员的眼动行为和工作负荷均有显著影响。主要研究结论：
1．驾驶员的注视持续时间、水平和垂直方向视觉搜索广度、扫视幅度和扫视速度均随着道路条件的改变而变化，驾驶员会根据交通环境不同而调整他们的关注目标和注意力分配；
2．与非熟练驾驶员相比，熟练驾驶员表现出更灵活的视觉搜索模式；
3．在相同交通环境中非熟练驾驶员表现出更高的工作负荷；
4．扫视行为能反映驾驶员的心智努力程度和工作负荷高低；
5．用动态聚类方法将驾驶员的视野平面划分为6个注视区域与驾驶员实际注视点的区域分布吻合度好；
6．运用主成分分析方法建立的两个驾驶熟练程度评价模型能有效区分熟练与非熟练驾驶员群体。
本研究得到了国家自然科学基金项目(50678027)的资助。
Among all reasons that induced road traffic accidents, about 90% are related to human factors, in which the perception factor plays the primary role. It is deemed that 90% of information perception of car drivers comes from vision. It is obvious that vision is of great importance in driving task and accident control. The study on the basic visual behavior of eye movement will help to discover and understand the process and laws in driver's object search and potential risk perception. The study also has important significance of theoretical instruction for improving the design guidelines of roadway and vehicles, for amending driver's training and testing rules, and for designing new-type driving assistant system and risk early warning system.
The driving tests were carried out in 4 typical traffic environments, including urban roadway, normal highway, mountainous highway and suburban highway. Totally 24 object drivers with different driving experiences were chosen. In driving process, the eye movement data such as the fixation duration, fixation object, saccade duration and saccade velocity were recorded by high speed eye movement tracking system (eye movement record device). The physiological signals of driver's heart electrical activity, galvanic skin response (GSR) and breath frequency were recorded by physiological test device. The GPS was used to record vehicle speed in real time during the whole test process.
The test data were analyzed with statistical method. Firstly, the variational rules of fixation duration, visual search variance, saccade velocity and saccade amplitude of drivers with road conditions were investigated statistically. Secondly, by counting the fixation objects one by one, drivers' fixation duration and fixation frequencies at each type of object were analyzed, and areas of fixations (AOFs) were divided according to object positions. On that basis, the variances between experienced drivers and inexperienced drivers in different traffic environments were explored focused on fixation frequencies at each type of object and fixation distribution in different AOFs. Drivers' visual field was divided into 6 AOFs by dynamic cluster analysis. Using Markov chain theory, driver's fixation transition mode was studied, in which the one-step transition probabilities among drivers' different AOFs were solved and the Markov stationary distributions in each AOF were obtained. In addition, driver's workload was characterized by fluctuating rate of heart rate and heart rate variability. The effects of traffic environments and driving experiences on driver's workload were explored, and the correlation analysis of eye movement and workload was carried out. Lastly, 8 indexes of fixation transition modes and 5 composite indexes were chosen, and two evaluation models on driving experience were built by using principal component analysis (PCA), the effects of each index on driving experience were investigated as well.
Study results indicate that, traffic environment and driving experience have significant effects on driver's eye movement behavior and workload. The following conclusions can be drawn from this research work.
1. Driver's fixation duration, horizontal and vertical variances of visual search, saccade amplitude and saccade velocity vary with change of road conditions. Drivers adjust their fixation object and distribution of attention according to the traffic environment.
2. Compared to inexperienced drivers, experienced drivers show a more flexible visual search mode.
3. Inexperience drivers endure heavier workload than experienced drivers in the same traffic environment.
4. Driver's mental effort and workload levels can be reflected by the saccade behavior.
5. The 6 AOFs for driver's visual field divided by the method of dynamic clustering are in accord with the actual AOFs distribution.
6. Inexperienced drivers can be efficiently distinguished from experienced ones with the evaluation models built by PCA method.
The research was sponsored by National Natural Science Foundation (50678027).
本文选取了24名具有不同驾驶经验的驾驶员样本，在城市道路、普通公路、山区公路、城乡结合道路四种典型交通环境中进行驾驶试验，利用高速眼动追踪系统(眼动仪)记录驾驶员在驾驶过程中的注视时间、注视目标、扫视时间、扫视速度等动态眼动数据；利用生理测试仪记录驾驶过程中驾驶员的心电、皮电和呼吸等生理信号；利用GPS记录试验过程中车辆的运行速度。
运用统计学方法对采集的数据进行处理和分析。首先统计分析驾驶员的注视时间、视觉搜索广度、扫视速度和扫视幅度随道路条件的变化规律；其次，运用注视目标逐一统计方法，分析驾驶员对各类目标的注视时间和频次，依据目标的位置进行注视区域划分，分析熟练与非熟练驾驶员群体在不同交通环境中对各类目标的注视频次和不同区域注视点分布的差异；运用动态聚类方法将驾驶员的视野平面划分为6个注视区域，应用马尔可夫链理论求解驾驶员在不同注视区域间的一步转移概率，求解各注视区域的马尔可夫平稳分布，研究驾驶员的注视转移模式；此外，用心率增长率和心率变异性指标表征驾驶员的工作负荷，分析交通环境和熟练程度对驾驶员工作负荷的影响，并进行眼动行为与工作负荷的相关性分析；最后，分别选取8个注视转移模式指标和5个综合指标，运用主成分分析方法建立了两个驾驶熟练程度评价模型，并分析各指标对驾驶熟练程度的影响度。
研究结果表明，不同交通环境和驾驶经验对驾驶员的眼动行为和工作负荷均有显著影响。主要研究结论：
1．驾驶员的注视持续时间、水平和垂直方向视觉搜索广度、扫视幅度和扫视速度均随着道路条件的改变而变化，驾驶员会根据交通环境不同而调整他们的关注目标和注意力分配；
2．与非熟练驾驶员相比，熟练驾驶员表现出更灵活的视觉搜索模式；
3．在相同交通环境中非熟练驾驶员表现出更高的工作负荷；
4．扫视行为能反映驾驶员的心智努力程度和工作负荷高低；
5．用动态聚类方法将驾驶员的视野平面划分为6个注视区域与驾驶员实际注视点的区域分布吻合度好；
6．运用主成分分析方法建立的两个驾驶熟练程度评价模型能有效区分熟练与非熟练驾驶员群体。
本研究得到了国家自然科学基金项目(50678027)的资助。
Among all reasons that induced road traffic accidents, about 90% are related to human factors, in which the perception factor plays the primary role. It is deemed that 90% of information perception of car drivers comes from vision. It is obvious that vision is of great importance in driving task and accident control. The study on the basic visual behavior of eye movement will help to discover and understand the process and laws in driver's object search and potential risk perception. The study also has important significance of theoretical instruction for improving the design guidelines of roadway and vehicles, for amending driver's training and testing rules, and for designing new-type driving assistant system and risk early warning system.
The driving tests were carried out in 4 typical traffic environments, including urban roadway, normal highway, mountainous highway and suburban highway. Totally 24 object drivers with different driving experiences were chosen. In driving process, the eye movement data such as the fixation duration, fixation object, saccade duration and saccade velocity were recorded by high speed eye movement tracking system (eye movement record device). The physiological signals of driver's heart electrical activity, galvanic skin response (GSR) and breath frequency were recorded by physiological test device. The GPS was used to record vehicle speed in real time during the whole test process.
The test data were analyzed with statistical method. Firstly, the variational rules of fixation duration, visual search variance, saccade velocity and saccade amplitude of drivers with road conditions were investigated statistically. Secondly, by counting the fixation objects one by one, drivers' fixation duration and fixation frequencies at each type of object were analyzed, and areas of fixations (AOFs) were divided according to object positions. On that basis, the variances between experienced drivers and inexperienced drivers in different traffic environments were explored focused on fixation frequencies at each type of object and fixation distribution in different AOFs. Drivers' visual field was divided into 6 AOFs by dynamic cluster analysis. Using Markov chain theory, driver's fixation transition mode was studied, in which the one-step transition probabilities among drivers' different AOFs were solved and the Markov stationary distributions in each AOF were obtained. In addition, driver's workload was characterized by fluctuating rate of heart rate and heart rate variability. The effects of traffic environments and driving experiences on driver's workload were explored, and the correlation analysis of eye movement and workload was carried out. Lastly, 8 indexes of fixation transition modes and 5 composite indexes were chosen, and two evaluation models on driving experience were built by using principal component analysis (PCA), the effects of each index on driving experience were investigated as well.
Study results indicate that, traffic environment and driving experience have significant effects on driver's eye movement behavior and workload. The following conclusions can be drawn from this research work.
1. Driver's fixation duration, horizontal and vertical variances of visual search, saccade amplitude and saccade velocity vary with change of road conditions. Drivers adjust their fixation object and distribution of attention according to the traffic environment.
2. Compared to inexperienced drivers, experienced drivers show a more flexible visual search mode.
3. Inexperience drivers endure heavier workload than experienced drivers in the same traffic environment.
4. Driver's mental effort and workload levels can be reflected by the saccade behavior.
5. The 6 AOFs for driver's visual field divided by the method of dynamic clustering are in accord with the actual AOFs distribution.
6. Inexperienced drivers can be efficiently distinguished from experienced ones with the evaluation models built by PCA method.
The research was sponsored by National Natural Science Foundation (50678027).
引文
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