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In order to study the influence of driving experience and traffic flow conditions on driver's visual search mode, an experiment platform was built. Driver's eye movement data was collected through a large number of real vehicle tests. By analyzing the visual features variations of different experience drivers at peak and peaceful peak period, we fo...
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Citations
... The dynamic clustering method, which has the benefits of a simple principle, ease of implementation, and the ability to gradually enhance clustering accuracy through several iterations, is frequently used in studying AOI division. For example, the K-means clustering method, owing to its simple calculations, strong applicability, efficient operation, and good scalability for large sample size datasets, has become a traditional method for AOI division [38][39][40]. ...
This study aimed to investigate disparities in drivers’ visual search behavior across various typical traffic conditions on prairie highways and analyze driving safety at the visual search level. The study captured eye movement data from drivers across six real-world traffic environments: free driving, vehicle-following, oncoming vehicles, rear vehicles overtaking cut-in, roadside risks, and driving through intersections, by carrying out a real vehicle test on a prairie highway. The drivers’ visual search area was divided into five areas using clustering principles. By integrating the Markov chain and information entropy theory, the information entropy of fixation distribution (IEFD) was constructed to quantify the complexity of drivers’ traffic information search. Additionally, the main area of visual search (MAVS) and the peak-to-average ratio of saccade velocity (PARSV) were introduced to measure visual search range and stability, respectively. The study culminated in the creation of a visual search load evaluation model that utilizes both VIKOR and improved CRITIC methodologies. The findings indicated that while drivers’ visual distribution and transfer modes vary across different prairie highway traffic environments, the current lane consistently remained their primary area of search for traffic information. Furthermore, it was found that each visual search indicator displayed significant statistical differences as traffic environments changed. Particularly when encountering roadside risks, drivers’ visual search load increased significantly, leading to a considerable decrease in driving safety.
... Previous traffic crash statistics and studies have shown that the human factor is a primary aspect that causes road traffic accidents [12][13][14][15][16]. Besides, statistical analysis showed that 80-90% of information used by drivers in driving was gathered by visual observation [17,18], of which dynamic information accounts for 95%. Therefore, drivers' dynamic visual features are by far the most relevant features to a tunnel's safe driving [19][20][21]. ...
This study reports the results of a pilot study on spatiotemporal characteristics of drivers’ visual behavior while driving in three different luminance levels in a tunnel. The study was carried out in a relatively long tunnel during the daytime. Six experienced drivers were recruited to participate in the driving experiment. Experimental data of pupil area and fixation point position (at the tunnel’s interior zone: 1566 m long) were collected by non-intrusive eye-tracking equipment at three luminance levels (2 cd/m2, 2.5 cd/m2, and 3 cd/m2). Fixation maps (color-coded maps presenting distributed data) were created based on fixation point position data to quantify changes in visual behavior. The results demonstrated that luminance levels had a significant effect on pupil areas and fixation zones. Fixation area and average pupil area had a significant negative correlation with luminance levels during the daytime. In addition, drivers concentrated more on the front road pavement, the top wall surface, and the cars’ control wheels. The results revealed that the pupil area had a linear relationship with the luminance level. The limitations of this research are pointed out and the future research directions are also prospected.
... The experienced drivers' peripheral perception is better than novice drivers. The reason may be that experienced drivers have more practical experience that they percept the road environment before, after and on both sides of the road while driving, and they adapt to percept traffic surrounding efficiently in long-distance driving or long time driving (Li et al., 2013). However, novice drivers have less practical experience and they could not adapt to perceive traffic surrounding efficiently. ...
The visual ability factors of peripheral perception, visual reaction and visual pursuit ability are important for safe driving. Thus, this study explored the influence of driving experience on these visual ability factors. Sixty-five drivers, including novice and experienced drivers, participated in this study. The visual ability levels of the three factors were measured by the Vienna Test System. In addition, driver's awareness of the three factors' importance for safe driving was measured by questionnaire items. Results showed that out of the three factors, drivers' peripheral perception ability was correlated significantly with their driving experience. Experienced drivers had higher peripheral perception ability than that of novice drivers. In addition, compared with novice drivers, experienced drivers believed that peripheral perception was more important whereas visual reaction was less important. It is suggested that higher attention should be paid to peripheral perception in driving training and novice drivers should increase their importance awareness for this factor.
... FALKMER research shows that both urban roads and rural roads increase the driver's cognitive needs, but after the driver enters the city, they pay more attention to the outside world and require a higher level of cognitive attention [40]. Figure 12 illustrates a comparison of various works on rural roads [41], the Chinese urban trunk roads in [42], and the Chinese highways in [43]. Due to different AOI areas in different literature, AOI areas are divided according to the left, front, right, and other areas of fixation. ...
... Due to different AOI areas in different literature, AOI areas are divided according to the left, front, right, and other areas of fixation. Reference [42] and [43] conducted the real vehicle test, whereas Bao [41] performed the driving simulation test. Considering the situation in China and the United States, the attention of Chinese drivers to the front is more than twice that of American drivers by percentage points. ...
... areas of fixation. Reference [42] and [43] conducted the real vehicle test, whereas Bao [41] performed the driving simulation test. Considering the situation in China and the United States, the attention of Chinese drivers to the front is more than twice that of American drivers by percentage points. ...
In this study, an on-road driving experiment was designed to investigate the visual attention fixation and transition characteristics of drivers when they are under different cognitive workloads. First, visual attention was macroscopically analyzed through the entropy method. Second, the Markov glance one- and two-step transition probability matrices were constructed, which can study the visual transition characteristics under different conditions from a microscopic perspective. Results indicate that the fixation entropy value of male drivers is 23.08% higher than that of female drivers. Under the normal driving state, drivers’ fixation on in-vehicle systems is not continuous and usually shifts to the front and left areas quickly after such fixation. When under cognitive workload, drivers’ vision transition is concentrated only in the front and right areas. In mild cognitive workload, drivers’ sight trajectory is mainly focused on the distant front area. As the workload level increases, the transition trajectory shifts to the junction near the front and far sides. The current study finds that the difference between an on-road test and a driving simulation is that during the on-road driving process, drivers are twice as attentive to the front area than to the driving simulator. The research provides practical guidance for the improvement of traffic safety.
... Driving experience. Although there is quite some literature concerning the influence of experience on driving (Chapman & Underwood, 1998;Crundall & Underwood, 1998;Crundall, Underwood, & Chapman, 1999;Konstantopoulos et al., 2010;Lehtonen, Lappi, Koirikivi, & Summala, 2014;Li, Ji, Sun, Wang, & Yang, 2015;Mourant & Rockwell, 1972;Olsen, Lee, & Simons-Morton, 2007;, the eye movements of experienced and inexperienced drivers during real-world night driving have not yet been investigated. Nevertheless, there is one small clue to the influence of experience: The fixation distribution of the most experienced driver (200,000-300,000 km driving experience compared to 10,000-30,000 km of the other two drivers) tested by Cengiz et al. (2014) was very similar during daytime and night driving. ...
We exhaustively review the published research on eye movements during real-world night driving, which is an important field of research as fatal road traffic accidents at night outnumber fatal accidents during the daytime. Eye tracking provides a unique window into the underlying cognitive processes. The studies were interpreted and evaluated against the background of two descriptions of the driving task: Gibson and Crooks' (1938) description of driving as the visually guided selection of a driving path through the unobstructed field of safe travel; and Endsley's (1995) situation awareness model, highlighting the influence of drivers' interpretations and mental capacities (e.g., cognitive load, memory capacity, etc.) for successful task performance. Our review unveiled that drivers show expedient looking behavior, directed to the boundaries of the field of safe travel and other road users. Thus, the results indicated that controlled (intended) eye movements supervened, but some results could have also reflected automatic gaze attraction by salient but task-irrelevant distractors. Also, it is not entirely certain whether a wider dispersion of eye fixations during daytime driving (compared to night driving) reflected controlled and beneficial strategies, or whether it was (partly) due to distraction by stimuli unrelated to driving. We concluded by proposing a more fine-grained description of the driving task, in which the contribution of eye movements to three different subtasks is detailed. This model could help filling an existing gap in the reviewed research: Most studies did not relate eye movements to other driving performance measurements for the evaluation of real-world night driving performance.
This study presents the results of a driving experiment study on spatiotemporal characteristics of drivers’ fixation when entering a tunnel portal with different driving speeds. The study was performed during the daytime in a relatively long tunnel. Six experienced drivers were recruited to participate in the driving experiment. Experimental data of pupil area and fixation point position (from 200 m before the tunnel to the tunnel portal) were collected by non-intrusive eye-tracking equipment for three predetermined vehicle speeds (40 km/h, 60 km/h and 80 km/h). Fixation maps (color-coded maps showing distributed data) were created from fixation point position data to quantify visual behaviour changes. The results demonstrated that vehicle speed has a significant impact on pupil area and fixation zones. Fixation area and average pupil area had a significant negative correlation with vehicle speed during the daytime. Moreover, drivers concentrated more on the tunnel entrance portal, front road pavement and car control wheeling. The results revealed that the relationship between pupil area and vehicle speed fitted an exponential function. Limitations and future directions of the study are also discussed.
Road safety is a serious problem worldwide. Pedestrians, as the most vulnerable road users, deserve more attention. The aims of this study were to examine the validity of the Chinese version of the pedestrian behavior scale (CPBS) in both driver and non-driver samples, and to compare pedestrian behaviors between the two samples. In addition, we assessed the association of attention with pedestrian behaviors by exploring the relationships among CPBS, Mindful Attention Awareness Scale (MAAS) and Attention-Related Cognitive Errors Scale (ARCES). Two groups were assessed, including 302 members in the population with driving experience and 307 individuals in the non-driver group without driving experience. All participants completed the CPBS, MAAS, and ARCES, and provided sociodemographic parameters. The results showed that the CPBS had acceptable internal consistency and stability structure. More importantly, pedestrian behaviors were significantly different between drivers and non-drivers. Drivers reported significantly less transgressive and aggressive behaviors compared with non-drivers. As for the relationship between attention and pedestrian behavior, the MAAS score showed a significant negative correlation with aggressive behavior in the CPBS among drivers, while the ARCES score had significant positive correlations with all three CPBS factors. In non-drivers, the MAAS score was negatively correlated with aggressive behavior and positively associated with positive behavior; the ARCES score was positively correlated with aggressive behavior.
