Drivers’ visual-distracted take-over performance model and its application on adaptive adjustment of time budget

Li, Qingkun; Hou, Lian; Wang, Zhenyuan; Wang, Wenjun; Zeng, Chao; Yuan, Quan; Cheng, Bo

doi:10.1016/j.aap.2021.106099

Cited by 28 publications

(5 citation statements)

References 37 publications

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“…In [16], the authors studied a scenario involving a cone avoidance on a test track while the ego vehicle was moving at 60 km/h with which a regression model of TOT, termed reaction time was developed. Similarly using eye data, left and right lane merge scenarios were studied to model TOR time budget using regression in [17]. In both studies, the obtained coefficients are only valid for the studied cases because the characteristics and complexity of the scenarios were not indicated in the models.…”

Section: B Takeover Time Performance and Tor Time Budgetmentioning

confidence: 99%

Determination of Takeover Time Budget Based on Analysis of Driver Behavior

Tanshi

Söffker

2022

IEEE Open J. Intell. Transp. Syst.

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An automated driving system notifies a fallback-ready human driver to resume driving when critical operational and functional limits have been or are about to be exceeded. The point between notification and critical limit is the time budget. Previous studies indicate that interdependencies exist between takeover variables and the time budget leading to performance variations and sometimes accidents. It is known that drivers may delay or respond inadequately depending on the time budget. This contribution focuses on utilizing these interdependencies to evaluate the suitability of the time budget for specific scenarios. A 7 s time budget, eight scenarios, and three secondary tasks were studied in a driving simulator with 70 participants aged between 19 Yrs and 41 Yrs. The results indicate that drivers prioritize takeover effort in decreasing order of relative speed, traffic agents, and junctions. Furthermore, 7 s is suitable at a vehicle speed of 80 Km/h to 130 km/h, maximum two traffic agents and three junctions, and handsfree tasks but too high for lower complexities. Generally, the time budget is a sum of the takeover time and maneuver (e.g., lane change) response time. These results are relevant to safety and adaptive variation of the time budget for successful takeover.INDEX TERMS Conditional driving automation, driver assistance, driver behavior, safety and human factors, takeover time.

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Section: B Takeover Time Performance and Tor Time Budgetmentioning

confidence: 99%

Determination of Takeover Time Budget Based on Analysis of Driver Behavior

Tanshi

Söffker

2022

IEEE Open J. Intell. Transp. Syst.

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“…On the other hand, the driver's braking deceleration in the covert hazard scenario is greater and the vehicle speed is higher. The driver taking a greater braking depth will result in a greater change in vehicle speed, which will cause greater harm to other vehicles in the traffic flow [46]. This is mainly related to the poor situational awareness of drivers [47], who are unable to perceive potential hazards in the traffic environment promptly.…”

Section: Effects Of the Overt And Covert Hazard Scenarios On Drivers'...mentioning

confidence: 99%

Analysis of hazard perception characteristics based on driving behavior considering overt and covert hazard scenarios

Wei

Zhu

et al. 2022

PLoS ONE

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The drivers’ hazard perception plays an important role in preventing and reducing the occurrence of traffic accidents. In order to explore the drivers’ hazard perception and their behavioral characteristics in overt and covert hazards, hazardous events of three traffic conflict types (vehicle to vehicle, vehicle to cyclist and vehicle to pedestrian) were designed for overt and covert hazards based on the UC-win/Road driving simulation software, respectively. 35 drivers were organized to conduct the driving simulation tests. The data of driving behavior was collected when they were driving. A comparative analysis of drivers’ hazard perception ability and driving behavior characteristics was carried out for hazardous scenarios and traffic conflict types. The result has shown that drivers are more likely to take slowing measures or brake earlier in overt hazard scenarios to ensure safe driving. And drivers are more likely to be involved in collisions in covert hazard scenarios. The types of traffic conflict have a significant effect on the hazard perception ability of drivers (F = 5.92, p < 0.01). Drivers have the strongest hazard perception for cyclists and the weakest hazard perception for pedestrians. Traffic conflict types has a significant effect on drivers’ average braking depth (F = 32.31, p < 0.01), average speed (F = 13.78, p < 0.01), and average acceleration (F = 9.26, p < 0.01).

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“…The results demonstrate the high impact of distraction on driving performance in critical traffic situations and indicate a driving-related inhibition deficit in young and old drivers [21]. For the take-over of automated driving systems, the percentage of face orientation to distraction area and time to boundary at take-over timing were proposed by Li, Q. et al to accurately evaluate the degree of visual distraction based on merely face orientation under naturalistic non-driving related tasks and to evaluate take-over performance, respectively [22]. Grahn, H. and Kujala, T. argued that visual distraction by secondary in-car tasks is a major contributing factor in traffic incidents and studied the effects of touch screen size, user interface design and subtask boundaries on in-car tasks' visual demand and visual distraction potential [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Driver’s Visual Attention Characteristics and Their Emotional Influencing Mechanism under Different Cognitive Tasks

Liu

Wang

Chen

et al. 2022

IJERPH

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The visual attention system is the gateway to the human information processing system, and emotion is an important part of the human perceptual system. In this paper, the driver’s visual attention characteristics and the influences of typical driving emotions on those were explored through analyzing driver’s fixation time and identification accuracy to different visual cognitive tasks during driving. The results showed that: the increasing complexity of the cognitive object led to the improvement of visual identification speed. The memory and recall process increased drivers’ fixation time to cognitive objects, and the recall accuracy decreased with the increase in time interval. The increase in the number of cognitive objects resulted in the driver improving the visual identification speed for the cognitive object at the end of the sequence consciously. The results also showed that: the visual cognitive efficiency was improved in the emotional states of anger and contempt, and was decreased in the emotional states of surprise, fear, anxiety, helplessness and pleasure, and the emotional state of relief had no significant effect on the visual cognitive efficiency. The findings reveal the driver’s visual information processing mechanism to a certain extent, which are of great significance to understand the inner micro-psychology of driver’s cognition.

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Drivers’ visual-distracted take-over performance model and its application on adaptive adjustment of time budget

Cited by 28 publications

References 37 publications

Determination of Takeover Time Budget Based on Analysis of Driver Behavior

Determination of Takeover Time Budget Based on Analysis of Driver Behavior

Analysis of hazard perception characteristics based on driving behavior considering overt and covert hazard scenarios

Driver’s Visual Attention Characteristics and Their Emotional Influencing Mechanism under Different Cognitive Tasks

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