Quantifying effects of reverse linear perspective as a visual cue on vehicle and platoon crash risk variations in car-following using path analysis

Ding, Naikan; Lu, Zhaoyou; Jiao, Nisha; Liu, Zhiguang; Lu, Linsheng

doi:10.1016/j.aap.2021.106215

Cited by 12 publications

(4 citation statements)

References 57 publications

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“…Car following scenario is mainly used to test the car following the performance of vehicles. In this scenario, there is basically no horizontal conflict between vehicles, and the driver only needs to take the car following behavior [5]. When drivers are changing lanes, overtaking, turning, they should pay full attention to the main driving tasks.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario

Wang

Guo

et al. 2022

IJERPH

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The user interface of vehicle interaction systems has become increasingly complex in recent years, which makes these devices important factors that contribute to accidents. Therefore, it is necessary to study the impact of dynamic complexity on the carrying capacity of secondary tasks under different traffic scenarios. First, we selected vehicle speed and vehicle spacing as influencing factors in carrying out secondary tasks. Then, the average single scanning time, total scanning time, and scanning times were selected as evaluation criteria, based on the theories of cognitive psychology. Lastly, we used a driving simulator to conduct an experiment under a car-following scenario and collect data on scanning behavior by an eye tracker, to evaluate the performance of the secondary task. The results show that the relationship between the total scanning time, scanning times, and the vehicle speed can be expressed by an exponential model, the relationship between the above two indicators and the vehicle spacing can be expressed by a logarithmic model, and the relationship with the total number of icons can be expressed by a linear model. Combined with the above relationships and the evaluation criteria for driving secondary tasks, the maximum number of icons at different vehicle speeds and vehicle spacings can be calculated to reduce the likelihood of accidents caused by attention overload.

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Section: Introduction 1backgroundmentioning

confidence: 99%

The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario

Wang

Guo

et al. 2022

IJERPH

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“…When analyzing the influence of road traffic environment on the driver in the car-following situation, the driving state of the car-following lane should be emphatically analyzed. (2) We determine the early warning value according to TTC. When the complexity of road traffic environment is [0.75, 0.85], the early warning system is prompted for early warning.…”

Section: Discussionmentioning

confidence: 99%

“…There are several driving states of vehicles on the road, which are car-following, overtaking, lane change and turning. The car-following behavior is a common state of vehicles in urban roads [2]. In the process of driving on the road, the driving behavior of the rear vehicle following the adjacent front vehicle in the lane and restricted by the front vehicle is called car-following behavior.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Quantitative Study on Road Traffic Environment Complexity under Car-Following Condition

Liu

Chen

et al. 2022

Sustainability

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With the development of the drive of electronic communication technology, the driving assistance system that perceives the external traffic environment has developed rapidly. However, when quantifying the complexity of the road traffic environment without fully considering the driving characteristics and subjective feelings, the false alarm rate of the driving warning system increases and affects the early warning effect. In order to more accurately quantify the complexity of the road traffic environment, we analyzed the impact of road traffic environment changes on drivers under the condition of car-following. Firstly, we selected the influencing factors of the traffic environment complexity, such as the driving operation indicators, the vehicle driving status indicators and the road environmental indicators. The weight calculation model of each influence factor is established based on the principal component analysis method. Secondly, the driver’s reaction time during car-following is used as the quantitative index of road traffic environment complexity. The quantitative model of road traffic environment complexity is constructed combined with the weight of road traffic environment complexity. Finally, the driving simulation experiment is designed to verify the complexity quantification model of the road traffic environment. The road traffic environment complexity value calculated in our study is better than the TTC, and the early-warning threshold is raised by 2–5%. The research conclusion can provide a basis for the design of the car alarm system.

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“…Connected and Automated Vehicles (CAVs) are vital for sustainable transportation, as they improve road safety [1,2], reduce traffic incidents [3,4], alleviate congestion [5], and lower carbon emissions [6]. Platoon control, a key technology in CAVs, aims to enable multiple vehicles to form a closely knit convoy and maintain short distances while driving on highways, and improve road utilization and fuel efficiency, leading to economic and environmental advantages [7].…”

Section: Introductionmentioning

confidence: 99%

The Sustainability and Energy Efficiency of Connected and Automated Vehicles from the Perspective of Public Acceptance towards Platoon Control

Li,

et al. 2024

Sustainability

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This study examines the public acceptance of platoon control for connected and automated vehicles (CAVs) and analyzes it from a sustainability perspective. A questionnaire survey was conducted targeting a diverse social group to collect data on public attitudes, acceptability, and factors related to the environment, social responsibility, personal economy, and behavior. Factor analysis was then performed to reduce these data into three factors: “attitudes and acceptance”, “environment and social responsibility”, and “personal economy and behavior”. Further, multiple regression analysis was employed to investigate the relationship between these three factors and the willingness to accept platooning control, as well as the willingness to actively adopt it. The findings indicate that the ”attitude and acceptance” factor significantly influences the public’s acceptance of platoon control. Moreover, the ”environment and social responsibility” and ”personal economy and behavior” factors also have a certain influence on decision-making. This study not only contributes to understanding public attitudes towards CAVs’ platoon control but also explores how to promote the development of sustainable intelligent transportation systems. By gaining a better understanding of public attitudes, policymakers and relevant stakeholders can take measures to enhance the acceptance of CAVs, thereby driving the sustainable development of transportation systems.

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Quantifying effects of reverse linear perspective as a visual cue on vehicle and platoon crash risk variations in car-following using path analysis

Cited by 12 publications

References 57 publications

The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario

The Effects of Dynamic Complexity on Drivers’ Secondary Task Scanning Behavior under a Car-Following Scenario

Quantitative Study on Road Traffic Environment Complexity under Car-Following Condition

The Sustainability and Energy Efficiency of Connected and Automated Vehicles from the Perspective of Public Acceptance towards Platoon Control

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