Driver behaviour validity in driving simulators – Analysis of the moment of initiation of braking at urban intersections

Zöller, Ilka; Abendroth, Bettina; Bruder, Ralph

doi:10.1016/j.trf.2017.09.008

Cited by 40 publications

(19 citation statements)

References 11 publications

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“…In addition, driving simulator data have been used to research the risk assessment of autonomous vehicles [25]. However, driver behavior in the experimental environment differs from naturalistic driving [26,27]. e remaining method is to use naturalistic driving data to explore driving risk.…”

Section: Related Workmentioning

confidence: 99%

Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

Zhao

Dai

2021

Mobile Information Systems

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With the development of big data technologies, usage-based insurance (UBI) has received considerable attention from insurance companies. UBI products focus on identifying the relationship between the individual driver’s risk and online channel behavior variables from Internet of Vehicles (IoV) data. Although omnichannel information integration has promoted the development of many industries, it has not been used to improve the accuracy of driver risk classification models in insurance industries. This paper investigates the role of combining different channel variables in improving the classification of driver’s risk. Specifically, several models, including logistic regression and three different data mining techniques (neural networks, random forests, and support vector machines), augmented with driving behavior data based on the IoV and offline consumer behavior data collected from 4S (Sale, Spare part, Service, Survey) dealers, are applied to the classification model of risk. The empirical results show that the inclusion of online and offline channel data improves the different risk assessments; results also demonstrate the importance of offline consumer behavior variables in different models. These insights have important implications for insurance companies on UBI pricing strategy and cost management.

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Section: Related Workmentioning

confidence: 99%

Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

Zhao

Dai

2021

Mobile Information Systems

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“…Consequently, participants might choose a riskier behaviour when driving using a simulator because no consequences are attached to this behaviour such as a crash or a ticket. For instance, Zöller, Abendroth, and Bruder (2017) found that braking behaviour near intersections differed between real and simulated environments, where participants showed higher speeds in the simulator. Another issue is that access to a driving simulator and creating a data-generated traffic environment that is compatible with the study's aims might be quite costly.…”

Section: The Use Of Videos and Photographs To Study Drivers' Speed Preferencesmentioning

confidence: 99%

Effects of roadwork characteristics and drivers’ individual differences on speed preferences in a rural work zone

Steinbakk

Ulleberg

Sagberg

et al. 2019

Accident Analysis & Prevention

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Many drivers have experienced the frustration of driving in a work zone. I have heard about how the speed limits are too low, that road workers never work, and how road authorities should stop bothering drivers and delaying their journeys. I realised that new and better roads are something every driver wants, but few are willing to "pay the price" for it. When I started to wonder more about work zone issues, I became curious about addressing the complaints regarding low posted speed limits and what road authorities should do to increase their credibility. I tried to find studies on work zone safety from a drivers' perspective, and I was surprised when I discovered how little attention this issue had received in the scientific literature. Many studies only answered what drivers do at work zones, without answering why.

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“…Ilka evaluated the Time to Intersection (TTI) time interval between field and simulator at five simple urban intersections. The braking behavior near urban intersections differs between real and simulated experimental environments for drivers has been proved [20]. Although these methods have been successfully implemented in specific cases, they are still limited to be used to understand the context behind the behaviors and may need tremendous data storage resources.…”

Section: Introductionmentioning

confidence: 99%

Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized Intersections

Chen

Sun

2020

IEEE Access

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Semantic understanding of drivers' behavior features at intersections plays a pivotal role in the proper decision-making of a platoon. This paper presents a flexible framework to automatically extract the driver's driving features from observed temporal sequences of driving raw data and traffic light information. An approach, which contains two key sub-problems, is proposed to select the separated vehicles from the platoon in the vicinity of the intersection. Then, the first sub-problem, accurately capturing the drivers' driving behavior features under the impact of traffic lights, is addressed by using the Bayesian nonparametric approach, which could segment drivers' driving raw data temporal sequences into small analytically interpretable components (called driving primitives) without using prior knowledge. In addition, the extracted driving primitives are used to obtain the vehicle separation strategy (which is also the second sub-problem) by considering safety, efficiency, and energy consumption. Finally, 200 groups of raw data of human-driven vehicles approaching the intersection are used to validate the effectiveness of the proposed primitive-based framework. Experimental results demonstrate that the acceleration indeterminacy of separated vehicles could be decreased 37%-72% by segmenting the captured driving behavior features into 3×15 patterns. Moreover, the vehicle separation strategy could not only increase the efficiency, but also the safety, and the energy consumption could be decreased.

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Driver behaviour validity in driving simulators – Analysis of the moment of initiation of braking at urban intersections

Cited by 40 publications

References 11 publications

Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

Individual Driver Crash Risk Classification Based on IoV Data and Offline Consumer Behavior Data

Effects of roadwork characteristics and drivers’ individual differences on speed preferences in a rural work zone

Platoon Separation Strategy Optimization Method Based on Deep Cognition of a Driver’s Behavior at Signalized Intersections

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