A Vehicle Stability Study of Combined Alignments on Mountainous Freeways: A Driving Simulator-Based Approach

Liu, Jiao; Wang, Xuesong; Zhang, Rongrui

doi:10.1061/9780784479896.141

Cictp 2016 2016

DOI: 10.1061/9780784479896.141

|View full text |Cite

A Vehicle Stability Study of Combined Alignments on Mountainous Freeways: A Driving Simulator-Based Approach

Jiao Liu

Xuesong Wang

Rongrui Zhang

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is because drivers are constantly required to decide on the appropriate acceleration (1)(2)(3)(4). To date, much effort has been put into evaluating longitudinal and lateral vehicle behavior or driver workload for road alignments to ensure driving safety (5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15). Driving speed, lane position, electroencephalogram (EEG), and the NASA task load index (NASA-TLX) were frequently used as the main indicators for evaluating the longitudinal and lateral driving behavior or driver workload according to the changes in road alignment or traffic conditions.…”

mentioning

confidence: 99%

Evaluating Driving Safety of Road Alignment Conditions by Predicted Driver’s Psychophysiological Workload Using Vehicle Maneuvering Data

Jang

Jung

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Evaluating driving safety according to road alignment conditions is a fundamental for safe road design and traffic operations to prevent traffic crashes. A significant intrinsic factor affecting driving safety is the driver’s psychophysiological workload generated in response to various external environmental changes during road driving. Electroencephalogram (EEG) is a well-known measure to evaluate the driver workload resulting from the response to the internal and external stimuli experienced while driving vehicles. However, the use of EEG in research of the transportation engineering domain requires not only high-cost experiments but also great efforts in data analysis. To address these issues, this study developed a promising method to predict the psychophysiological workload affecting driving safety using vehicle maneuvering data. A total of 30 drivers participated in driving simulation experiments using a high-fidelity motion-based driving simulator to obtain both psychophysiological workload data and vehicle maneuvering data to be used for developing the methodology. The proposed methodology consists of two parts: the prediction of the level of psychophysiological workload using a K-nearest neighbor (KNN) algorithm and the derivation of the alertness risk index (ARI) based on the spatiotemporal aggregation of the prediction results. Additionally, evaluating driving safety by different road conditions is conducted based on the proposed methodology.

show abstract

mentioning

confidence: 99%

Evaluating Driving Safety of Road Alignment Conditions by Predicted Driver’s Psychophysiological Workload Using Vehicle Maneuvering Data

Jang

Jung

et al. 2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

Driver behaviour and experience around automated freight vehicle platoons

Yang

Park

Abdel-Aty

2023

Proceedings of the Institution of Civil Engineers - Transport

View full text Add to dashboard Cite

Autonomous vehicles are becoming more common, and the market size is growing. In the truck industry, deploying a platoon of autonomous vehicles is emerging as a response to existing safety and environmental problems. However, before autonomous technology can be applied to all vehicles in a traffic flow, mixed traffic containing both autonomous and non-autonomous vehicles is inevitable. Therefore, this study analysed the behaviour of non-autonomous vehicles at the inflow and outflow of highways to understand platoon operation management in mixed traffic conditions by using driving simulator and NASA-TLX survey. Unlike previous studies, this study considered an environment in which freight vehicle platoons occupy a designated lane and examined the vehicle driving characteristics and driver psychology as non-autonomous vehicles entered and exited the highway. Statistical methods such as ANOVA and MANOVA were used to analyse the difference according to the situation. As a result, in entry, platoon had an effect to driver and especially platoon size had a larger effect than inter-platoon spacing. However, among drivers with driving experience above a certain level, the platooning parameters did not have significant effects on the driving. The results of this study provide basic information about the operation and management of a platoon of autonomous vehicles on mixed-traffic highways.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

A Vehicle Stability Study of Combined Alignments on Mountainous Freeways: A Driving Simulator-Based Approach

Cited by 2 publications

References 5 publications

Evaluating Driving Safety of Road Alignment Conditions by Predicted Driver’s Psychophysiological Workload Using Vehicle Maneuvering Data

Evaluating Driving Safety of Road Alignment Conditions by Predicted Driver’s Psychophysiological Workload Using Vehicle Maneuvering Data

Driver behaviour and experience around automated freight vehicle platoons

Contact Info

Product

Resources

About