Optimizing longitudinal control model parameters of connected and automated vehicles using empirical trajectory data of human drivers in risky car-following scenarios

Xing, Lu; Wu, Dan; Tang, You-yi; Li, Ye

doi:10.1007/s11771-023-5413-6

J. Cent. South Univ.

2023

DOI: 10.1007/s11771-023-5413-6

|View full text |Cite

Optimizing longitudinal control model parameters of connected and automated vehicles using empirical trajectory data of human drivers in risky car-following scenarios

Lu Xing,

Dan Wu,

You-yi Tang

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model

Wu,

Lee,

et al. 2024

Accident Analysis & Prevention

View full text Add to dashboard Cite

A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model

Wu,

Lee,

et al. 2024

Accident Analysis & Prevention

View full text Add to dashboard Cite

Identifying drivers’ perception-reaction time (PRT) in car-following processes via two different methods using vehicle trajectory data

Wu,

2024

Safety and Reliability

View full text Add to dashboard Cite

Predicting vehicle trajectory of non-lane based driving behaviour with Temporal Fusion Transformer

Long,

Fei,

Xing

et al. 2024

Transportmetrica B: Transport Dynamics

View full text Add to dashboard Cite

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

customersupport@researchsolutions.com

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

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

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.

Optimizing longitudinal control model parameters of connected and automated vehicles using empirical trajectory data of human drivers in risky car-following scenarios

Cited by 3 publications

References 33 publications

A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model

A surrogate model-based approach for adaptive selection of the optimal traffic conflict prediction model

Identifying drivers’ perception-reaction time (PRT) in car-following processes via two different methods using vehicle trajectory data

Predicting vehicle trajectory of non-lane based driving behaviour with Temporal Fusion Transformer

Contact Info

Product

Resources

About