Learning From Real-World Trajectories: A Hybrid Genetic Algorithm and Reinforcement Learning Approach for Vulnerable Road User Model Calibration [brochure]

doi:10.21949/1qgs-s409

i

Learning From Real-World Trajectories: A Hybrid Genetic Algorithm and Reinforcement Learning Approach for Vulnerable Road User Model Calibration [brochure]

2026-01-01
By United States. Department of Transportation. Federal Highway Administration

File Language:

English

Details

Corporate Creators:

United States. Department of Transportation. Federal Highway Administration
Corporate Contributors:

United States. Department of Transportation. Federal Highway Administration. Turner-Fairbank Highway Research Center
Subject/TRT Terms:

Genetic Algorithms Learning Simulation Vehicle Trajectories Vulnerable Road Users
Publication/ Report Number:

FHWA-HRT-26-033
DOI:

https://doi.org/10.21949/1qgs-s409
Resource Type:

Booklet/Pamphlet
Right Statement:

Public Domain
Geographical Coverage:

United States
Corporate Publisher:

United States. Department of Transportation. Federal Highway Administration
Abstract:

Microscopic simulation models require accurate calibration to reflect real-world dynamics. Vulnerable road user (VRU) model calibration is challenging due to: Complex interactions and behaviors, Mode-specific interactions, Diversity in infrastructure and the existence of shared spaces. Existing calibration methods, relying solely on numeric loss functions, often fail to capture realistic, human-perceived behavior.
Format:

PDF
Funding:

693JJ324C000004
Collection(s):

Federal Highway Administration
Main Document Checksum:

urn:sha-512:343240213b80d58b1b55c47ac10ca6b50667a25c447d97e8bbb14c7f0239414d02e762f312aae21829aceb13dfb5a54b8b3b3a4d1a646ca0519f6d2a3cc646ff
Download URL:

https://rosap.ntl.bts.gov/view/dot/88973/dot_88973_DS1.pdf
File Type:

[PDF - 864.34 KB ]

File Language:

English

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