Modeling framework of human driving behavior based on Deep Maximum Entropy Inverse Reinforcement Learning-Reference-Cited by-同舟云学术

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Modeling framework of human driving behavior based on Deep Maximum Entropy Inverse Reinforcement Learning

Published:2024-10 Issue: Volume:652 Page:130052
ISSN:0378-4371
Container-title:Physica A: Statistical Mechanics and its Applications
language:en
Short-container-title:Physica A: Statistical Mechanics and its Applications

Author:

Wang Yongjie^ORCID,Niu Yuchen,Xiao Mei,Zhu Wenying,You Xinshang

Funder

National Natural Science Foundation of China

Hebei Provincial Natural Science Foundation

China Scholarship Council

Shaanxi Province Natural Science Foundation

Publisher

Elsevier BV

Reference52 articles.

1. Eco-driving control for connected and automated electric vehicles at signalized intersections with wireless charging;Zhang;Appl. Energy,2021

2. Multi-scale pedestrian intent prediction using 3D joint information as spatio-temporal representation;Ahmed;Expert Syst. Appl.,2023

3. Pedestrians’ road-crossing behavior towards eHMI-equipped autonomous vehicles driving in segregated and mixed traffic conditions;Song;Accid. Anal. Prev.,2023

4. Calibration of human driving behavior and preference using vehicle trajectory data;Dai;Transp. Res. C,2022

5. A deep learning framework for modelling left-turning vehicle behaviour considering diagonal-crossing motorcycle conflicts at mixed-flow intersections;Yao;Transp. Res. C,2021

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