no code implementations • 27 Mar 2024 • Mingxing Peng, Xusen Guo, Xianda Chen, Meixin Zhu, Kehua Chen, Hao, Yang, Xuesong Wang, Yinhai Wang
To the best of our knowledge, this is the first attempt to utilize LLMs for predicting lane change behavior.
no code implementations • 24 Oct 2023 • Di Chen, Meixin Zhu, Hao Yang, Xuesong Wang, Yinhai Wang
The primary objective of this paper is to review current research efforts and provide a futuristic perspective that will benefit future developments in the field.
no code implementations • 30 Aug 2023 • Xu Han, Xianda Chen, Meixin Zhu, Pinlong Cai, Jianshan Zhou, Xiaowen Chu
The experimental results illustrate that EnsembleFollower yields improved accuracy of human-like behavior and achieves effectiveness in combining hybrid models, demonstrating that our proposed framework can handle diverse car-following conditions by leveraging the strengths of various low-level models.
no code implementations • 12 Aug 2023 • Kehua Chen, Xianda Chen, Zihan Yu, Meixin Zhu, Hai Yang
The growing popularity of deep learning has led to the development of numerous methods for trajectory prediction.
1 code implementation • 25 May 2023 • Xianda Chen, Meixin Zhu, Kehua Chen, Pengqin Wang, Hongliang Lu, Hui Zhong, Xu Han, Yinhai Wang
To address this gap and promote the development of microscopic traffic flow modeling, we establish a public benchmark dataset for car-following behavior modeling.
no code implementations • 7 Mar 2023 • Pengqin Wang, Meixin Zhu, Shaojie Shen
It models the probability distribution of the environment dynamics and reward function to capture aleatoric uncertainty and treats epistemic uncertainty as a learnable noise parameter.
no code implementations • 4 Feb 2022 • Meixin Zhu, Simon S. Du, Xuesong Wang, Hao, Yang, Ziyuan Pu, Yinhai Wang
Through cross-attention between encoder and decoder, the decoder learns to build a connection between historical driving and future LV speed, based on which a prediction of future FV speed can be obtained.
no code implementations • 2 Aug 2020 • Ruimin Ke, Zhiyong Cui, Yanlong Chen, Meixin Zhu, Hao Yang, Yinhai Wang
It is among the first efforts in applying edge computing for real-time traffic video analytics and is expected to benefit multiple sub-fields in smart transportation research and applications.
no code implementations • 27 Oct 2019 • Meixin Zhu, Jingyun Hu, Ziyuan Pu, Zhiyong Cui, Liangwu Yan, Yinhai Wang
This study developed a traffic sign detection and recognition algorithm based on the RetinaNet.
no code implementations • 13 Oct 2019 • Meixin Zhu, Jingyun Hu, Hao, Yang, Ziyuan Pu, Yinhai Wang
Also, results of the multinomial logit model show that (1) an increase in travel cost would decrease the utility of all the transportation modes; (2) people are less sensitive to the travel distance for the metro mode or a multi-modal option that containing metro, i. e., compared to other modes, people would be more willing to tolerate long-distance metro trips.
1 code implementation • 29 Jan 2019 • Meixin Zhu, Yinhai Wang, Ziyuan Pu, Jingyun Hu, Xuesong Wang, Ruimin Ke
A model used for velocity control during car following was proposed based on deep reinforcement learning (RL).
no code implementations • 3 Jan 2019 • Meixin Zhu, Xuesong Wang, Yinhai Wang
This study demonstrates that reinforcement learning methodology can offer insight into driver behavior and can contribute to the development of human-like autonomous driving algorithms and traffic-flow models.