Long-term prediction for high-resolution lane-changing data using temporal convolution network-Reference-Cited by-同舟云学术

Long-term prediction for high-resolution lane-changing data using temporal convolution network

Published:2021-07-22 Issue: Volume: Page:1-15
ISSN:2168-0566
Container-title:Transportmetrica B: Transport Dynamics
language:en
Short-container-title:Transportmetrica B: Transport Dynamics

Author:

Zhang Yue¹^ORCID,Zou Yajie¹,Tang Jinjun²,Liang Jian²

Affiliation:

1. Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai, People’s Republic of China

2. School of Traffic & Transportation Engineering, Central South University, Changsha, People’s Republic of China

Funder

National Natural Science Foundation of China

Shanghai Science and Technology Committee

Fundamental Research Funds for the Central Universities

Publisher

Informa UK Limited

Subject

Transportation,Modelling and Simulation,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/21680566.2021.1950072

Reference38 articles.

1. Vehicle Lane Changing Model Based on Genetic Algorithm and Random Utility

2. Bai, S., J. Z. Kolter, and V. Koltun. 2018. “An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.” arXiv preprint arXiv 1803: 01271.

3. Personalized Driver/Vehicle Lane Change Models for ADAS

4. Predictive human operator model to be utilized as a controller using linear, neuro-fuzzy and fuzzy-ARX modeling techniques

5. Chen, Y. 2018. “Learning-Based Lane Following and Changing Behaviors for Autonomous Vehicle.” Master’s thesis. Carnegie Mellon University, Pittsburgh, PA.

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