Traffic Forecasting Using Vehicle-to-Vehicle Communication
Abstract
Vehicle-to-vehicle (V2V) communication is utilized in order to provide real-time on-board traffic predictions. A hybrid approach is proposed where physics based models are supplemented with deep learning. A recurrent neural network is used to improve the accuracy of predictions given by first principle models. Our hybrid model is able to predict the velocity of individual vehicles up to 40 seconds into the future with improved accuracy over physics based baselines. A comprehensive study is conducted to evaluate different methods of integrating physics with deep learning.
Cite
Text
Wong et al. "Traffic Forecasting Using Vehicle-to-Vehicle Communication." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.Markdown
[Wong et al. "Traffic Forecasting Using Vehicle-to-Vehicle Communication." Proceedings of the 3rd Conference on Learning for Dynamics and Control, 2021.](https://mlanthology.org/l4dc/2021/wong2021l4dc-traffic/)BibTeX
@inproceedings{wong2021l4dc-traffic,
title = {{Traffic Forecasting Using Vehicle-to-Vehicle Communication}},
author = {Wong, Steven and Jiang, Lejun and Walters, Robin and Molnár, Tamás G. and Orosz, Gábor and Yu, Rose},
booktitle = {Proceedings of the 3rd Conference on Learning for Dynamics and Control},
year = {2021},
pages = {917-929},
volume = {144},
url = {https://mlanthology.org/l4dc/2021/wong2021l4dc-traffic/}
}