Toward safe and smart mobility : energy-aware deep learning for driving behavior analysis and prediction of connected vehicles
Connected automated driving technologies have shown tremendous improvement in recent years. However, it is still not clear how driving behaviors and energy consumption correlate with each other and to what extent these factors related to connected vehicles can influence the motion prediction performa...
Saved in:
Main Authors: | Xing, Yang, Lv, Chen, Mo, Xiaoyu, Hu, Zhongxu, Huang, Chao, Hang, Peng |
---|---|
其他作者: | School of Mechanical and Aerospace Engineering |
格式: | Article |
語言: | English |
出版: |
2021
|
主題: | |
在線閱讀: | https://hdl.handle.net/10356/147440 |
標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
機構: | Nanyang Technological University |
語言: | English |
相似書籍
-
Heterogeneous graph social pooling for interaction-aware vehicle trajectory prediction
由: Mo, Xiaoyu, et al.
出版: (2024) -
Towards autonomous driving : review and perspectives on configuration and control of four-wheel independent drive/steering electric vehicles
由: Hang, Peng, et al.
出版: (2021) -
Modeling the proactive driving behavior of connected vehicles : a cell-based simulation approach
由: Zhu, Feng, et al.
出版: (2020) -
Trajectory prediction for autonomous driving using deep learning approach
由: Zhang, Zihan
出版: (2024) -
Interactive prediction and decision-making for autonomous vehicles: online active learning with traffic entropy minimization
由: Zhang, Yiran, et al.
出版: (2025)