A personalized behavior learning system for human-like longitudinal speed control of autonomous vehicles
As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and be...
Saved in:
Main Authors: | Lu, Chao, Gong, Jianwei, Lv, Chen, Chen, Xin, Cao, Dongpu, Chen, Yimin |
---|---|
Other Authors: | School of Electrical and Electronic Engineering |
Format: | Article |
Language: | English |
Published: |
2020
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/137254 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Prioritized experience-based reinforcement learning with human guidance for autonomous driving
by: Wu, Jingda, et al.
Published: (2024) -
Towards autonomous driving : review and perspectives on configuration and control of four-wheel independent drive/steering electric vehicles
by: Hang, Peng, et al.
Published: (2021) -
Energy oriented driving behavior analysis and personalized prediction of vehicle states with joint time series modeling
by: Xing, Yang, et al.
Published: (2022) -
An integrated decision-making framework for highway autonomous driving using combined learning and rule-based algorithm
by: Xu, Can, et al.
Published: (2022) -
ACAV: A framework for automatic causality analysis in autonomous vehicle accident recordings
by: SUN, Huijia, et al.
Published: (2024)