Multi-modal sensor fusion-based deep neural network for end-to-end autonomous driving with scene understanding
This study aims to improve the performance and generalization capability of end-to-end autonomous driving with scene understanding leveraging deep learning and multimodal sensor fusion techniques. The designed end-to-end deep neural network takes as input the visual image and associated depth inf...
Saved in:
Main Authors: | Huang, Zhiyu, Lv, Chen, Xing, Yang, Wu, Jingda |
---|---|
Other Authors: | School of Mechanical and Aerospace Engineering |
Format: | Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/159714 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
IMPROVED CAMERA-RADAR FUSION FOR ACCURATE OBJECT DETECTION AND TRACKING IN AUTONOMOUS DRIVING
by: SHEN LYUYU
Published: (2024) -
Prioritized experience-based reinforcement learning with human guidance for autonomous driving
by: Wu, Jingda, et al.
Published: (2024) -
An analytic end-to-end collaborative learning algorithm
by: Li, Sitan, et al.
Published: (2024) -
SENSOR FUSION FOR DYNAMIC OBJECT DETECTION IN AUTONOMOUS DRIVING
by: CHRISTINA LEE DAO WEN
Published: (2021) -
End-to-end task-oriented dialogue: A survey of tasks, methods, and future directions
by: QIN, Libo, et al.
Published: (2023)