Data driven interaction-aware trajectory prediction for urban driving
As an important tool to promote the development of intelligent transportation systems, autonomous driving can effectively reduce human-induced traffic accidents, relieve traffic congestion, and reduce environmental pressure under certain conditions. It is a key technology that needs to be developed...
Saved in:
Main Author: | Hu, Zongyao |
---|---|
Other Authors: | Lyu Chen |
Format: | Thesis-Master by Coursework |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/163997 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Data-driven research on vehicles' risky lane-changing manoeuvre
by: Chen, Tianyi
Published: (2021) -
Spatio-temporal interaction aware and trajectory distribution aware graph convolution network for pedestrian multimodal trajectory prediction
by: Wang, Ruiping, et al.
Published: (2023) -
Off-road fitness-to-drive assessment using driving simulator (Effect of experience on fitness-to-drive)
by: Chan, Peggy Myat Kay Khine
Published: (2016) -
Deep learning-based traffic flow prediction and traffic management system for urban transportation networks
by: Zhao, Han
Published: (2023) -
Driving skills retention of young drivers after period of driving inactivity
by: Upahita, Dwi Phalita
Published: (2018)