Self-funded PhD Positions Available

Biomedical Engineering with Deep Learning based Video Analysis
Computer Vision with Deep Learning for Human Data Modelling
Deep Learning based Computer Graphics for Creating Virtual Characters

Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars

Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars

Abstract

The control of self-driving cars has received growing attention recently. While existing research shows promising results in vehicle control using video from a monocular dash camera, there has been very limited work on directly learning vehicle control from motion-based cues. Such cues are powerful features for visual representations, as they encode the per-pixel movement between two consecutive images, allowing a system to effectively map the features into the control signal. We propose a new framework that exploits the use of a motion-based feature known as optical flow extracted from the dash camera, and demonstrates that such a feature is effective in significantly improving the accuracy of the control signals. Our proposed framework involves two main components. The flow predictor, as a self-supervised deep network, models the underlying scene structure from consecutive frames and generates the optical flow. The controller, as a supervised multi-task deep network, predicts both steer angle and speed. We demonstrate that the proposed framework using the optical flow features can effectively predict control signals from a dash camera video. Using the Cityscapes dataset, we validate that the system prediction has errors as low as 0.0130 rad/s on steer angle and 0.0615 m/s on speed, outperforming existing research.

Publication

Yuan Hu, Hubert P. H. Shum and Edmond S. L. Ho,
"Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars",
IET Intelligent Transport Systems (ITS)
, 2020
Impact Factor: 2.568#

# Impact factors from the Journal Citation Reports 2021

Downloads

YouTube

References

BibTeX

@article{hu21multitask,
 author={Hu, Yuan and Shum, Hubert P. H. and Ho, Edmond S. L.},
 journal={IET Intelligent Transport Systems},
 title={Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars},
 year={2020},
 volume={14},
 number={13},
 pages={1845--1854},
 numpages={10},
 doi={10.1049/iet-its.2020.0439},
 issn={1751-956X},
 publisher={Institution of Engineering and Technology},
}

RIS

TY  - JOUR
AU  - Hu, Yuan
AU  - Shum, Hubert P. H.
AU  - Ho, Edmond S. L.
T2  - IET Intelligent Transport Systems
TI  - Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars
PY  - 2020
VL  - 14
IS  - 13
SP  - 1845
EP  - 1854
DO  - 10.1049/iet-its.2020.0439
SN  - 1751-956X
PB  - Institution of Engineering and Technology
ER  - 

Plain Text

Yuan Hu, Hubert P. H. Shum and Edmond S. L. Ho, "Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars," IET Intelligent Transport Systems, vol. 14, no. 13, pp. 1845-1854, Institution of Engineering and Technology, 2020.

Similar Research

 

 
 

Last updated on 01 August 2022, RSS Feeds