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

Hu, Yuan, Shum, Hubert and Ho, Edmond (2020) Multi-task Deep Learning with Optical Flow Features for Self-Driving Cars. IET Intelligent Transport Systems. ISSN 1751-956X (In Press)

End_to_End_Learning_Motion_Representations_for_Self_Driving.pdf - Accepted Version

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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.

Item Type: Article
Additional Information: This paper is a postprint of a paper submitted to and accepted for publication in IET intelligent transport systems and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at the IET Digital Library.
Subjects: G400 Computer Science
G500 Information Systems
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 07 Dec 2020 11:51
Last Modified: 07 Dec 2020 12:00

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