학술논문

Toward Sustainable Transportation: Robust Lane-Change Monitoring With a Single Back View Cabin Camera
Document Type
Periodical
Source
IEEE Transactions on Intelligent Transportation Systems IEEE Trans. Intell. Transport. Syst. Intelligent Transportation Systems, IEEE Transactions on. 24(12):15414-15424 Dec, 2023
Subject
Transportation
Aerospace
Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Wheels
Vehicles
Monitoring
Videos
Cameras
Feature extraction
Roads
Sustainable development
Intelligent vehicles
Safety
Computer vision
Sustainable transportation
safety monitoring
lane-change detection
cabin camera
computer vision
Language
ISSN
1524-9050
1558-0016
Abstract
The risk of death and injury from traffic crashes has been universally recognized as one of the most serious threats to sustainable development. Among all the factors in traffic crashes, aggressive driving in which the driver usually makes excessive lane changes to overtake other vehicles is prevalent. As such, monitoring lane changes and providing real-time warnings is beneficial for improving transportation sustainability. This article presents BackWatch, a novel vehicle-mounted sensing system that uses a back view cabin camera monitoring the steering wheel rotations to track lane-change events. BackWatch consists of an encoder network to extract essential visual features of steering wheel rotations, and an inference network incorporating the visual and GPS speed features to recognize the resulting lane changes. Our system does not rely on precise coordinate alignment between the monitoring device and the vehicle, nor the wearables worn by the driver, and is robust against different drivers, vehicles, driving speeds, and environmental settings. We evaluate the system based on 16 hours of real-world on-road driving data collected from three pairs of cars and drivers under different traffic and environmental conditions. The results show that BackWatch achieves 0.952 of precision and 0.981 of recall on the detection of lane changes.