Paper Info
Title
Robust Vision-Based Daytime Vehicle Brake Light Detection Using Two-Stage Deep Learning Model
Abstract
Today's ADAS functions can be divided into active control, early warning and other auxiliary three major categories, including Adaptive Cruise Control (ACC), Autonomous Emergency Braking (AEB), Forward Collision Warning (FCW), which uses radar and some sensors to measure the distance between itself and the front, and uses this as a parameter for analysis. However, in addition to the distance, if it is possible to know the information of the vehicle in front of the vehicle in a timely manner and transmit the real-time vehicle information of the vehicle in front through the Internet of Vehicle, it will be possible to accurately determine the driving conditions of the surrounding vehicles. Therefore, we proposed a daytime vehicle brake light detection system that uses a single image as the input without object tracking. The experimental results show that our proposed system can achieve very high resolution under various weather conditions.
Year
DOI
Venue
2019
10.1145/3361758.3361778
Proceedings of the 3rd International Conference on Big Data and Internet of Things
Keywords
Field
DocType
Brake light detection, daytime, deep learning
Radar,Warning system,Cruise control,Computer science,Daytime,Collision,Real-time computing,Video tracking,Artificial intelligence,Deep learning,The Internet
Conference
ISBN
Citations 
PageRank 
978-1-4503-7246-6
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Duan-Yu Chen129628.79
Tsu-Yang Lin200.34
Guo-Ruei Chen340.77