Robust Vision-Based Daytime Vehicle Brake Light Detection Using Two-Stage Deep Learning Model

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.