Despite the advancement of advanced driver assistance systems (ADAS) and autonomous driving systems, surpassing the threshold of level 3 of driving automation remains a challenging task. Level 3 of driving automation requires assuming full responsibility for the vehicle's actions, necessitating the acquisition of safer and more interpretable cues. To approach level 3, we propose a novel method for detecting driving vehicles and their brake light status, which is a crucial visual cue relied upon by human drivers. Our proposal consists of two main components. First, we introduce a fast and accurate one-stage brake light status detection network based on YOLOv8. Through transfer learning using a custom dataset, we enable YOLOv8 not only to detect the driving vehicle, but also to determine its brake light status. Furthermore, we present the publicly available custom dataset, which includes over 11,000 forward images along with manual annotations. We evaluate the performance of our proposed method in terms of detection accuracy and inference time on an edge device. The experimental results demonstrate high detection performance with an mAP50 (mean average precision at IoU threshold of 0.50) ranging from 0.766 to 0.793 on the test dataset, along with a short inference time of 133.30 ms on the Jetson Nano device. In conclusion, our proposed method achieves high accuracy and fast inference time in detecting brake light status. This contribution effectively improves safety, interpretability, and comfortability by providing valuable input information for ADAS and autonomous driving technologies.
Keywords: ADAS; YOLOv8; autonomous; brake light status; one-stage detection.