Abstract
ADAS (Advanced Driver Assistance System) has become a vital part of the driving experience. In recent years, there have been several advancements in ADAS technology such as parking assistance and lane detection. The proposed work presents a real-time Driver assistance framework by implementing the state-of-the-art object detection algorithm YOLOv4. This paper provides a comparison between and other state-of-the-art object detectors. Comparison is done based on mean average precision (mAP) and frames per second (FPS) on three different datasets and one standard dataset. YOLOv4 proves to be faster and more accurate than the other object detection algorithms in the comparison. This framework is used to build an application which helps users make better decisions on the road. This application consists of a simple user interface that displays alerts and warnings.
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Murthy, J.S., Chitlapalli, S.S., Anirudha, U.N., Subramanya, V. (2022). A Real-Time Driver Assistance System Using Object Detection and Tracking. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2022. Communications in Computer and Information Science, vol 1614. Springer, Cham. https://doi.org/10.1007/978-3-031-12641-3_13
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