Cost reduction for advanced driver assistance systems through hardware downscaling and deep learning

With the ever-growing population density, transportation sector has become haphazard causing accidents and fatalities. It can be reduced by the effective utilization of intelligent transportation systems (ITS). The applications of advanced driver-assistance systems (ADAS) and deep learning are trending globally with immense research potential and combined with ITS results into an intelligent system with decision-taking capabilities. High-priced driving modules are getting introduced day-by-day offering humongous processing power and computational capabilities. This research aims at utilizing the downscaled hardware for real-time vehicular and pedestrian detection using a deep learning algorithm. YOLO v3 deep learning network is incorporated with NVIDIA series of GTX 1060 for real-time object detection for assisting the ADAS systems. The system offers high precision (0.9618) of object detection in real time with high frame rate (74.36 fps). The comparative analysis between different GPU-based hardware modules and the proposed module has been carried out keeping in mind the Indian context of automobile usages. The work lays a solid foundation for carrying out research for transportation analysis based on ADAS and deep learning for ITS such as real-time congestion estimation and accident detection.

Automated summary

This research combines intelligent transportation systems and advanced driver-assistance systems with deep learning algorithms to achieve real-time detection of vehicles and pedestrians at a high frame rate with high precision. The work lays a foundation for future research on traffic analysis based on ADAS and deep learning.