Multi-Sensor Object Detection System for Real-Time Inferencing in ADAS

Advanced Driver Assistance Systems (ADAS) are designed to assist drivers in various driving scenarios, and the object detection system is a critical component of ADAS. This paper aims to develop and evaluate an object detection system using two cameras placed on the vehicle's front and rear sides for real-time inferencing in ADAS. The real-world data set is collected under different weather and lighting conditions to evaluate the object detection system. The object detection system is further optimized using the TensorRT engine to deploy the system on the in-vehicle computing unit, NVIDIA Jetson AGX Xavier. The object detection system achieved 18 fps to process two cameras simultaneously on the in-vehicle computing unit, NVIDIA Jetson AGX Xavier. The experimental findings of this study will be useful for researchers, engineers, and manufacturers in the field of ADAS and autonomous vehicles to improve road safety and reduce accidents.