Advances in information and signal processing, driven by artificial intelligence techniques and recent breakthroughs in deep learning, have significantly impacted autonomous driving by enhancing safety and reducing the dependence on human intervention. Generally, prevailing ADASs (advanced driver assistance systems) incorporate costly components, making them financially unattainable for a substantial portion of the population. This paper proposes a solution: an embedded system designed for real-time pedestrian and priority sign detection, offering affordability and universal applicability across various vehicles. The suggested system, which comprises two cameras, an NVIDIA Jetson Nano B01 low-power edge device and an LCD (liquid crystal system) display, ensures seamless integration into a vehicle without occupying substantial space and provides a cost-effective alternative. The primary focus of this research is addressing accidents caused by the failure to yield priority to other drivers or pedestrians. Our study stands out from existing research by concurrently addressing traffic sign recognition and pedestrian detection, concentrating on identifying five crucial objects: pedestrians, pedestrian crossings (signs and road paintings separately), stop signs, and give way signs. Object detection was executed using a lightweight, custom-trained CNN (convolutional neural network) known as SSD (Single Shot Detector)-MobileNet, implemented on the Jetson Nano. To tailor the model for this specific application, the pre-trained neural network underwent training on our custom dataset consisting of images captured on the road under diverse lighting and traffic conditions. The outcomes of the proposed system offer promising results, positioning it as a viable candidate for real-time implementation; its contributions are noteworthy in advancing the safety and accessibility of autonomous driving technologies.

Due to the rapid advancement of computer vision and deep learning technologies, advanced driver assistance systems (ADAS) have become widespread, aiming to enhance driving safety and decrease the number of traffic accidents. Although modern cars generally possess integrated ADAS systems, other vehicles do not. This study introduces a portable and image-based ADAS system that utilizes a deep learning technique, specifically convolutional neural networks (CNN), for traffic priority sign and pedestrian recognition. The proposed CNN was designed to maintain accuracy while minimizing model size, making it suitable for embedded implementation while achieving high performance and real-time processing. The SSD approach involves creating object proposals and conducting classification using a single CNN. The model was trained and tested on own dataset, containing a total of 740 images taken under various lighting and traffic conditions to better represent the environment in which this system will be used.