Embedded Intelligent ADAS Car Prototype Using Raspberry Pi and YOLOv12n

Abstract

"This paper presents the design, quantization, and deployment of an embedded Advanced Driver Assistance System (ADAS) prototype on a Raspberry Pi 3, utilizing a custom-trained YOLOv12n object detection model. The system achieves real-time multi-class object detection and autonomous control of a test vehicle within the resource constraints of low-power edge devices. To optimize performance, posttraining INT8 quantization was applied to YOLOv12n, resulting in over 65% reduction in model size and nearly 3× faster inference, with minimal accuracy loss. Object detection outputs are combined with ultrasonic sensor measurements to enable obstacle-aware vehicle control, including braking and navigation. Lightweight classical computer vision methods facilitate lane detection for lane-keeping functionality. Additionally, a Flask-based dashboard streams detection overlays and telemetry data for monitoring. The deployed system operates at approximately 6 frames per second on the Raspberry Pi 3 with a responsive control latency of around 210 ms, demonstrating the practical feasibility of deploying deep learning–based ADAS functions on low-cost, resource-constrained embedded platforms. This work highlights the effectiveness of quantization techniques in enabling real-time perception for embedded autonomous driving applications."