FPGA–STM32-Embedded Vision and Control Platform for ADAS Development on a 1:5 Scale Vehicle

This paper presents the design, development, and experimental validation of a low-cost, modular, and scalable Advanced Driver Assistance System (ADAS) platform intended for research and educational purposes. The system integrates embedded computer vision and electronic control using an FPGA for accelerated real-time image processing and an STM32 microcontroller for sensor data acquisition and actuator management. The YOLOv3-Tiny model is implemented to enable efficient pedestrian and vehicle detection under hardware constraints, while additional vision algorithms are used for lane line detection, ensuring a favorable trade-off between accuracy and processing speed. The platform is deployed on a 1:5 scale gasoline-powered vehicle, offering a safe and cost-effective testbed for validating ADAS functionalities, such as lane tracking, pedestrian and vehicle identification, and semi-autonomous navigation. The methodology includes the integration of a CMOS camera, an FPGA development board, and various sensors (LiDAR, ultrasonic, and Hall-effect), along with synchronized communication protocols to ensure real-time data exchange between vision and control modules. A wireless graphical user interface (GUI) enables remote monitoring and teleoperation. Experimental results show competitive detection accuracy—exceeding 94% in structured environments—and processing latencies below 70 ms per frame, demonstrating the platform’s effectiveness for rapid prototyping and applied training. Its modularity and affordability position it as a powerful tool for advancing ADAS research and education, with high potential for future expansion to full-scale autonomous vehicle applications.