The growing demand for self-driving vehicles in the market leads to investigate new optimized and higher performance technologies. Obstacle detection on the road is one of the key tasks for this purpose and it requires very short response times to avoid any possible risk. This work has the main objective to develop an AI model for FPGA-based devices. YOLOv5 is one of the most efficient models to satisfy the object detection task. The expected final pipeline is made of an input sensor to acquire the traffic images and an output display to visualize them with the vehicles surrounded by rectangles generated by the inference process.