Motorcycle accidents contribute significantly to road traffic fatalities worldwide. Advanced Rider Assistance Systems are systems designed to improve safety by providing real-time monitoring, collision detection, and adaptive control. However, implementing these systems on low-power hardware poses some challenges. This work examines the usage of object detection models on edge computing devices, with a focus on the Raspberry Pi 4. In this work are evaluated some models like SSD, and YOLO models using a custom dataset based on BDD100K dataset. All the models used in the project were converted to Open Neural Network Exchange format and tested at resolutions of 320 \(\times \) 320 and 640 \(\times \) 640 to assess their efficiency and real-time applicability. The results indicate that YOLO-based models, particularly YOLOv11, give the best balance between accuracy and inference speed, making them a candidate for Advanced Rider Assistance Systems applications. Despite these advancements, challenges such as real-time constraints and hardware limitations remain. Future research should focus on model quantization and hardware acceleration to improve deployment feasibility.