Driving into the Future: A Comparative Analysis of the Hough Transform and Convolutional Neural Network for Autonomous Vehicles

This paper conducts a comparative analysis of two lane detection algorithms, the Hough Transform and Convolutional Neural Network (CNN), within real-world scenarios using camera and Raspberry Pi technology. A comprehensive prototype is developed, integrating both hardware and software components. The algorithms’ results are categorized as “pass” or “fail,” and accuracy is determined by comparing the number of “pass” cases to the total cases. This research effectively discerns the conditions in which each algorithm excels, providing valuable insights for practical implementation. It aids in selecting the most suitable algorithm for specific environmental and operational conditions, advancing the field of lane detection.