Creating an Effective Advanced Driver Assistance System (ADAS) by Employing Real-Time Lane Detection Utilizing Hough Transformation and CARLA Simulation

Lane detection is a critical component in the development of autonomous driving systems. This work employs Hough transformation as a key technique for the identification of vertical lanes. The methodology begins with image pre-processing to extract essential edge features. A designated region of interest (ROI) is subsequently defined within the image. This region undergoes Hough transformation, aiding in the identification of straight-line segments. These identified segments are further refined and simulated through the linear regression method. The outcome is the extraction of a linear equation representing the lane lines. One of the key takeaways is the system's computational efficiency. Unlike deep learning methods, which often require extensive computational resources, the proposed algorithm accomplishes accurate lane detection with significantly less computational load. This makes it a highly viable solution for real-time applications in autonomous driving systems The process outlined herein offers a rigorous, efficient, and cost-effective approach to lane detection, vital for enhancing road safety in automated driving applications. However, it's crucial to acknowledge the algorithm's variance in efficacy under different environmental conditions. This framework provides a robust base for further research and real-world testing, specifically in the domain of autonomous vehicles. The report concludes by emphasizing the cost-effectiveness and computational efficiency of this method compared to deep learning approaches, advocating for standardization of lane detection techniques. It also opens the door for the integration of machine learning and deep learning techniques for more advanced feature recognition and system adaptability in the future.