Lane Segmentation and Turn Prediction Using CNN and SVM Approach

As the automotive industry continually evolves towards the realization of autonomous vehicles, the critical task of real-time lane detection and road segmentation using computer vision assumes paramount significance. Achieving reliable and accurate lane marking detection and road region segmentation represents a cornerstone for the safe and efficient operation of smart cars. This problem statement seeks to elucidate the multifaceted challenges encompassing this domain, taking into account the complexities of real-world driving scenarios, diverse environmental conditions, and the imperative need for robust and adaptive algorithms. This paper aims To develop a comprehensive understanding of the state-of-the-art techniques and methodologies in computer vision, with a specific focus on lane segmentation and road detection in the context of smart car vision. To investigate the integration of Deep Convolutional Neural Networks (CNNs) and Support Vector Machine (SVM) classification for enhancing the accuracy and robustness of lane segmentation and road detection in smart car environments.