Autonomous vehicle navigation demands accurate perception systems capable of viewing the real-time scenes in the surroundings. In our research, we present an integrated strategy to semantic segmentation, incorporating traffic sign recognition and lane detection, vital for safe and efficient autonomous driving. Using deep learning techniques, our model employs a multi-class segmentation architecture trained on diverse datasets containing various environmental conditions. Lane detection is done through a combination of computer vision methods and deep learning algorithms, ensuring robust performance across different road types and lighting conditions. Similarly, traffic sign recognition utilizes a fusion of template matching and deep learning-based classifiers. SVM classifiers are trained for each task and integrated into the autonomous vehicle system, creating a real-time processing pipeline. Classification to accurately identify and interpret traffic signs in the scene. Through extensive testing and evaluation, we showcase the effectiveness and reliability of our integrated system in real-world driving environments, achieving high accuracy in tasks such as semantic segmentation, lane detection, and traffic sign recognition. Our systematic presentation represents a significant advanced perception systems essential for safe and autonomous navigation of vehicles in diverse road environments.