Autonomous vehicles (AVs) have revolutionized transportation, but challenges such as accurate object detection, reliable lane adherence, and real-time decision-making persist. Addressing these issues is critical for enhancing AV safety and efficiency. This study proposes a novel approach integrating temporal AI with computer vision technologies to improve AV performance. Object detection models classify and track environmental elements, while lane detection models ensure proper navigation. Temporal AI is incorporated to analyze sequential data, capturing the dynamic nature of driving scenarios. A comprehensive analysis of current challenges and a survey of public perceptions regarding AV capabilities provided insights for developing the integrated solution. The suggested methodology improves object and lane identification systems’ accuracy and dependability by utilizing temporal AI’s advantages, facilitating improved situational awareness and decision-making. Initial results demonstrate a significant improvement in system performance, reducing detection errors and enhancing real-time adaptability. The findings suggest that this integration offers a promising pathway to overcoming current AV limitations, contributing to safer and more efficient autonomous driving technologies.