Autonomous Driving Design Based on Deep Learning

In today's world, globalization and rapid technological progress are driving deeper urbanization. Along with the explosive increase in population and the number of vehicles, urban transportation systems face unprecedented challenges, including severe traffic congestion, frequent traffic accidents, and worsening air quality. According to international statistics, traffic accidents cause million deaths annually and result in direct economic losses exceeding 0.1 trillion USD. In this context, intelligent vehicle technology, especially autonomous driving cars, is widely regarded as one of the key technologies to alleviate these issues. This thesis delves into the application of deep learning and machine vision in intelligent vehicle systems, focusing particularly on their practical effects in path planning and obstacle recognition. By systematically analyzing and evaluating existing intelligent vehicle technologies, this paper proposes an innovative algorithmic solution that combines an enhanced A* search algorithm with advanced real-time image processing techniques. Experimental results demonstrate that this new algorithm significantly outperforms traditional methods in enhancing navigation efficiency and accuracy, providing a new solution for safe navigation of intelligent vehicles in complex environments. Moreover, this research not only advances the development of autonomous driving technology but also supports the theoretical and practical implementation of future intelligent transportation systems.