Experimental study on autonomous driving control algorithm based on deep learning

With the rapid advancement of artificial intelligence and machine learning, autonomous driving technology has achieved remarkable maturity. Its application in complex scenarios is no longer beyond reach, making the development of high-precision, adaptive control systems a critical challenge in this field. This paper focuses on deep learning applications in autonomous driving control algorithms. Specifically targeting typical scenarios such as adverse weather, extreme climates, and complex road conditions, we propose an innovative control algorithm integrating multi-sensor fusion technology with adaptive strategies to achieve efficient management of autonomous vehicles in complex traffic environments. Furthermore, by establishing a hybrid virtual-real training environment and incorporating an enhanced Soft Actor-Critic (SAC) algorithm with deep learning mechanisms, we significantly improve the algorithm's environmental adaptability and decision robustness. These research findings provide new approaches for stable autonomous driving operations in complex environments while enhancing road safety.