Application and Challenges of Automotive Visual Perception Technology Based on Deep Learning

Under the trend of the deep integration of artificial intelligence and the automotive industry, the development frontier in the transportation field is intelligent connected vehicles. The core prerequisite for achieving autonomous driving is to endow vehicles with precise environmental perception capabilities. This article systematically reviews the current application status and challenges of automotive visual perception technology based on deep learning. The article first provides an overview of the basic paradigms of deep learning and its core role in key visual tasks of autonomous driving. Next, a detailed analysis will be conducted on the typical applications of these technologies in advanced driver assistance systems and high-level autonomous driving, as well as their implementation principles. However, when this technology is widely applied, it still faces many severe challenges such as environmental adaptability, safety and reliability. Therefore, at the end of this article, the future development trends such as sensor fusion, model lightweighting and algorithm optimization are prospected. The analysis results show that deep learning visual perception technology is a key driving force for the development of intelligent vehicles. However, to achieve full implementation, continuous breakthroughs are still needed in terms of technological robustness and engineering.