Deep Learning in Autonomous Vehicles: Enhancing Perception and Decision-Making
Abstract
Deep learning, a subset of machine learning (ML), has been instrumental in advancing the capabilities of autonomous vehicles. This paper explores the application of deep learning techniques in enhancing the perception and decision-making processes of self-driving cars. By examining sensor fusion, object detection, and path planning algorithms, the study assesses their effectiveness in improving vehicle safety, navigation, and overall performance. The findings suggest that deep learning can significantly enhance the reliability and efficiency of autonomous vehicles, paving the way for safer and more intelligent transportation systems.
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