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Artificial Intelligence and machine learning have revolutionized the Advanced Driver Assistance System (ADAS) by utilizing the strength of deep learning techniques. ADAS relies heavily on deep learning to analyze and interpret data from a wide range of sensors. Data preprocessing is an important step in the development and deployment of deep learning models for ADAS, which involves tasks such as cleaning data, handling missing values, addressing outliers, and normalizing features. This ensures that the ADAS system can work with reliable and standardized data, improving its accuracy and performance.
