Artificial Intelligence and machine learning have significantly 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 large amounts of data obtained from a wide range of sensors. Cameras, LiDAR (Light Detection and Ranging), radar, and ultrasonic sensors are examples of these sensors. The data collected in real-time from the surrounding environment of the vehicle encompasses images, video, and sensor readings.
By effectively incorporating machine learning development techniques into the training deep learning models, ADAS systems can analyze the sensor data in real-time and make informed decisions to enhance driver safety and assist in driving tasks, making it future ready for autonomous driving. They can also estimate distances, velocities, and trajectories of surrounding objects, allowing ADAS systems to predict potential collisions and provide timely warnings or take preventive actions. Let’s dive into the key steps of deep learning techniques in the Advanced Driver Assistance System and tools commonly used in developing and deploying ADAS systems.