As the population increases, an escalating number of vehicles participate in traffic daily. This inevitably increases the likelihood of traffic fatalities and accidents due to various variables, including fatigue, drowsiness, and adverse road conditions. In recent years, there has been an increasing global apprehension regarding the escalating incidence of traffic accidents. This facilitates a highly accurate Advanced Driver Assistance System (ADAS) to assess any situation while driving in real-time and alert the driver, who must occasionally make decisions. ADAS aids drivers by offering control and alerts via visual assistance. A set of electrical components known as ADAS provides an enhanced driving experience in vehicles. These systems include, among other components, image, ultrasonic, radar, infrared, laser, and LiDAR sensors. Deep learning has been the primary catalyst for significant advancements in the precision of object detection systems. This paper provides a survey of ADAS techniques employed in automobiles through deep learning and computer vision.