Advanced driver assistance system (ADAS) and machine learning (ML): The dynamic duo revolutionizing the automotive industry

The advanced driver assistance system (ADAS) primarily serves to assist drivers in monitoring the speed of the car and helps them make the right decision, which leads to fewer fatal accidents and ensures higher safety. In the artificial Intelligence domain, machine learning (ML) was developed to make inferences with a degree of accuracy similar to that of humans; however, enormous amounts of data are required. Machine learning enhances the accuracy of the decisions taken by ADAS, by evaluating all the data received from various vehicle sensors. This study summarizes all the critical algorithms used in ADAS technologies and presents the evolution of ADAS technology. Initially, ADAS technology is introduced, along with its evolution, to understand the objectives of developing this technology. Subsequently, the critical algorithms used in ADAS technology, which include face detection, head-pose estimation, gaze estimation, and link detection are discussed. A further discussion follows on the impact of ML on each algorithm in different environments, leading to increased accuracy at the expense of additional computing, to increase efficiency. The aim of this study was to evaluate all the methods with or without ML for each algorithm.