Autonomous vehicles have become increasingly popular from the last few years because of their numerous benefits, such as minimum travel time, enhanced safety, and improved fuel economy. Many vehicle manufacturing companies and tech giants are working on this technology to make fully autonomous vehicles or strengthen their existing driver-less vehicles. These vehicles use complex, advanced, and sophisticated hardware technologies. However, the software is an equally important feature because it must operate all functions seamlessly in sync with other vehicle components. The software must analyze large volumes of data to make quick real-time decisions, so any vulnerabilities or bugs can be a severe problem to the vehicle and the passengers riding in it. Many researchers have proposed various software defect prediction schemes for different projects and applications, but most of them have focussed on specific software issues and excluded others. Thus, their methods cannot be applied to the software of autonomous vehicles. In this paper, we propose an improved Artificial Neural Network (ANN) model, called Dropout-Artificial Neural Network (D-ANN), to solve this problem of defect prediction in autonomous vehicles. This inclusive model can consider all the parameters simultaneously for effective bug prediction. The proposed model can be used for the software of any autonomous vehicles, and it is trained and evaluated using standard methods. The results obtained show that the proposed model predicts software defects with higher accuracy than other models.
Tanwar et al. (Wed,) studied this question.