ABSTRACT
At present around 750 million people are living on the earth and the growth of urbanization and industrialization is very high. As we are developing ourselves as individuals or as society, more and more facilities and precision of work are important. Nowadays, a number of new technologies are being invented to help us to do more and more work and create a comfortable environment. Due to rapid urbanization and increasing incomes, more and more vehicles are roaming on the highways and city roads, which is a cause of increasing driving security threat, because increasing traffic on the roads has increased the chances of human driving errors (i.e., around 95% road incidents are due to human error). So, autonomous driving has been the hot cake research for the industries and academics. We have the power of deep learning algorithms and computer vision to develop a robust autonomous driving vehicle solution. In autonomous driving vehicle solutions various IoT sensors (air pressure sensor, parking sensor, LIDAR, ultrasonic, Radar, Camera, GPS, etc.) and other electronic devices are used to update the different stages of the vehicle operations. These devices, sensors acquire a lot of run time information and send that data to the processing unit which machine learning model analyzes and gives an appropriate instruction to the vehicle control system. However, the data collected by sensors and other devices and processed by machine learning tools has a great chance to hack 374the signal and malfunction the vehicle. So the cybersecurity threat to the autonomous vehicles has been a great concern to the researchers. In this chapter we are exploring different cybersecurity threats, case studies, and their possible prevention in autonomous vehicles.
