A comprehensive Study on Security Threats in Autonomous Vehicles: Safeguarding the Future

Autonomous vehicles, commonly referred to as self- driving vehicles, have the capability to operate and perform necessary functions without the need for human intervention. This is made possible with advanced technologies such as cameras, ultrasonic and infrared sensors, INS, GPS, DSRC, RADAR, LiDAR, and onboard computers, which work together to map the vehicle's position and surroundings. However, because these technologies are easily susceptible to manipulation, autonomous vehicles are at risk of cyber-attacks if hackers succeed to find vulnerabilities in the vehicle's system or in the electronic system of the company producing the vehicle. This study aims to investigate how machine learning and deep learning methods can be utilized to improve the defence against cyber threats aimed at autonomous vehicles by analysing several computational algorithms.