Recently, autonomous vehicles (AVs) have received a lot of attention from the automotive industry as well as the AV research community across the globe. To increase the safety of the flow of traffic, they are anticipated to help or perhaps take the place of human drivers when it comes to vehicle manoeuvring at various levels of autonomy. But before they can be widely used, AVs must first be developed to overcome their inherent security and road safety issues. The adoption of autonomous vehicles depends on the results of the driving test and their safety validation on the roads. This paper examines the associated autonomous vehicle testing and validation methodologies such as autonomous vehicle functional testing, integrated vehicle testing, and system verification across many architectures. In addition, the paper presents some of the state-of-the-art machine learning algorithms used for AV operation on roads. Knowledge of such recent trends in the validation and verification techniques for road safety will be helpful for the development of autonomous vehicles.