With the introduction of autonomous and self-driving cars, innovative research is needed to ensure safety and reliability on the road. This work introduces a solution to understand vehicle behaviour based on sensors data. The behaviour is classified according to driving events. Understanding driving events can play a significant role in road safety and estimating the expense and risks of driving a vehicle. Rather than relying on the distance and time driven, driving events can provide a more accurate measure of vehicle driving consumption. This measure will become valuable as more ride-sharing applications are introduced to roads around the world. Estimating driving events can also help better design the road infrastructure to reduce congestion, energy consumption and pollution. By sharing data from official vehicles and volunteers, crowd sensing can be used to better understand congestion and road safety. This work studies driving events and proposes using machine learning to classify these events into different categories. The acquired data is collected using embedded mobile device motion sensors to train machine learning algorithms to classify the events.