Autonomous cars are computer-controlled and have the ability to navigate without a human operator. This technology vastly differs from automated features such as lane departure warning systems, which some vehicles are equipped with today. According to Canada’s Safety Framework for Automated and Connected Vehicles, six levels of autonomy are used to classify cars, with Level 0 having no autonomous features and Level 5 being fully self-driving.
There are several benefits to the adoption of fully autonomous vehicles. Firstly, it is predicted that there will be fewer road accidents as collisions will no longer be caused by human error, drug use, inexperience, or poor road conditions. Secondly, autonomous vehicles will reduce traffic congestion by communicating their speed, position, and direction to the other vehicles on the road through artificial intelligence technologies. Lastly, autonomous vehicles could be programmed to safely transport the young, disabled, and elderly by picking them up from a specified location, driving them to their destination, and parking until needed.
While self-driving cars are an exciting advancement from a technological viewpoint, they also raise questions around crash-avoidance programming and liability in the event of a car accident.
In 2016, Ontario’s Ministry of Transportation gave the green light to the launch of a 10-year pilot program aimed at testing autonomous vehicles on Ontario public roads. While testing continues, insurance companies are examining the risks associated with self-driving cars and how this new technology could alter the insurance industry as a whole. Currently, auto insurance policies are centered around the notion that human negligence and error are the primary causes of vehicle collisions. However, as the control of driving passes from human to automated technology, collisions will likely be due to product malfunction. Insurance companies are thus focusing on the question of liability and on whether automated vehicles are “smart” enough to adapt to certain weather conditions or recognize objects such as bikes.