Report Open Access
There are as many paths to mass adoption of autonomous vehicle systems as there are people, companies, and governments willing to try to engineer and support the development of such systems. Opinions vary vastly. Many researchers, engineers, and policy-makers believe that semi-autonomous systems are too difficult to engineer safely and effectively due to the human factor of vigilance degradation in out-of-the-loop supervision of automation. On the other hand, many believe that fully-autonomous systems are too difficult to engineer safely and effectively due to the enumerable edge-cases that must be accounted for by perception, control, and planning algorithms including especially cases that involve complex, non-verbal communication with human beings. Like for many problems in machine learning, robotics, and artificial intelligence, the transition from prototype to large-scale real-world deployment can fundamentally change our understanding of the underlying problem and the set of approaches that are effective at solving it. The truth emerges when the rubber hits the road. Motivated by this real-world perspective, this paper details an approach to estimate and project into the future the number of miles driven in Tesla vehicles based on vehicle delivery rates and system utilization. Tesla Autopilot has driven over 1 billion miles to date and is forecast to reach over 2.3 billion miles by the end of 2019.