Self-driving cars are as fascinating as they are challenging to develop. The sensors, compute, actuation all need to continuously work together to understand the surrounding environment and respond in real-time. And when it all works as expected, we will save lives and change society for the better. Back in 2013, Tesla, while still struggling, decided to take on this challenge. All in the midst of posting huge operating losses, struggling to manufacture cars and scale production. What was Tesla's strategy, and why? How has it evolved over the years?

Self-driving cars operate using machine learning. Machine learning algorithms have become commoditized, even open source. Data, not algorithms, is what confers competitive advantage. Currently, Tesla (NASDAQ:TSLA) has access to vastly more driving data than any other company. The Alphabet (NASDAQ:GOOG) (NASDAQ:GOOGL) subsidiary Waymo, once widely recognized as the leader in self driving, had a cumulative 2 million miles of driving data in October 2016. Tesla's cars with "full self-driving hardware" (HW2) are currently driving over 1 million miles per day.

Self-driving vehicles will play a crucial role in improving transportation safety and accelerating the world's transition to a sustainable future. Full autonomy will enable a Tesla to be substantially safer than a human driver, lower the financial cost of transportation for those who own a car and provide low-cost on-demand mobility for those who do not. We are excited to announce that, as of today, all Tesla vehicles produced in our factory – including Model 3 – will have the hardware needed for full self-driving capability at a safety level substantially greater than that of a human driver. Eight surround cameras provide 360 degree visibility around the car at up to 250 meters of range. Twelve updated ultrasonic sensors complement this vision, allowing for detection of both hard and soft objects at nearly twice the distance of the prior system.