Autonomous vehicles combine a variety of sensors to perceive their surroundings, including radar, lidar, computer vision, sonar, and GPS, among others. These sensors interpret sensory information to identity navigation paths, avoid obstacles and read relevant markers, like road signs. In multiple locations around the world autonomous vehicle development teams run tests that take thousands of hours of test drive data. One eight-hour shift can create more than 100 terabytes of data. This massive amount of data must be collected, offloaded, stored, and interpreted for algorithmic training to build vehicle decision-making.The big challenge: How to efficiently manage all the data that gets generated during the tests and teach the vehicle how to make decisions faster in very diverse conditions …even a moral dilemma. And how do you teach the vehicle to make an adjustment it’s not been trained to make when an unexpected issue in the real world becomes an event that should change the vehicles behavior?