When people start to figure the self driving cars will replace some vehicles on the road in the very near future, it somehow means the learner in the robot software could reach a very low empirical error to ensue the safety of passengers. The great deal of driving data collected and the deep reinforcement learning algorithms contribute mostly to the success of the self driving practice as we see it.
When it comes to many cases of our own practice, we yet cannot build up a predictive model to such a high accuracy to bring the huge difference to our own business.
My question is, when I saw the machine learning techniques can make revolution on the car driving due to its successful algorithms to implement behavioral cloning tasks well, what is the foremost reason for this success, the big training data set or the deep reinforcement learning techniques or any particular reason in self driving problems?
Furthermore, can this success be copied in most of the practical problems we solve in machine learning? In other words, if today's machine learning techniques can help a car drive by itself, how can I reach the same success when developing my own predictive models to boost my business? If we cannot, what are the constraints, not enough data in the particular case or not a smart model yet? The reason the self driving thrives can help answer this question.