How to Guarantee Driving Safety for Autonomous Vehicles in a Real-World Environment: A Perspective on Self-Evolution Mechanisms

A succession of accidents shows that production vehicles with autonomous driving systems do not work safely in real-world environments, especially when facing unseen scenarios. Therefore, how to ensure that autonomous systems drive more safely becomes a challenge. Thanks to the self-learning ability of human beings, human drivers can gradually learn how to drive from a driving test with typical and finite scenarios to the real world with infinite ones. Analogically, it is believed that accidents can be largely reduced once the designed autonomous vehicles are endowed with a self-learning ability to adapt to the unseen and then to infinite scenarios in the real world. Accordingly, this work proposes a principle to design autonomous systems with a self-evolution feature not just for a single vehicle but for a group. In addition, it describes our development of a self-evolution autonomous system as an illustrative case study of implementing such principles in practice. The ultimate aim is to propose a feasible solution to speed up the design process of a fully safe autonomous system.