A Survey on Self-Evolving Autonomous Driving: A Perspective on Data Closed-Loop Technology

This is the authors' abstract. We don't add key points for this paper.

Self evolution refers to the ability of a system to evolve autonomously towards a better performance, which is a potential trend for autonomous driving systems based on self-learning approaches. However, current algorithms for autonomous driving still lack of self-evolving mechanisms and the capability of maintaining continuously performance-enhancing. Some recent studies turn to the data closed-loop (DCL) architecture to realize self evolution. Therefore, this study analyzes some relevant technologies and then proposes a novel design mechanism to guarantee the self-evolving performance for autonomous driving systems. Although existing data closed-loop platforms are not yet mature enough to fully achieve this purpose, it has the potential to incorporate cutting-edge technologies that will enhance their functionality. Moreover, we give some suggestions for its future directions for self-evolving autonomous driving, including some more cutting-edge technologies that can be incorporated into the DCL architecture.