Author:
Yuan Kang,Huang Yanjun,Guo Lulu,Chen Hong,Chen Jie
Abstract
AbstractArtificial intelligence empowers the rapid development of autonomous intelligent systems (AISs), but it still struggles to cope with open, complex, dynamic, and uncertain environments, limiting its large-scale industrial application. Reliable human feedback provides a mechanism for aligning machine behavior with human values and holds promise as a new paradigm for the evolution and enhancement of machine intelligence. This paper analyzes the engineering insights from ChatGPT and elaborates on the evolution from traditional feedback to human feedback. Then, a unified framework for self-evolving intelligent driving (ID) based on human feedback is proposed. Finally, an application in the congested ramp scenario illustrates the effectiveness of the proposed framework.
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Cited by
1 articles.
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