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The aspiration of the next generation's autonomous driving (AD) technologyrelies on the dedicated integration and interaction among intelligentperception, prediction, planning, and low-level control. There has been a hugebottleneck regarding the upper bound of autonomous driving algorithmperformance, a consensus from academia and industry believes that the key tosurmount the bottleneck lies in data-centric autonomous driving technology.Recent advancement in AD simulation, closed-loop model training, and AD bigdata engine have gained some valuable experience. However, there is a lack ofsystematic knowledge and deep understanding regarding how to build efficientdata-centric AD technology for AD algorithm self-evolution and better AD bigdata accumulation. To fill in the identified research gaps, this article willclosely focus on reviewing the state-of-the-art data-driven autonomous drivingtechnologies, with an emphasis on the comprehensive taxonomy of autonomousdriving datasets characterized by milestone generations, key features, dataacquisition settings, etc. Furthermore, we provide a systematic review of theexisting benchmark closed-loop AD big data pipelines from the industrialfrontier, including the procedure of closed-loop frameworks, key technologies,and empirical studies. Finally, the future directions, potential applications,limitations and concerns are discussed to arouse efforts from both academia andindustry for promoting the further development of autonomous driving.

Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies