  The aspiration of the next generation's autonomous driving (AD) technology
relies on the dedicated integration and interaction among intelligent
perception, prediction, planning, and low-level control. There has been a huge
bottleneck regarding the upper bound of autonomous driving algorithm
performance, a consensus from academia and industry believes that the key to
surmount the bottleneck lies in data-centric autonomous driving technology.
Recent advancement in AD simulation, closed-loop model training, and AD big
data engine have gained some valuable experience. However, there is a lack of
systematic knowledge and deep understanding regarding how to build efficient
data-centric AD technology for AD algorithm self-evolution and better AD big
data accumulation. To fill in the identified research gaps, this article will
closely focus on reviewing the state-of-the-art data-driven autonomous driving
technologies, with an emphasis on the comprehensive taxonomy of autonomous
driving datasets characterized by milestone generations, key features, data
acquisition settings, etc. Furthermore, we provide a systematic review of the
existing benchmark closed-loop AD big data pipelines from the industrial
frontier, 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 and
industry for promoting the further development of autonomous driving. The
project repository is available at:
https://github.com/LincanLi98/Awesome-Data-Centric-Autonomous-Driving.
