With the continuous maturation and application of autonomous driving
technology, a systematic examination of open-source autonomous driving datasets
becomes instrumental in fostering the robust evolution of the industry
ecosystem. Current autonomous driving datasets can broadly be categorized into
two generations. The first-generation autonomous driving datasets are
characterized by relatively simpler sensor modalities, smaller data scale, and
is limited to perception-level tasks. KITTI, introduced in 2012, serves as a
prominent representative of this initial wave. In contrast, the
second-generation datasets exhibit heightened complexity in sensor modalities,
greater data scale and diversity, and an expansion of tasks from perception to
encompass prediction and control. Leading examples of the second generation
include nuScenes and Waymo, introduced around 2019. This comprehensive review,
conducted in collaboration with esteemed colleagues from both academia and
industry, systematically assesses over seventy open-source autonomous driving
datasets from domestic and international sources. It offers insights into
various aspects, such as the principles underlying the creation of high-quality
datasets, the pivotal role of data engine systems, and the utilization of
generative foundation models to facilitate scalable data generation.
Furthermore, this review undertakes an exhaustive analysis and discourse
regarding the characteristics and data scales that future third-generation
autonomous driving datasets should possess. It also delves into the scientific
and technical challenges that warrant resolution. These endeavors are pivotal
in advancing autonomous innovation and fostering technological enhancement in
critical domains. For further details, please refer to
this https URL

通过系统考察当前两代自动驾驶数据集，本综述提供关于高质量数据集创建原则、数据引擎系统的关键作用，以及采用生成式基础模型促进可扩展数据生成等方面的见解。此外，本综述还详细分析了第三代自动驾驶数据集应具备的特征和数据规模，并深入探讨需要解决的科学和技术挑战，以推动自主创新和关键领域的技术提升。