The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This letter reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by a range of technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the ``6A-goal" of ACS and propose potential avenues for future research.

翻译：网络-物理-社会系统（CPSS）的精确表征与建模需要更全面、更准确的数据，这对智能感知能力提出了更高要求。为解决这一问题，群体感知智能（CSI）通过利用多样化劳动力的集体智慧，从CPSS中收集数据。我们举办的第一届和第二届分布式/去中心化群体感知智能混合研讨会（DHW-CSI）聚焦于CSI组织与运行的原则及高层次流程，以及CSI涉及的参与者、方法和阶段。本信函报告了最新一届DHW-CSI的成果，重点介绍了由去中心化自主组织与运行、大语言模型以及面向人机操作系统等一系列技术赋能的自主群体感知（ACS）。具体而言，我们阐释了ACS的内涵，并探讨了其与传统群体感知相比的独特特征。此外，我们提出了ACS的“6A目标”，并指出了未来研究的潜在方向。