Recently, the autonomous driving industry in China has been gradually shifting its focus from individual-vehicle intelligence to vehicle‒infrastructure cooperation. This shift has brought significant opportunities for the intelligent transportation industry. Although research on vehicle‒infrastructure cooperative sensing is still in its early stage in China, it shows a strong dedication to technological innovation, indicating significant potentials for future growth. This study examines the development status of vehicle‒infrastructure cooperative sensing and thoroughly explores the characteristics and status of core technologies that support vehicle‒infrastructure cooperative sensing. It discusses ongoing advancements in this field, investigates future technology trends, and proposes a range of recommendations for further development. Research indicates that vehicle‒infrastructure cooperative sensing is evolving toward the integration of multi-source data. Presently, its development directions mainly focus on the optimization of pure visual cooperative sensing, upgrades in LiDAR point cloud processing, advancements in multi-sensor spatiotemporal information matching and data fusion, as well as the establishment of a standards system for vehicle‒infrastructure cooperative sensing technologies. To further boost the rapid growth of vehicle‒infrastructure cooperation in China, increasing investment in the research and development of relevant technologies is advised. Enhancing partnerships among different industry sectors, establishing unified standards for processing perception data, and expediting the broad application of these technologies are also key recommendations. These strategies aim to position China advantageously in the global market of autonomous driving, contributing to the sustainable development of the industry.

Collective intelligence is an important component of the new generation of artificial intelligence (AI). It plays a decisive role in stimulating and converging innovative forces as well as coupling and integrating large-scale intelligent systems. It is of great significance for promoting deep integration of AI and traditional industries and enabling the sustainable development of the national economy. This study summarizes the overall technical framework of collective intelligence and its major research areas, including:multi-agent systems and optimal decision-making, unmanned swarm systems, open source collective intelligence software, and federated learning. Moreover, it analyzes how these core technologies can be applied in industrial scenarios, in order to establish intelligent processing loops of perception‒cognition‒decision‒action, to support platform economy with distributed intelligence, and to reshape industrial development and digital economy ecosystems. Based on the subjects and application modes of the technical framework, this study analyzes the core industries related to collective intelligence, particularly the software service industry, the smart city industrial cluster, and the intelligent agriculture and port industries based on unmanned swarm systems, by highlighting their significant requirements and empowerment approaches for collective intelligence technologies. Furthermore, this study presents suggestions on how to utilize collective intelligence technologies to foster development of rated industries. It is suggested that we should continuously promote the establishment of open source communities of collective intelligence, enhance the intellectual core of the AI technological innovation ecosystem, and accelerate the domestic substitute of unmanned swarm systems through integrated system research.

As intelligent technologies and unmanned systems develop rapidly, the concept of cross-domain cooperative technology of intelligent unmanned swarm systems has emerged, received widespread attention, and gradually become the high ground in the competition of unmanned system technologies among countries worldwide. Based on the development demand for the cross-domain cooperative technology of intelligent unmanned swarm systems in China, this study summarizes the research status of the crossdomain cooperative technology in typical unmanned scenarios such as sea – air, air – ground, and sea – ground/sea – ground – air, and thoroughly analyzes the current status, technological demand, and key research directions of the technology. Additionally, countermeasures and suggestions are proposed to promote the steady and rapid development of the cross-domain cooperative technology from the perspectives of overall concept, system architecture, theoretical innovation, and technological breakthroughs, with the aim of facilitating the sustained development of unmanned systems in China.

Collaborative intelligence formed via information and behavioral interactions of multiple autonomous systems is an inevitable trend of future intelligent systems. It is a focus of planning of the next-generation artificial intelligence in China and is crucial for supporting national security and strengthening the manufacturing industry. Research aimed at overcoming bottlenecks regarding collaborative multiple autonomous systems will significantly aid the advancement of intelligent industries and accelerate industrial transformation and upgrading in China. Focusing on the challenge that collaborative multiple autonomous systems cannot adapt to complex tasks, this study thoroughly analyzes the research status and major bottlenecks of collaborative multiple autonomous systems from the aspects of fundamental research and engineering. Using multi-robot collaborative intelligent manufacturing as an example, we provide an in-depth analysis of relevant theoretic and technical problems. Our research indicates that collaborative multiple autonomous systems will inevitably evolve toward human ‒ machine teaming. To master this opportunity, it is critical to proactively lay the groundwork for the theoretical exploration and technological breakthroughs of human‒machine teaming and to conduct exemplary applications.

Autonomous driving is an important research direction in computer vision which has broad application prospects. Pure vision perception schemes have significant research value in autonomous driving scenarios. Different from traditional cameras, spike vision sensor offers imaging speeds over a thousand times faster than traditional cameras, possess advantages such as high temporal resolution, high dynamic range, low data redundancy, and low power consumption. This study focuses on autonomous driving scenarios, introducing the imaging principles, perception capabilities, and advantages of the spike camera. Besides, focusing on visual tasks related to autonomous driving, this study elaborates on the principles and methods of spike-based image/video reconstruction, discusses the approach to image enhancement based on sensor fusion with spike cameras,and provides a detailed description of the algorithms and technical routes for motion optical flow estimation, object recognition, detection, segmentation, and tracking, and deep estimation of three-dimensional scenes based on spike cameras. It also summarizes the development of the spike camera data and systems. At last, it analyzes the challenges, potential solutions, and future directions for spike vision research. Spike cameras and their algorithms and systems hold great potentials in the field of autonomous driving and represent one of the future research directions in computer vision.

Device-cloud collaborative intelligent computing, an emergent result of the development in big data, cloud computing, and edge computing, offers significant improvements in data utilization while protecting user privacy. This approach synergizes the realtime response capabilities of intelligent computing with service robustness. The study explores the application value of this computing paradigm, highlighting technical challenges such as optimizing on-device learning efficiency, mitigating overfitting with limited samples at the device, customizing on-device models, learning false associations under distributional discrepancies, and balancing communication overhead with computational efficiency. We systematically review the progress in mainstream methods within devicecloud collaborative intelligent computing, encompassing efficient computation hardware as the application cornerstone, device-centric collaborative computing, cloud-centric collaborative computing, bidirectional device-cloud collaborative computing, and trustworthy device-cloud collaborative computing. The study also summarizes applications in vertical domains such as recommendation systems, autonomous driving, security systems, and educational models. Looking toward the future of device-cloud collaborative intelligent computing, it underscores the need for focused research on cloud resource application strategies in device model personalization, multi-objective optimization algorithms for device-cloud collaboration, and optimized collaborative strategies between devices and the cloud.

Embodied intelligence stands as a strategic technology in the ongoing scientific and technological revolution, forming a frontier in global competition. The mobile manipulator robot system, with its exceptional mobility, planning, and execution capabilities, has become the preferred hardware carrier for embodied intelligence. Moreover, the mobile manipulator robot system, rooted in embodied intelligence, emerges as a pivotal platform capable of cross-domain functionality. Positioned at the forefront of a new era in information technology and artificial intelligence, this system is integral for future development. Addressing the strategic demand for embodied-intelligence-based mobile manipulator robot systems, this study presents an overview of the current developmental landscape. It delves into the challenges faced by this field, proposing key common technologies such as multimodal perception, world cognition, intelligent autonomous decision-making, and joint planning for movement and manipulation. Furthermore, the study offers recommendations for advancing the field, encompassing national policy support, breakthroughs in common technologies, interdisciplinary collaboration, talent cultivation, and construction of comprehensive verification platforms. These suggestions aim to facilitate the rapid progress of mobile manipulator robots in China amid the wave of embodied intelligence development.

Space robots can adapt to the extreme environment of space, break through the limits of human space exploration, and greatly improve the safety and economy of space operation and control. Moreover, space robots are the core equipment to improve the level of space science and technology, providing important support and a strong guarantee for promoting space industry development. This study elaborates on the great values of developing space robot technology for the autonomous repair and maintenance of spacecraft, which include promoting the construction of strengthening China’s space powerindustry, promoting the development of national defense science and technology, and leading transformative scientific and technological innovation. The progress and development trends of domestic and foreign space robotics technologies in China and abroad are analyzed from the policy, technology, and market perspectives. In addition, technical challenges and problems faced by China are dissected toward the autonomous repair and maintenance of spacecraft. The development system and breakthrough path of China’s space robot technologies for autonomous repair and maintenance of spacecraft are demonstrated based on major national strategic needs, research foundation, and development directions. Furthermore, the following suggestions are proposed: (1) accelerating the implementation of major special projects for on-orbit services (Scientific and Technological Innovation 2030), (2) increasing support for basic research on intelligent operation and control of space robots, (3) accelerating the construction of a government-enterprise-university collaborative innovation mechanism, and (4) strengthening international cooperation to attract foreign science and technology talents to China.