Introduction: With the rapid development of artificial intelligence and intelligent transportation technology, swarm intelligence, as an important direction of the new generation of artificial intelligence, is gradually becoming the core of intelligent transportation systems. Swarm intelligence forms collective intelligence and decision-making capabilities through collaboration and interaction among multiple simple individuals, thereby solving complex problems. The "Sense-Fusion-Computing" vehicle-road-cloud network is one of the key technologies to achieve this goal. This article will explore how the "Sense-Fusion-Computing" vehicle-road-cloud network becomes the foundation for building swarm intelligence and analyze its application in intelligent transportation systems.

I. Concept and Characteristics of Swarm Intelligence

(I) Concept of Swarm Intelligence

Swarm intelligence stems from the observation and research of biological group behavior in nature, such as ant colonies and bee colonies. In these biological groups, although individuals are relatively simple, they can complete complex tasks and exhibit intelligent behavior beyond individual capabilities through information exchange and collaboration with each other. In human society, swarm intelligence also has wide applications, such as crowdsourcing on the internet and information dissemination in social networks. The theoretical basis of swarm intelligence mainly involves mathematical models, convergence, and time complexity. Swarm intelligence algorithms solve complex optimization problems by simulating group behavior in nature. Common algorithms include Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO).

However, swarm intelligence faces various challenges and limitations in practical applications. Firstly, the mechanism of swarm intelligence stimulation and aggregation is not yet clear, and there is limited understanding of swarm intelligence systems. Secondly, the lack of high-quality training data is also an important issue. In addition, in the research of generalized swarm intelligence technology, there are multiple dynamic data sources, with vague and diverse goals. The correlations between group behavior patterns are often more deeply complex. Ethical issues such as privacy protection also pose challenges to research.

(II) Characteristics of Swarm Intelligence

1. Distributed: Swarm intelligence is composed of numerous dispersed individuals, with no central control node, exhibiting distributed characteristics.

2. Self-organized: Individuals interact and collaborate through simple rules, spontaneously forming ordered structures and behaviors, demonstrating self-organized characteristics.

3. Adaptive: Swarm intelligence can adjust its behavior and structure according to environmental changes, demonstrating strong adaptability.

4. Robust: Since swarm intelligence is composed of numerous individuals, even if some individuals fail, the entire system can still maintain certain functions, demonstrating strong robustness.

II. Concept and Architecture of the "Sense-Fusion-Computing" Vehicle-Road-Cloud Network

(I) Meaning of "Sense-Fusion-Computing" Vehicle-Road-Cloud Network

The "Sense-Fusion-Computing" vehicle-road-cloud network is a new network architecture that integrates communication, sensing, and computing. It connects vehicles, roadside equipment, and cloud platforms through wireless communication technologies such as 5G and C-V2X (Vehicle-to-Everything), enabling real-time data transmission and processing. This network architecture not only provides system-level real-time digital twin services but also solves group safety, efficiency, and game problems at the system level.

Specifically, the "Sense-Fusion-Computing" vehicle-road-cloud network includes the following key components:

Communication Layer: Utilizes 5G and C-V2X technologies to enable efficient communication between vehicles, roadside equipment, and cloud platforms.

Perception Layer: Collects environmental information through onboard sensors and roadside perception systems, including road conditions, traffic conditions, pedestrians, etc.

Computation Layer: The cloud platform performs data processing and analysis, providing decision support and control strategies.

Application Layer: Based on the support of the above three layers, realizes various applications of intelligent transportation systems, such as autonomous driving and traffic management.

(II) Vehicle-Road-Cloud Network Architecture

The "Sense-Fusion-Computing" vehicle-road-cloud network consists of three parts: vehicles, roads, and the cloud.

1. Vehicles: As mobile perception nodes and computing terminals, vehicles are equipped with various sensors and communication devices, capable of real-time collection of surrounding environmental information and communication with roads and the cloud. Vehicles can also realize autonomous driving, intelligent navigation, and other functions through computing technology.

2. Roads: Various sensors and communication devices are installed on the roads to realize real-time monitoring and information transmission of traffic conditions. Roads can also interact with vehicles and the cloud to realize intelligent control of traffic signals, traffic condition warnings, and other functions.

3. Cloud: The cloud is responsible for storing, analyzing, and processing the massive data uploaded by vehicles and roads, providing intelligent decision support for vehicles and roads. The cloud can also interact with other systems to realize traffic management, urban planning, and other functions.

III. Role of "Sense-Fusion-Computing" Vehicle-Road-Cloud Network in Building Swarm Intelligence

(I) Providing an Information Interaction Platform

The "Sense-Fusion-Computing" vehicle-road-cloud network provides an efficient information interaction platform between vehicles, roads, and the cloud. Through this platform, vehicles can obtain real-time information about roads and the surrounding environment, roads can understand the location and status of vehicles, and the cloud can analyze and predict the overall traffic situation. This information interaction enables better collaboration and coordination among individuals, providing the foundation for the formation of swarm intelligence.

(II) Realizing Intelligent Perception and Decision-Making

The sensing and computing technologies in the "Sense-Fusion-Computing" vehicle-road-cloud network enable intelligent perception and decision-making for vehicles, roads, and the surrounding environment. Sensing technology can acquire various status information, and computing technology can analyze and process this information to provide intelligent decision support for vehicles and roads. For example, vehicles can automatically adjust their speed and route based on perceived road conditions, and roads can automatically adjust traffic light timing based on traffic flow. This intelligent perception and decision-making capability enable individuals to complete tasks more efficiently, improving the intelligence level of the entire system.

(III) Promoting Distributed Collaboration

The distributed architecture of the "Sense-Fusion-Computing" vehicle-road-cloud network enables distributed collaboration between vehicles, roads, and the cloud. Individuals interact and collaborate through simple rules, spontaneously forming ordered structures and behaviors. For example, vehicles can collaborate with other vehicles to form convoys, improving driving efficiency and safety; roads can collaborate with surrounding roads to achieve balanced distribution of traffic flow. This distributed collaboration capability is crucial for building swarm intelligence.

(IV) Enhancing System Adaptability and Robustness

The "Sense-Fusion-Computing" vehicle-road-cloud network can adjust its behavior and structure according to environmental changes, demonstrating strong adaptability and robustness. When some vehicles or roads fail, the system can ensure the normal operation of the entire transportation system by redistributing tasks and resources. At the same time, the system can dynamically adjust traffic management strategies according to different traffic conditions and demands, improving system efficiency and adaptability.

IV. Technical Challenges and Solutions for Building Swarm Intelligence with "Sense-Fusion-Computing" Vehicle-Road-Cloud Network

(I) Technical Challenges

Data Fusion and Processing: The massive data generated in the "Sense-Fusion-Computing" vehicle-road-cloud network requires efficient fusion and processing to extract valuable information. However, different types of data have different characteristics and formats, posing a challenge in achieving efficient data fusion and processing.

Communication Reliability: Vehicles need to maintain stable communication connections with roads and the cloud during high-speed movement. However, the wireless communication environment is complex and variable, posing a challenge in ensuring communication reliability.

Security and Privacy Protection: The "Sense-Fusion-Computing" vehicle-road-cloud network involves a large amount of sensitive information, such as vehicle locations and driving trajectories. Ensuring the security and privacy protection of this information poses a challenge.

Algorithm Optimization: Building swarm intelligence requires efficient algorithm support. However, existing algorithms often suffer from inefficiency and slow convergence when processing large-scale distributed systems. Optimizing algorithms to meet the needs of the "Sense-Fusion-Computing" vehicle-road-cloud network poses a challenge.

(II) Solutions

Data Fusion and Processing: Employ advanced data fusion techniques, such as multi-sensor fusion and data mining, to fuse and process different types of data. Simultaneously, utilize cloud computing and edge computing technologies to achieve distributed storage and processing of data, improving data processing efficiency and reliability.

Communication Reliability: Combine multiple communication technologies, such as 5G and V2X, to improve communication reliability and stability. Simultaneously, optimize communication protocols and algorithms to reduce communication delay and packet loss rates.

Security and Privacy Protection: Use encryption, identity authentication, access control, and other technologies to ensure information security and privacy protection. Simultaneously, establish and improve a security management system, strengthen system monitoring and maintenance, and promptly detect and address security vulnerabilities.

Algorithm Optimization: Research and develop swarm intelligence algorithms suitable for the "Sense-Fusion-Computing" vehicle-road-cloud network, such as distributed optimization algorithms and reinforcement learning algorithms. Simultaneously, optimize and improve algorithms based on actual application scenarios to enhance algorithm efficiency and performance.

V. How to Achieve the Integration of Single-Point Intelligence and System Intelligence through the "Sense-Fusion-Computing" Vehicle-Road-Cloud Network?

Achieving the integration of single-point intelligence and system intelligence through the "Sense-Fusion-Computing" vehicle-road-cloud network primarily relies on the following key technologies and strategies:

System-Level Real-Time Digital Twin Services: By establishing the "Sense-Fusion-Computing" network system, provide system-level real-time digital twin services for vehicles (including autonomous and non-autonomous vehicles) and various smart devices (such as robots and drones). This service can solve complex problems at the system level, realizing the integration of single-point intelligence and system intelligence.

Convergent Intelligent Control Technology: Convergent intelligent control technology integrates multiple intelligent technologies and control strategies to address the insufficient effectiveness, adaptability, and scalability of traditional control technologies in nonlinear systems, complex systems, and systems with uncertain factors. This technology aims to enhance the scientific and technological level, robustness, and adaptability of control systems.

End-to-End AI: The enhancement of single-point intelligence capability is often insufficient to change the overall system efficiency. Highly systematic and coordinated end-to-end AI is required to achieve comprehensive capacity improvements. Through end-to-end AI, a closed-loop system can be realized, from sensing the physical world to digitization and complex computation, and then applying control from the digital world to the physical world.

Multi-Agent Technology: Multi-agent technology has unique advantages in solving complex industrial problems. Each agent has independence and autonomy, maintaining consistency in the global system. This technology facilitates the integration of single-point intelligence and global intelligence.

Cyber-Physical Systems (CPS): CPS integrates advanced sensing, computing, communication, control, and other information technologies with automatic control technology to construct a system where people, machines, objects, environments, and information in the physical and virtual worlds are mapped to each other and interact in a timely manner. This system enables seamless integration of single-point intelligence and system intelligence.

VI. Case Study: Collaboration between China Mobile Beijing and MoGo Auto

The collaboration between China Mobile Beijing and MoGo Auto is a typical case of the "Sense-Fusion-Computing" vehicle-road-cloud network. Both parties have built a vehicle-road-cloud network and established a "Sense-Fusion-Computing" network system to provide system-level real-time digital twin services for vehicles and various smart devices (such as robots and drones). By addressing group safety, group efficiency, and group game problems at the system level through the vehicle-road-cloud network, they have laid the foundation for building an integrated network of single-point intelligence and system intelligence.

In practical applications, this network system has jointly promoted the 5G vehicle-road-cloud joint testing project in Beijing Yizhuang's high-level autonomous driving demonstration zone. The project, based on MoGo Auto's AI digital road base stations, autonomous driving vehicles/multi-brand intelligent connected vehicles, Mobile Cloud, and Mobile 5G private network, conducted tests on end-to-end data application latency and roadside perception accuracy of the vehicle-road-cloud network and enabled advanced application scenarios of high-level perception flow for traffic data to empower connected vehicles.

After 24 days, the test results showed excellent performance across all indicators. Specifically, the end-to-end application latency of the vehicle-road-cloud network was 91.09ms, significantly better than the industry standard of less than 200ms, fully meeting the latency requirements of vehicle-road collaborative autonomous driving. The 5G private network air interface latency reached 10.55ms. The data quality of roadside cloud infrastructure met the industry's highest "Double SL3" technical requirements (China Academy of Information and Communications Technology - 2024), fully satisfying the needs of automakers for roadside data quality and laying a solid foundation for data application in vehicles.

This test also demonstrated that MoGo Auto's integrated vehicle-road-cloud solution supports both C-V2X and 5G-A communication methods, enabling flexible deployment based on different infrastructure conditions in various regions, providing strong support for large-scale implementation of the integrated vehicle-road-cloud network.

VII. Application Prospects of Building Swarm Intelligence with "Sense-Fusion-Computing" Vehicle-Road-Cloud Network

1. Intelligent Transportation

In the Internet of Vehicles scenario, the Sense-Fusion-Computing integration technology can recognize and perceive the road itself and its environment, including the location, speed, and direction of movement of vehicles. This enables vehicles to perceive the surrounding environment in real-time, improving driving safety and efficiency.

Through the Sense-Fusion integration technology of 5G base stations, traditional roadside radars can be replaced in vehicle-road collaboration scenarios to perceive vehicles and pedestrians, thereby reducing subsequent engineering maintenance costs. The application of this technology not only improves the intelligence level of traffic management but also reduces the construction and maintenance costs of infrastructure.

In the vehicle-road-cloud network, by establishing the "Sense-Fusion-Computing" network system, system-level real-time digital twin services are provided for vehicles and various smart devices, addressing group safety, group efficiency, and group game problems. This service enables comprehensive monitoring and optimization of the transportation system, enhancing overall traffic management efficiency.

The Sense-Fusion integration technology achieved over-the-horizon perception for the first time in urban road environments, providing highly reliable guarantees for real-time perception of vehicles and pedestrians as well as comprehensive perception of roads. The application of this technology greatly enhances the response speed and safety of the transportation system.

2. Smart City

The Sense-Fusion-Computing integrated network realizes deep integration of multi-dimensional perception, collaborative communication, and intelligent computing functions through the collaboration and sharing of software and hardware resources, enabling the network to possess new capabilities for intelligent interaction and processing of closed-loop information flow and wide-area intelligent collaboration. This capability provides strong technical support for the construction of smart cities.

In smart homes and urban infrastructure, the Sense-Fusion-Computing integration technology can realize real-time monitoring and management of the environment, enhancing the intelligence level of urban management. For example, in smart streetlights and intelligent parking, real-time analysis and processing of sensory data can optimize resource allocation and improve urban operational efficiency.

In security protection and drone regulation, the Sense-Fusion-Computing integration technology also demonstrates strong application potential. Through the integration of high-frequency communication and radar technology, real-time monitoring and management of specific areas can be achieved to ensure public safety.

The Sense-Fusion-Computing network has diverse application scenarios in intelligent transportation and smart cities, covering various aspects such as the Internet of Vehicles, vehicle-road collaboration, real-time digital twin services, over-the-horizon perception, multi-dimensional perception and intelligent computing, smart homes and urban infrastructure, security protection, and drone regulation.

END

As an emerging technical architecture, the "Sense-Fusion-Computing" vehicle-road-cloud network provides a solid foundation for building swarm intelligence. By providing an information interaction platform, realizing intelligent perception and decision-making, promoting distributed collaboration, and enhancing system adaptability and robustness, the "Sense-Fusion-Computing" vehicle-road-cloud network can effectively promote the formation and development of swarm intelligence. However, in the process of building swarm intelligence, the "Sense-Fusion-Computing" vehicle-road-cloud network also faces technical challenges such as data fusion and processing, communication reliability, security and privacy protection, and algorithm optimization. By adopting advanced data fusion technologies, combining multiple communication technologies, employing encryption and other security measures, and optimizing algorithms, these challenges can be effectively overcome, promoting the application of the "Sense-Fusion-Computing" vehicle-road-cloud network in building swarm intelligence.