Driven by the 5G wave, applications such as vehicle-road collaboration, smart parking, intelligent transportation planning, and autonomous driving are generally put on the agenda. At the same time, 5G has brought about a huge explosion in the amount of data. More and more applications are running in the cloud, and many Specific application scenarios will have very strict latency requirements. With the rapid increase of strong real-time data, pre-processing is performed on the edge side, so “edge computing” emerged in the field of intelligent transportation.
What is edge computing
Edge Computing is the core link in the 5G network architecture. It refers to a distributed open platform that integrates core capabilities such as network, computing, storage, and applications at the edge of the network close to the source of things or data. Edge computing is a distributed computing framework that brings enterprise applications closer to data sources, such as IoT devices or local edge servers. This approach to the data source can bring huge business benefits: faster insights, better response times, and better bandwidth availability.
In the era of Internet of Everything, massive devices will be connected to the network for data collection and user interaction. Edge computing is often associated with the Internet of Things. IoT devices engage in increasingly powerful processing, so the vast amounts of data generated can be re-migrated to the “edge” of the network. This means data does not have to be continuously transferred back and forth between centralized servers for processing. As a result, edge computing is more efficient at managing large amounts of data from IoT devices, with lower latency, faster processing, and scalability. Under the multiplier effect of 5G and AI, which expands the high-bandwidth and low-latency capabilities of wireless data transmission, people are full of expectations for what edge computing can achieve. This technology will greatly increase the speed of edge computing systems and ultimately enhance their ability to support real-time applications.
According to IDC predictions, the number of global IoT connections will grow to 27 billion in 2025, and the number of IoT devices will reach 100 billion. The total amount of global data is expected to reach 163 ZB in 2025, and more than 70% of data and applications will be in the future. Will be generated and processed at the edge.
In the early days of cloud computing, many people believed that the value of the terminal had reached its limit. All data through the network would be transmitted to the cloud for processing and calculation, and then returned to the user’s terminal. But the development of the facts is somewhat unexpected. Many application scenarios have very strict requirements on latency. If you rely entirely on the cloud, efficiency will inevitably decrease.
Application of edge computing in intelligent transportation
The implementation of intelligent transportation is a huge system project. In addition to the adjustment of urban space and roads, you also need a stable and reliable management system that combines the latest software and hardware technologies to cope with complex applications in various scenarios. According to a consulting research report by McKinsey, transportation accounts for the highest proportion of industry applications of edge computing.
As the amount of urban traffic data increases, users’ real-time needs for massive traffic information will also increase. If all data is transmitted back to the cloud computing center, problems such as waste of bandwidth resources and delays will occur. However, if the data is analyzed and processed in real time on the edge server, users can make decisions based on real-time road conditions and available resources. Instructions accordingly.
The application of edge computing in transportation is in geographical location-based applications such as smart city transportation and facility management. For location identification technology, edge computing can process and collect geographical location-based data in real time without having to transmit it to a cloud computing center. Take appropriate action.
In addition, in the application of urban video surveillance systems, a new video surveillance application software and hardware service platform that integrates edge computing models and video surveillance technology can be built to improve the intelligent processing capabilities of the front-end cameras of the video surveillance system, thereby affecting the early warning system and disposal mechanism.
It is not difficult to see that “cloud computing” is equivalent to the brain of smart devices, processing relatively complex processes, while “edge computing” is equivalent to the nerve endings of smart devices, performing some “subconscious” reactions, which has long plagued the development of the industry. There is hope for solutions to many of the problems.
Application of edge computing in vehicle-road collaboration
The most typical application scenarios such as smart cars cannot transmit data to the cloud for processing before making judgments. Instead, a large amount of real-time data needs to be collected. This is the value of edge computing.
For example, when a self-driving car faces danger and needs to stop in time, it needs to upload data to the “cloud”, calculate the stop command, and then transmit it to the car, and the car will respond. Then it is better to let the vehicle itself have certain computing power to deal with this problem. At the same time, we can also envision a scenario where sudden natural disasters, signal interference or technical failures cause self-driving cars and trains in a certain area to fall into a network-free state. Then, they can only rely on the computing power given to them by edge computing to make “knee-jerk” reactions to ensure their safety.
In addition, intelligent transportation is developing from single-scenario traffic management to integrated scenario transportation services. V2X scenarios can help intelligent driving to be safer, more efficient, more economical, and more convenient, such as speed limit warnings, severe weather warnings, merging reminders, and intersection dispatching. etc. The key technologies of V2X include perception, high-definition mapping and positioning. High computing power requirements, high mobility, high reliability and real-time performance bring major technical challenges, and edge computing will have more technologies in the field of vehicle-road collaboration. Break through space.
Application of edge computing in static traffic
The application of edge computing in smart parking is also reflected in the parking control system and the Internet of Vehicles, and affects its future parking number and road traffic planning. At present, smart parking has been included in the strategic construction of new infrastructure and smart cities. Edge computing helps smart parking become more mature and perfect. Smart parking management methods have made great strides, accelerating the integration of technology and business.
Big data of static traffic information is snowballing over time, and cloud storage and other similar services are under pressure from a large number of complex data processing requests. The construction of smart parking relies on a single centralized processing cloud computing model that cannot cope with all problems. It requires the integration of multiple computing models to solve these problems. The edge computing model can migrate computing to the maximum extent close to the data source, and actual needs are processed at the edge of the computing model. Edge computing has several major advantages in smart parking such as massive data processing, low latency, and location awareness.
Résumer
At present, China’s edge computing market is still in the early stage of the industry and has great potential for technological explosion. Various types of industry players are actively planning to seize the market. Major manufacturers and scientific research institutions are formulating standards and specifications. Although no consensus has been reached, multiple industry alliances have been formed at home and abroad to vigorously promote the standards and technological progress of edge computing. How to unify the production standards of these devices requires some important companies in the field of intelligent transportation to take the lead in formulating standards.
The arrival of edge computing makes smart transportation safer. Whether it is road, rail, maritime or aviation, safety is the most important thing in the transportation industry. I believe that in the future, there will be more technological breakthroughs in the field of intelligent transportation “edge computing” and can effectively improve our daily transportation life.