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In today's era of rapid technological development, autonomous driving is undoubtedly one of the most innovative and challenging fields. However, it still faces many challenges in terms of data, computing power, and optimization of large models. The technology has not yet been finalized, and large-scale replication is still difficult. But with the development of vehicle-road-cloud integration, we may see new hope. Can AI work wonders in this field?

As the name implies, vehicle-road-cloud integration is the close integration of vehicles, roads and the cloud to form a coordinated intelligent system. In this system, vehicles are no longer isolated individuals, but are interconnected and communicate with the entire traffic environment. Mushroom Autolink, a leading domestic provider of vehicle-road-cloud integration and full-stack technology and operation services for autonomous driving, has demonstrated significant advantages in the roadside, vehicle-side and cloud side of vehicle-road-cloud integration, providing strong support for solving the problem of autonomous driving.

In terms of roadside, Mushroom Car Link's advantage lies in its powerful perception and data collection capabilities. By deploying self-developed AI digital road base stations and roadside edge perception systems (MRS) on both sides of the road, it is possible to obtain rich road condition information in real time, including road conditions, traffic flow, the behavior of pedestrians and other vehicles, etc. These data not only provide vehicles with a more comprehensive environmental perception, but also provide valuable materials for the training of large models. Compared with traditional single-vehicle perception, roadside perception can break through the limitations of the vehicle's own sensors, detect potential dangers and complex situations in advance, and provide more time and information for autonomous driving decisions. For example, at intersections without signal lights, roadside equipment can perceive the dynamics of vehicles and pedestrians in the intersecting direction in advance, and provide accurate warnings for autonomous driving vehicles that are about to pass, thereby avoiding potential collision risks. In severe weather conditions, such as rain, snow, fog and haze, high-definition cameras and meteorological monitoring equipment on the roadside can provide vehicles with clearer and more accurate road condition information, helping vehicles make safer driving decisions.

The vehicle side is the key link for direct interaction with drivers and passengers. Based on L4 autonomous driving technology, Mogo AutoLink has fully developed the MogoAP autonomous driving system and installed it in a variety of autonomous driving vehicles. It has jointly developed a number of pre-installed mass-produced autonomous driving models with car companies, and has realized the application of semi-solid-state laser radar and self-developed Orin domain controller on a variety of models, which has better potential and performance.

By providing intelligent connected vehicles with efficient local computing power and optimized algorithms, it is able to process large amounts of data in an instant and make real-time decisions. Even when the connection with the cloud and the roadside is temporarily interrupted, the vehicle can rely on its own intelligent system to maintain a certain degree of autonomous driving capabilities to ensure driving safety and stability. In addition, Mushroom Car Union focuses on the coordinated optimization of vehicle hardware and software to reduce energy consumption and improve performance. Through precise sensor fusion and intelligent control strategies, the vehicle can automatically adjust the driving mode and power output according to real-time road conditions and energy consumption, achieving efficient energy utilization and smooth and comfortable driving.

In the cloud, Mushroom Auto Union has established a powerful data processing and analysis center. The cloud control platform can present the overall picture of traffic through real-time digital processing, and obtain massive data such as the location, direction, speed, etc. of all traffic participants. The role of the cloud control platform supported by real-time traffic data and algorithms has been shown in road traffic, allowing people, intelligent networked vehicles, autonomous driving vehicles, and traffic managers to participate together, greatly improving the systematicity, safety and efficiency of traffic.

Cloud servers have a large amount of high-performance hardware resources that can support large-scale parallel processing and storage of massive amounts of data. These data come not only from real-time collection on the roadside and vehicle side, but also from historical data and simulation data. Through in-depth mining and analysis of these data, using advanced machine learning algorithms and artificial intelligence technology, the cloud can train more accurate and intelligent large models. For example, by learning from a large amount of extreme traffic situation data, the large model can predict the best driving strategy for vehicles in similar scenarios, and make early warnings and decisions. At the same time, the cloud can also update and optimize the vehicle-side model in real time to ensure that the vehicle always has the latest intelligent driving capabilities.

Back to the challenges faced by autonomous driving, the integrated vehicle-road-cloud model provides new ideas for solving these problems.

In order to solve the problem of data collection, labeling and maintenance, the integration of vehicle, road and cloud can realize the fusion of multi-source data. The large amount of real-time data collected by roadside equipment, combined with the driving data on the vehicle side, ensures the diversity and richness of the data. At the same time, through the powerful computing power of the cloud, these data can be automatically labeled and cleaned to improve data quality and availability. Especially for those rare but critical extreme traffic situation data, the integration of vehicle, road and cloud can more comprehensively record and analyze these situations through monitoring and data fusion from multiple angles, providing more valuable samples for the training of large models.

In terms of computing power, the integration of vehicle, road and cloud realizes the rational allocation of resources. The vehicle side focuses on real-time decision-making and local data processing, using limited computing resources to ensure the execution of key functions. Complex model training and large-scale data processing are completed by the cloud, giving full play to the powerful computing power advantage of the cloud. At the same time, roadside equipment can also share some computing tasks, such as preprocessing some data, to reduce the burden on the vehicle side and the cloud. Through this collaborative approach, the contradiction between the limited computing power of the vehicle side and the growing demand for computing power in the cloud side is effectively resolved, and the operating efficiency of the entire system is improved.

Vehicle-road-cloud integration also has unique advantages in streamlining and optimizing large models. Large models trained on the cloud can be customized and streamlined and optimized according to the specific needs and hardware conditions of the vehicle. Through model compression technology, the number of model parameters and the amount of calculation can be reduced while maintaining its performance and accuracy. Roadside equipment can serve as an intermediate link to adapt and convert data on the vehicle and cloud to ensure smooth information transmission and effective execution of the model.

However, the development of vehicle-road-cloud integration is not all smooth sailing. In practical applications, a series of issues need to be resolved, such as the unification of communication protocols between different devices, data privacy protection, and system reliability and stability. However, with the continuous advancement of technology and the joint efforts of the industry, these issues are gradually being resolved.

In summary, the integration of vehicle, road and cloud has brought new opportunities and possibilities for the development of autonomous driving. Although there are still many challenges, with its synergistic advantages in data, computing power and model optimization, as well as the continuous innovation and exploration of companies such as Mushroom Car Union, we have reason to believe that AI can work wonders in the integration of vehicle, road and cloud, promote the large-scale application of autonomous driving technology as soon as possible, and bring people a safer, more convenient and efficient travel experience.

(Source: Financial Information)

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