正文 · AI 翻译
IT Home reported on November 7 that Geely Auto Group and the China Meteorological Administration's Earth System Numerical Prediction Center formally signed a strategic cooperation framework agreement on November 5.
The two parties jointly launched the industry's first “Zhixiang-Xingrui Traffic Weather Large Model.” Focused on extreme weather scenarios such as icing and dense fog patches, the model can monitor weather conditions at the lane level in real time and provide alerts, supporting further improvements in driving safety and the reliable operation of autonomous driving systems in complex weather conditions.
For blind spots where the weather is visible but conditions on specific road sections are unclear—such as sudden drops in visibility, black ice forming on particular bridges or shaded road sections, and heavy rain causing standing water on localized stretches of road—the “Zhixiang-Xingrui Traffic Weather Large Model” has already produced preliminary lane-level visibility, black ice, and heavy rain risk alerts through its “Beyond-Line-of-Sight Safety Warning” feature. Using connections among multiple vehicles, it can intelligently identify hazardous road conditions such as ice and proactively issue alerts.
IT Home has learned that the model is planned to enter production first in the Lynk & Co 900L, before gradually being applied to vehicles from multiple brands, including Geely, Galaxy, Lynk & Co, and Zeekr.
In addition, in the first quarter of next year, Geely Auto Group and the China Meteorological Administration's Earth System Numerical Prediction Center will jointly release the industry's first “General-Purpose Weather Model.”
In the future, the two parties will also work together to advance the proposal for a national standard titled “Specifications for the Collection and Processing of Meteorological Environment Data for Intelligent Connected Vehicles,” aiming to develop a technical approach and standards suited to the realities of China's domestic industry in the field of onboard weather observation.