XPENG expands XNGP urban intelligent driving coverage to 336 cities in China
Beijing (Gasgoo)- On June 18, Chinese new energy vehicle maker XPENG announced that its XNGP (XPENG Navigation Guided Pilot) system has been expanded to 14 additional cities this week. This includes six new cities in Shaanxi province and eight in Shanxi province. With this expansion, XNGP is now available in a total of 336 cities in China.
Photo credit: XPENG
Additionally, the safety validation distance for XNGP in Shaanxi, Shanxi, Jiangsu, and Zhejiang provinces has surpassed 31,100 kilometers. XPENG stated that XNGP will be available nationwide by the third quarter of this year.
XPENG's XNGP system is designed to handle various complex scenarios and cross-scenario navigation in major cities, particularly on key routes frequently used by local XPENG owners. In smaller cities and county-level areas, XPENG prioritizes opening key routes to ensure a relatively seamless and complete user experience. Future updates will target coverage of lower-tier roads with specific functionalities.
Moreover, in areas without high-precision maps, XPENG's XNGP leverages navigation maps,XNet perception capability, and driving strategies to deliver functionality that closely approximates that of regions with high-precision maps.
During a launch event, XPENG set new goals for the advancement of XNGP, including achieving full coverage of major city road networks across the country by the end of 2024, and commencing global development of expressway NGP. By 2025, XPENG plans to begin global development of XNGP.
XPENG's CEO and Chairman, He Xiaopeng, stated at the event that the next step is to fully implement end-to-end large models in XPENG vehicles. He also boldly claimed that XPENG aims to match the travel experience of Google's Waymo in core regions. He projected that passive takeover occurrences would be less than once every 1,000 kilometers in core areas, with autonomous driving times approaching those of human drivers. Additionally, efforts will be made to optimize performance in adverse weather and on irregular road surfaces.
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