---
title: "President of the China Association for Science and Technology Wan Gang: Exploring generative artificial intelligence solutions based on autonomous driving large models"
type: "News"
locale: "en"
url: "https://longbridge.com/en/news/233720595.md"
description: "At the China Electric Vehicle 100 Forum, Wan Gang, Chairman of the China Association for Science and Technology, emphasized the promotion of intelligent connected systems and the interconnection of digital urban traffic information with in-vehicle navigation. He mentioned exploring generative artificial intelligence solutions based on large autonomous driving models to enhance the game-playing ability of autonomous vehicles, and achieving the intelligence of autonomous driving through data uploading, model training, and scenario validation"
datetime: "2025-03-29T08:51:52.000Z"
locales:
  - [zh-CN](https://longbridge.com/zh-CN/news/233720595.md)
  - [en](https://longbridge.com/en/news/233720595.md)
  - [zh-HK](https://longbridge.com/zh-HK/news/233720595.md)
generator: "portal-rs"
---

# President of the China Association for Science and Technology Wan Gang: Exploring generative artificial intelligence solutions based on autonomous driving large models

At the China Electric Vehicle 100 Forum 2025 held today, Wan Gang, Chairman of the China Association for Science and Technology, stated that it is essential to continuously promote the collaborative integration of vehicles, roads, and cloud systems in intelligent connected networks. By taking the bidirectional integration of digital maps and traffic control information as a starting point, we should promote the interconnection of digital urban traffic information and in-vehicle navigation information. With the collaborative empowerment of "big data green wave navigation + real-time traffic signals," we aim to enhance urban traffic efficiency. Exploring generative artificial intelligence solutions based on large models for autonomous driving will improve the game-theoretic capabilities of autonomous vehicles based on traffic rules. This requires exploration under the collaboration of vehicles, roads, and cloud systems, through data annotation uploads, training of large autonomous driving models, experimental validation of typical scenarios, and OTA downloads of automotive-grade real-time models, continuously iterating and evolving during practical applications to achieve embodied intelligence in end-to-end autonomous vehicles

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> **Disclaimer: This article is for reference only and does not constitute any investment advice.**