Core Viewpoint - The article discusses the advancements in autonomous driving technology, particularly focusing on Xiaopeng Motors' approach to developing large foundation models for autonomous driving, emphasizing the transition from traditional software models to AI-driven models [4][6][32]. Group 1: Development of Autonomous Driving Models - Liu Xianming from Xiaopeng Motors presents the concept of foundational models in autonomous driving, highlighting the evolution from Software 1.0 to Software 3.0, where the latter utilizes data-driven AI models for vehicle operation [6][8]. - Xiaopeng is currently building an end-to-end AI model for driving, leveraging vast amounts of data collected from real-world vehicles to train a large visual model [8][9]. - The company aims to achieve L4-level autonomous driving by 2026, indicating a strong commitment to advancing its technology [13]. Group 2: Training Methodology - Xiaopeng's training methodology involves using a VLM (Vision Language Model) as a base, followed by pre-training with driving data to create a specialized VLA (Vision Language Action) model [15][30]. - The training process includes supervised fine-tuning (SFT) to ensure the model can follow specific driving instructions, enhancing its performance in real-world scenarios [27][30]. - Reinforcement learning is employed to refine the model further, focusing on safety, efficiency, and compliance with traffic rules [30]. Group 3: Data Utilization and Model Deployment - The article introduces the "inner loop" and "outer loop" concepts for model training, where the inner loop focuses on creating training flows for model expansion, and the outer loop utilizes data from deployed vehicles for continuous training [9][11]. - Xiaopeng's approach emphasizes the importance of high-quality data and computational power in developing effective autonomous driving solutions [32].