为什么定义2000 TOPS + VLA + VLM为L3 级算力？

Core Viewpoint - The article discusses the advancements in autonomous driving technology, particularly focusing on Xiaopeng Motors' recent paper presented at CVPR 2025, which validates the scaling laws in the context of autonomous driving and introduces new standards for computing power in Level 3 (L3) autonomous vehicles [4][6][22]. Group 1: Scaling Laws and Model Performance - Xiaopeng Motors' paper systematically verifies the effectiveness of scaling laws in autonomous driving, indicating that larger model parameters lead to improved performance [4][6]. - The research establishes a clear power-law relationship between model performance, parameter scale, data scale, and computational power, originally proposed by OpenAI [4][6]. Group 2: Computing Power Standards - The paper introduces a new computing power standard of 2000 TOPS for L3 autonomous driving, highlighting the exponential increase in computational requirements as the driving level advances [8][20]. - For L2 systems, the required computing power ranges from 80 to 300 TOPS, while L3 systems necessitate thousands of TOPS due to the complexity of urban driving scenarios [8][20]. Group 3: VLA and VLM Model Architecture - Xiaopeng's VLA (Vision-Language-Action) model architecture integrates visual understanding, reasoning, and action generation capabilities, requiring substantial computational resources [10][12]. - The architecture's visual processing module alone demands hundreds of TOPS for real-time data fusion from multiple sensors [10][12]. Group 4: Comparison of Onboard and Data Center Computing Power - The article differentiates between onboard computing power, which focuses on real-time data processing for driving decisions, and data center computing power, which is used for offline training and model optimization [12][15]. - Onboard systems must balance real-time performance and power consumption, while data centers can leverage significantly higher computational capabilities for complex model training [12][15]. Group 5: Market Dynamics and Competitive Landscape - The market for AI chips in autonomous driving is dominated by a few key players, with NVIDIA holding a 36% market share, followed by Tesla and Huawei [20]. - The competitive landscape has shifted significantly since 2020, impacting the development of AI chips and their applications in autonomous driving [17][20].