自动驾驶之心·2025-08-05 23:32Core Viewpoint - The article highlights the launch of the Li Auto i8, which features significant upgrades in its driver assistance capabilities, particularly through the integration of the VLA (Vision-Language-Action) model, marking a milestone in the mass production of autonomous driving technology [2][3]. Summary by Sections VLA Model Capabilities - The VLA model enhances understanding of semantics through multimodal input, improves reasoning with a thinking chain, and aligns more closely with human driving intuition. Its four core capabilities include spatial understanding, reasoning ability, communication and memory, and behavioral ability [3][6]. Industry Development - The VLA represents a new milestone in the mass production of autonomous driving, with many companies investing in human resources for research and development. The transition from E2E (End-to-End) and VLM (Vision-Language Model) to VLA indicates a progressive technological evolution [5][8]. Educational Initiatives - In response to the growing interest in transitioning to VLA-related roles, the industry has launched a specialized course titled "End-to-End and VLA Autonomous Driving Small Class," aimed at providing in-depth knowledge of the algorithms and technical development in this field [7][15]. Course Structure and Content - The course covers various aspects of end-to-end algorithms, including historical development, background knowledge, and specific methodologies such as two-stage and one-stage end-to-end approaches. It emphasizes practical applications and theoretical foundations [21][22][23][24]. Job Market Insights - The demand for VLA/VLM algorithm experts is high, with salary ranges for positions varying based on experience and educational background. For instance, positions for VLA/VLM algorithm engineers typically offer salaries between 35K to 70K for those with 3-5 years of experience [11]. Learning Outcomes - Participants in the course are expected to achieve a level of understanding equivalent to that of an autonomous driving algorithm engineer with one year of experience, covering key technologies such as BEV perception, multimodal models, and reinforcement learning [32].