谁能撬动自动驾驶汽车落地

Core Viewpoint - The article discusses the potential for autonomous vehicles to achieve rapid deployment through advancements in technology, cost reduction, and the evolution of social rules [1][2]. Technology: From Ideal to Reality - Technological innovation is the fundamental driver of new developments, with a significant shift from content-based generative AI to goal-driven intelligent agent AI expected to lead to breakthroughs in autonomous driving capabilities [3]. - Two main technological approaches in autonomous driving are identified: "end-to-end" technology, which requires vast amounts of high-quality data for training, and modular technology, which combines human-designed algorithms with neural networks [4]. - Current autonomous driving systems primarily offer driver assistance rather than full autonomy, constrained by technological capabilities and costs [4]. Cost: From Niche to Popularity - Cost reduction is crucial for the commercialization and widespread adoption of new products, as seen historically with the introduction of the Ford Model T, which made cars affordable for the middle class [7]. - Significant advancements in AI cost reduction, particularly in China, are expected to drive explosive applications in autonomous driving, with examples like DeepSeek achieving training costs significantly lower than competitors [8]. - Companies like Tesla are actively working on reducing costs, with projections for autonomous taxi services to be economically viable by 2026 [8]. Rules: From Phenomenon to Institutional Framework - The integration of autonomous driving into society requires adaptive rules and regulations, as technology alone cannot address all challenges [10]. - Historical precedents show that technological advancements often lead to societal and cultural shifts, necessitating a reevaluation of existing norms and values [11]. - Establishing long-term rules for autonomous driving is essential, particularly concerning safety, responsibility allocation, and the ethical implications of AI decision-making [13][14].