电动车公社·2025-07-22 15:27Core Viewpoint - The article discusses the evolution of artificial intelligence, particularly in the context of autonomous driving, highlighting the transition from basic systems to advanced world models that mimic human cognitive abilities [5][30][47]. Group 1: Historical Context - 37 years ago, Yang Lequn developed the first convolutional neural network for text digit recognition, laying the groundwork for AI advancements [1][2][3]. - The evolution of AI has led to significant breakthroughs, transitioning from "tool intelligence" to "cognitive intelligence" [5][6]. Group 2: Development of Autonomous Driving - Before 2016, autonomous driving systems relied on simple geometric features and could only handle static environments with limited accuracy [12][14]. - By 2020, deep learning technologies began to change spatial cognition paradigms, although models still required vast amounts of labeled data and struggled with three-dimensional spatial understanding [15][16]. - The introduction of LiDAR technology improved point cloud density, complementing camera systems and leading to hybrid architectures [20][21]. Group 3: World Models and Their Importance - The industry has shifted towards using OCC (Occupancy Grid) models, which simulate environments in 3D, eliminating reliance on high-precision maps [23]. - World models are essential for understanding complex scenarios, allowing systems to recognize various obstacles and make decisions similar to human drivers [30][56]. - The article emphasizes the limitations of current AI models, which, despite processing vast amounts of data, lack true understanding of the world and causal reasoning [28][29]. Group 4: Practical Applications and Future Prospects - NIO's world model demonstrates advanced capabilities, such as navigating parking lots and recognizing dynamic environments, showcasing the potential for future iterations [50][51][53]. - The ability to predict multiple scenarios based on real-time data illustrates the sophistication of world models compared to traditional systems [61][62]. - The article concludes by reflecting on the ongoing development of AI technologies and the anticipation of future advancements in the field [67][69].