Investment Rating - The report does not explicitly state an investment rating for the smart driving industry Core Insights - The smart driving industry is experiencing rapid evolution driven by "end-to-end" and "smart driving equity" concepts, with significant growth in both new energy vehicle sales and smart driving functionalities [3][4][9] - The penetration rate of L2-level smart driving in new energy vehicles in China has increased from approximately 7% in 2019 to around 65% by the first half of 2025, indicating a strong correlation between new energy vehicle sales and the adoption of smart driving technologies [9][10] - The smart driving market is projected to exceed 5 trillion yuan by 2030, with a compound annual growth rate driven by technological advancements and increased consumer acceptance [15][16] Summary by Sections 1. "Equity + End-to-End" Accelerating Smart Driving Evolution - The smart driving industry has seen a significant increase in new energy vehicle sales, which has created a positive feedback loop for the adoption of smart driving technologies [9][10] - The penetration of L2-level smart driving features in new energy vehicles has rapidly increased, reflecting the growing consumer acceptance and market expansion of smart driving technologies [9][10] 2. End-to-End Smart Driving Review - The evolution of end-to-end smart driving can be categorized into four main stages, with advancements in perception, decision-making, and control processes [30][32] - The introduction of the "occupancy network" has enhanced environmental perception capabilities, allowing for more accurate and stable decision-making in complex driving scenarios [46][47] 3. VLA Technology Route - The VLA (Vision-Language-Action) model is emerging as a key driver of paradigm shifts in autonomous driving, integrating visual, linguistic, and action modalities into a cohesive framework [70][71] - The VLA model's development is divided into four stages, with significant advancements in task understanding and execution capabilities [76][77] 4. World Model Technology Route - The world model approach emphasizes physical reasoning and spatial understanding, representing a long-term evolution path for smart driving technologies [69][70] - The integration of world models with cloud computing is expected to enhance the iterative optimization of end-to-end smart driving systems [65][66]

Core Viewpoint - The article discusses the challenges and methodologies in automating the labeling of training data for occupancy networks (OCC) in autonomous driving, emphasizing the need for high-quality data to improve model generalization and safety [2][10]. Group 1: OCC and Its Importance - The occupancy network aims to partition space into small grids to predict occupancy, addressing irregular obstacles like fallen trees and other background elements [3][4]. - Since Tesla's announcement of OCC in 2022, it has become a standard in pure vision autonomous driving solutions, leading to a high demand for training data labeling [2][4]. Group 2: Challenges in Automated Labeling - The main challenges in 4D automated labeling include: 1. High temporal and spatial consistency requirements for tracking dynamic objects across frames [9]. 2. Complexity in fusing multi-modal data from various sensors [9]. 3. Difficulty in generalizing to dynamic scenes due to unpredictable behaviors of traffic participants [9]. 4. The contradiction between labeling efficiency and cost, as high precision requires manual verification [9]. 5. High requirements for generalization in production scenarios, necessitating data extraction from diverse environments [9]. Group 3: Training Data Generation Process - The common process for generating OCC training ground truth involves: 1. Ensuring consistency between 2D and 3D object detection [8]. 2. Comparing with edge models [8]. 3. Involving manual labeling for quality control [8]. Group 4: Course Offerings - The article promotes a course on 4D automated labeling, covering the entire process and core algorithms, aimed at learners interested in the autonomous driving data loop [10][26]. - The course includes practical exercises and addresses real-world challenges in the field, enhancing algorithmic capabilities [10][26]. Group 5: Course Structure - The course is structured into several chapters, including: 1. Basics of 4D automated labeling [11]. 2. Dynamic obstacle labeling [13]. 3. Laser and visual SLAM reconstruction [14]. 4. Static element labeling based on reconstruction [16]. 5. General obstacle OCC labeling [18]. 6. End-to-end ground truth labeling [19]. 7. Data loop topics, addressing industry pain points and interview preparation [21].