Core Insights - The article discusses the evolution of autonomous driving technology, emphasizing the transition from data closed-loop systems to training closed-loop systems, which focus on real-world utility and evaluation of progress [13][14]. Group 1: Data and Infrastructure - The company has accumulated 1.5 billion kilometers of driving data, which is crucial for training autonomous systems [8]. - A closed-loop data system is in place, utilizing over 200 trigger data points for training datasets, with clips ranging from 15 to 45 seconds [8]. - The data scaling law indicates a significant increase in the number of clips used for training, with projections showing up to 600 million clips by 2025 [10]. Group 2: Technology Stack - The key technology stack for autonomous driving includes regional-scale simulation, synthetic data, reinforcement learning, and multimodal generation [18]. - The focus is on enhancing simulation quality through advanced techniques like scene reconstruction and traffic agent modeling [18][19]. - The transition from reconstruction to generation in simulation is highlighted, utilizing diffusion models for improved scene generation [19]. Group 3: Training and Evaluation - The article emphasizes the importance of building a training closed-loop that integrates various models, including VLA (Vision-Language Alignment) and reinforcement learning [15]. - The evaluation environment and reward systems are critical for assessing the performance of autonomous driving systems [14][35]. - Interactive agents are identified as a key challenge in the training closed-loop, necessitating accurate feedback and generalization ability [38][40]. Group 4: Future Directions - The company is working on various projects aimed at enhancing both reconstruction and generation capabilities, with milestones set for 2024 and 2025 [21][24]. - The application of generated data includes scene editing, scene transfer, and scene generation, which are essential for improving the realism of simulations [27][33].