对比之后，VLA的成熟度远高于世界模型...

Core Insights - The article discusses the competition between VLA (Vision-Language Action) models and world models in the field of end-to-end autonomous driving, highlighting that over 90% of current models are segmented end-to-end rather than purely VLA or world models [2][6]. Group 1: Model Comparison - VLA models, represented by companies like Gaode Map and Horizon Robotics, show superior performance compared to world models, with the latest VLA papers published in September 2023 [6][43]. - The performance metrics of various models indicate that VLA models outperform world models significantly, with the best VLA model achieving an average L2 distance of 0.19 meters and a collision rate of 0.08% [5][6]. Group 2: Data Utilization - The Shanghai AI Lab's GenAD model utilizes unlabelled data sourced from the internet, primarily YouTube, to enhance generalization capabilities, contrasting with traditional supervised learning methods that rely on labeled data [7][19]. - The GenAD framework employs a two-tier training approach similar to Tesla's, integrating diffusion models and Transformers, but requires high-precision maps and traffic rules for effective operation [26][32]. Group 3: Testing Methods - Two primary testing methods for end-to-end autonomous driving are identified: open-loop testing using synthetic data in simulators like CARLA, and closed-loop testing based on real-world collected data [4][6]. - The article emphasizes the limitations of open-loop testing, which cannot provide feedback on the execution of predicted actions, making closed-loop testing more reliable for evaluating model performance [4][6]. Group 4: Future Directions - The article suggests that while world models have potential, their current implementations often require additional labeled data, which diminishes their advantages in generalization and cost-effectiveness compared to VLA models [43]. - The ongoing research and development in the field indicate a trend towards improving the integration of various data sources and enhancing model robustness through advanced training techniques [19][32].