VLA和World Model世界模型，哪种自动驾驶路线会胜出？

Core Viewpoint - The article discusses the advancements and differences between Vision-Language-Action (VLA) models and World Models in the context of autonomous driving, emphasizing that while VLA is currently dominant, World Models possess inherent advantages in understanding and predicting physical realities [3][4][30]. Group 1: VLA vs. World Models - VLA currently dominates the market, with over 95% of global models generating videos for autonomous driving training rather than direct application [3]. - World Models are considered to have a significant theoretical advantage as they enable end-to-end learning without relying on language, directly linking perception to action [3][4]. - Proponents of World Models argue that they can understand the physical world and infer causal relationships, unlike VLA, which primarily mimics learned patterns [4][6]. Group 2: Development and Architecture - The World Model framework consists of three main modules: Vision Model (V), Memory RNN (M), and Controller (C), which work together to learn visual representations and predict future states [11]. - The architecture of World Models has evolved, with notable developments like RSSM and JEPA, which focus on combining deterministic and stochastic elements to enhance performance [15][17]. - JEPA, introduced in 2023, emphasizes predicting abstract representations rather than pixel-level details, significantly reducing computational requirements [17][19]. Group 3: Advantages and Challenges - World Models have two main advantages: they require less computational power than VLA and can utilize unlabelled data from the internet for training [19]. - However, challenges remain, such as the need for diverse and high-quality data to accurately understand physical environments, and the limitations of current sensors in capturing all necessary information [19][20]. - Issues like representation collapse and error accumulation in long-term predictions pose significant hurdles for the effective deployment of World Models [21][22]. Group 4: Future Directions - The integration of VLA and World Models is seen as a promising direction, with frameworks like IRL-VLA combining the strengths of both approaches for enhanced performance in autonomous driving [22][28]. - The article suggests that while VLA is likely to prevail in the near term, the combination of VLA with World Model enhancements could lead to superior outcomes in the long run [30].