The paper "Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities" by Xu Yan et al. explores the development of Vision Foundation Models (VFM) tailored for autonomous driving (AD). The authors highlight the challenges in AD, such as data scarcity and task heterogeneity, and discuss the potential of large foundation models, particularly those trained on extensive datasets, to address these issues. They review existing datasets and data simulation techniques, including GANs, NeRF, diffusion models, and 3D Gaussian Splatting (3DGS), to overcome data scarcity. The paper also delves into self-supervised learning techniques, categorizing them into reconstruction-based, contrastive-based, distillation-based, rendering-based, and world model-based approaches. Additionally, it examines the application of VFM from other domains to AD tasks and proposes future research directions. The authors have developed Forge-VFM4AD, an open-access repository to support researchers in this field. The paper provides a comprehensive overview of the current state and future prospects of VFM development for AD.The paper "Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities" by Xu Yan et al. explores the development of Vision Foundation Models (VFM) tailored for autonomous driving (AD). The authors highlight the challenges in AD, such as data scarcity and task heterogeneity, and discuss the potential of large foundation models, particularly those trained on extensive datasets, to address these issues. They review existing datasets and data simulation techniques, including GANs, NeRF, diffusion models, and 3D Gaussian Splatting (3DGS), to overcome data scarcity. The paper also delves into self-supervised learning techniques, categorizing them into reconstruction-based, contrastive-based, distillation-based, rendering-based, and world model-based approaches. Additionally, it examines the application of VFM from other domains to AD tasks and proposes future research directions. The authors have developed Forge-VFM4AD, an open-access repository to support researchers in this field. The paper provides a comprehensive overview of the current state and future prospects of VFM development for AD.