Recent breakthroughs in autonomous driving have been propelled by advances in robust world modeling, fundamentally transforming how vehicles interpret dynamic scenes and execute safe decision-making. In particular, world models have emerged as a linchpin technology, offering high-fidelity representations of the driving environment that integrate multi-sensor data, semantic cues, and temporal dynamics. This paper systematically reviews recent advances in world models for autonomous driving, proposing a three-tiered taxonomy: 1) Generation of Future Physical World, covering image-, BEV-, OG-, and PC-based generation methods that enhance scene evolution modeling through diffusion models and 4D occupancy forecasting; 2) Behavior Planning for Intelligent Agents, combining rule-driven and learning-based paradigms with cost map optimization and reinforcement learning for trajectory generation in complex traffic conditions; 3) Interaction Between Prediction and Planning, achieving multi-agent collaborative decision-making through latent space diffusion and memory-augmented architectures. The study further analyzes training paradigms including self-supervised learning, multimodal pretraining, and generative data augmentation, while evaluating world models' performance in scene understanding and motion prediction tasks. Future research must address key challenges in self-supervised representation learning, long-tail scenario generation, and multimodal fusion to advance the practical deployment of world models in complex urban environments. Overall, our comprehensive analysis provides a theoretical framework and technical roadmap for harnessing the transformative potential of world models in advancing safe and reliable autonomous driving solutions.
View on arXiv@article{feng2025_2501.11260,
title={ A Survey of World Models for Autonomous Driving },
author={ Tuo Feng and Wenguan Wang and Yi Yang },
journal={arXiv preprint arXiv:2501.11260},
year={ 2025 }
}