World models have emerged as a transformative approach in autonomous driving, enabling systems to synthesize and interpret vast sensor data, predict future scenarios, and compensate for information gaps. This paper provides an initial survey of world models in autonomous driving, covering their theoretical foundations, practical applications, and ongoing research. World models are crucial for advancing autonomous driving technologies by enabling systems to understand and predict complex environments. The paper highlights the role of world models in bridging the cognitive gap between human and machine intelligence, offering a pathway toward more sophisticated autonomous systems. World models have evolved from conceptual frameworks in control theory to their current prominence in AI research. Early efforts in control theory laid the groundwork for integrating computational models in dynamic systems. The advent of neural networks introduced a paradigm shift, allowing for the modeling of dynamic systems with unprecedented depth and complexity. Recurrent neural networks (RNNs) were particularly transformative, enabling systems to process temporal data and predict future states. In autonomous driving, world models enable data-driven intelligence by predicting and simulating future scenarios, crucial for safety and efficiency. They address data scarcity challenges, particularly in specialized tasks like BEV labeling, by generating predictive scenarios from historical data. This approach enhances training in simulated environments that mirror real-world conditions. The paper explores the architectural foundations of world models, including perception, memory, control/action, and world model modules. These components enable systems to simulate cognitive processes and decision-making akin to humans. World models utilize latent dynamical models to abstractly represent observed information, enabling compact forward predictions within a latent state space. This allows for efficient parallel predictions and better approximation of real-world complexity and uncertainty. Key world model architectures include RSSM and JEPA, which have shown promise in enhancing model performance by better approximating real-world dynamics. RSSM uses a shared GRU to compress states and actions into a deterministic encoding, while JEPA focuses on representation space rather than direct predictions, achieving efficiency and accuracy. World models have been applied across various domains, including gaming, robotics, and virtual environment generation, demonstrating their versatility and potential. In autonomous driving, they are used for scenario generation, planning, and control, enhancing the ability of autonomous systems to navigate complex environments. Models like GAIA-1 and DriveDreamer generate realistic driving scenarios, while SEM2 and Drive-WM improve planning and control strategies. Challenges remain in long-term memory integration, simulation-to-real-world generalization, and theoretical and hardware advancements. Future research aims to address these challenges through multi-pronged strategies, including enhanced memory modules, robust simulation technologies, and advancements in hardware capabilities. Ethical and safety challenges, such as decision-making accountability, also require careful consideration to ensure the responsible deployment of world models in autonomous driving.World models have emerged as a transformative approach in autonomous driving, enabling systems to synthesize and interpret vast sensor data, predict future scenarios, and compensate for information gaps. This paper provides an initial survey of world models in autonomous driving, covering their theoretical foundations, practical applications, and ongoing research. World models are crucial for advancing autonomous driving technologies by enabling systems to understand and predict complex environments. The paper highlights the role of world models in bridging the cognitive gap between human and machine intelligence, offering a pathway toward more sophisticated autonomous systems. World models have evolved from conceptual frameworks in control theory to their current prominence in AI research. Early efforts in control theory laid the groundwork for integrating computational models in dynamic systems. The advent of neural networks introduced a paradigm shift, allowing for the modeling of dynamic systems with unprecedented depth and complexity. Recurrent neural networks (RNNs) were particularly transformative, enabling systems to process temporal data and predict future states. In autonomous driving, world models enable data-driven intelligence by predicting and simulating future scenarios, crucial for safety and efficiency. They address data scarcity challenges, particularly in specialized tasks like BEV labeling, by generating predictive scenarios from historical data. This approach enhances training in simulated environments that mirror real-world conditions. The paper explores the architectural foundations of world models, including perception, memory, control/action, and world model modules. These components enable systems to simulate cognitive processes and decision-making akin to humans. World models utilize latent dynamical models to abstractly represent observed information, enabling compact forward predictions within a latent state space. This allows for efficient parallel predictions and better approximation of real-world complexity and uncertainty. Key world model architectures include RSSM and JEPA, which have shown promise in enhancing model performance by better approximating real-world dynamics. RSSM uses a shared GRU to compress states and actions into a deterministic encoding, while JEPA focuses on representation space rather than direct predictions, achieving efficiency and accuracy. World models have been applied across various domains, including gaming, robotics, and virtual environment generation, demonstrating their versatility and potential. In autonomous driving, they are used for scenario generation, planning, and control, enhancing the ability of autonomous systems to navigate complex environments. Models like GAIA-1 and DriveDreamer generate realistic driving scenarios, while SEM2 and Drive-WM improve planning and control strategies. Challenges remain in long-term memory integration, simulation-to-real-world generalization, and theoretical and hardware advancements. Future research aims to address these challenges through multi-pronged strategies, including enhanced memory modules, robust simulation technologies, and advancements in hardware capabilities. Ethical and safety challenges, such as decision-making accountability, also require careful consideration to ensure the responsible deployment of world models in autonomous driving.