The world model has found extensive application in autonomous driving technology. Nevertheless, when delving into its role within autonomous driving, differing viewpoints inevitably emerge. Does it endow autonomous vehicles with the ability to comprehend the world, or does it offer them a vantage point to forecast the future?

What precisely is the function of the world model?

Fundamentally, the world model is a fusion of 'internal representation + dynamic prediction'. In simpler terms, it condenses perceived information (such as images, point clouds, radar data, text, and action history) into a set of internal states. Subsequently, it utilizes these states to generate and predict potential future scenarios or observations.

To achieve 'internal representation + dynamic prediction', two pivotal technologies must be considered. The first is 'representation', which entails refining intricate external information into a structure that is beneficial for subsequent inference and decision - making. The second is 'generative/predictive', which involves reasoning, sampling, and evaluating possible future sequences based on the representation.

Early research revealed that if an agent merely reacts based on the current observation at each step, it is akin to a reflex action, lacking the ability to look ahead. Once the scenario becomes slightly more complex or requires weighing outcomes several steps in advance, this approach is prone to errors. Consequently, the concept of the 'world model' was introduced into reinforcement learning. The underlying idea is to first enable the system to learn a simplified yet credible representation of how the world functions, clarifying the likely direction in which the environment will evolve when a specific action is taken in a given state. Once this model is acquired, the policy no longer focuses solely on the present moment but can pre - test steps within this 'internal world' to assess the probable consequences of different choices before making real - world decisions.

The key transformation in this approach is that the system no longer 'reacts immediately to what it perceives' but first conducts a round of internal simulation and reasoning prior to outputting actions. Due to this additional step of 'thinking ahead', the agent's behavior leans more towards planning rather than reflexive responses.

This also provides an answer to why some claim that the world model pertains to 'understanding the world'. If 'understanding' is defined as the capacity to internally construct a representation that elucidates causality, predicts consequences, and makes reasonable choices accordingly, then the world model can indeed be considered a form of machine understanding. Conversely, if 'understanding' is defined as possessing subjective experiences, common - sense reasoning, and high - level abstract concepts similar to those of humans, then the world model is far from reaching that stage.

In reality, a more accurate description of the world model is as a machine representation and prediction mechanism that can substitute for some understanding functions. It offers useful understanding rather than a comprehensive, human - like subjective understanding. The world model is more akin to enabling a large model to internally simulate the future and then using the simulated consequences to guide real - world actions.

Three Key Elements of the World Model

When dissected, the world model can be divided into three components: the first is representation, the second is the dynamics/generative model, and the third is utilizing these capabilities to support decision - making (planning/control).

These three elements are not simply independent modules assembled together but are mutually supportive. A good representation can enhance the robustness and reliability of predictions. Reliable predictions, in turn, can make planning safer. Moreover, the planning process can drive improvements in representation and prediction (for example, through closed - loop data collection). This closed loop is regarded as the core of the world model paradigm, where a large model learns an internal world that can be used to imagine the future and then trains and evaluates actions within that imagination.

Representation typically maps high - dimensional observations to a low - dimensional or discrete latent space. This latent space must compress information while retaining structures that are crucial for future prediction and decision - making (such as object speed, relative position, collision potential, and road surface properties).

The generative/dynamics module learns the rules of temporal evolution in this latent space. Given the current latent state and action, it predicts the next latent state or directly generates the next observation frame. Once this mechanism is in place, trajectory sampling can be performed internally to compare the consequences of different action sequences and select an action that appears safer and more beneficial. This 'think - before - you - act' mode is precisely why the world model is highly regarded in robotics and automatic control.

Of course, the world model does not aim to generate pixel - level realistic images but to retain causal and actionable information at an abstract level. In other words, for the world model, the ability to predict future high - level structures (such as which object will collide with which, how speeds will change, and whether a pedestrian intends to cross the road) is more important than simply rendering visually appealing images. This is why some research does not involve frame - by - frame generation of raw pixels but instead predicts 4D occupancy, geometric representations, BEV (bird's - eye view) trajectories, or more compact behavioral intentions.

Is the World Model About 'Understanding' or 'Predicting'?

If one is compelled to choose between 'understanding the world' and 'predicting the future' as the essence of the world model, Zhizhia's cutting - edge perspective is that the world model is essentially a representation system constructed for prediction, but this prediction serves decision - making. Thus, it can be equated to a form of actionable understanding. In other words, the world model demonstrates its understanding of the world through its ability to predict the future (short - term or mid - term), but this understanding is functional and behavior - oriented rather than philosophical, in the sense of comprehending why the world exists.

In fact, for the world model, prediction is a means, not an end. The ultimate goal of the world model is to enhance decision - making effectiveness, and predicting the future is merely a way to achieve this goal. 'Understanding' for the world model is also just an actionable form; it is not a dictionary - style definition or a humanistic comprehension but rather encoding useful causality, dynamics, and constraints into the model so that it can infer consequences and choose better actions when encountering new situations. Furthermore, the understanding provided by the world model is merely an engineering goal. Whether it can transform predictions into safe and robust decision - making bases is more critical and practical than whether it can achieve human - like understanding.

Impact on Autonomous Driving

In traditional autonomous driving systems, perception is responsible for identification and localization, prediction provides trajectory or intention distributions, and decision - making/planning selects paths based on these inputs. After the introduction of the world model, the system can internally simulate various action sequences and external responses, evaluate the long - term effects of different strategies in simulated futures, and enable the autonomous driving system to no longer rely solely on short - term trajectory predictions. This means the system can weigh risks and benefits over a longer time horizon rather than making short - term judgments based on each frame of data.

The world model also offers a crucial technical solution for autonomous driving. In simulated environments, the world model can batch - generate extreme scenarios for training and validation, reducing the time - consuming, laborious, and dangerous issues associated with real - world road testing. For example, the autonomous driving large model GAIA - 1 utilizes joint modeling of video, text, and actions to synthesize diverse driving scenarios for training more robust strategies. Of course, this approach requires that the synthesized scenarios be of high quality and cover key weaknesses in the real distribution; otherwise, the trained strategies will not be applicable in the real world. Therefore, for autonomous driving, the world model is a strong complementary tool rather than a complete replacement for real - world road testing.

The world model can provide forward - looking predictions for autonomous driving, but this forward - looking capability is not infallible. When relying on the world model for decision - making, the autonomous driving system must have clear uncertainty metrics and fallback strategies. When the model's confidence is low or the predicted distribution is overly dispersed, the system should revert to more conservative control strategies or request human intervention.

How Does the World Model Handle Long - Tail Problems?

For autonomous driving, the real traffic environment is highly complex. It is impossible for any model to collect all the necessary data during the learning phase. So, how does the world model address this issue?

The world model first uses real data to learn representation and basic dynamics and then employs generative or simulation methods to extend to rare scenarios. In recent years, some generative world models (such as those that jointly model video, actions, and text) have used unsupervised or self - supervised methods to learn high - level structures and then synthesized data with these models to train strategies or conduct safety testing.

The advantage of this approach is that it can 'compress' the occurrence frequency of long - tail risks in simulations and accelerate the improvement of strategy robustness in extreme situations. The downside is that the differences between the synthesized and real distributions may introduce biases or illusions, leading to discrepancies between training results and reality.

Many technical solutions integrate different modalities (vision, radar, lidar, maps) into the representation, using latent variable - based generative models or JEPA - based predictive architectures to learn temporally consistent representations. Then, planners or reinforcement learning algorithms perform closed - loop training in the latent space.

The goal is to reduce the impact of noise in the original observation dimensions and place the decision - making problem on a more stable abstract layer. Some of the latest techniques even represent the world model as a sequence of discrete tokens, transforming the prediction problem into a sequence generation problem and leveraging the power of large - scale sequence models to enhance long - term temporal stability.

Regardless of the technical route, the core is to use internal models to replace some real interactions, saving costs and improving safety.

Final Thoughts

Returning to the original question: Is the world model about understanding the world or predicting the future? The answer is both. The world model enhances future prediction capabilities by learning internal representations, and these predictions primarily serve decision - making and action.

By providing an understandable and reasoning - enabled representation of the world, the world model empowers autonomous driving systems with the ability to predict the future. Understanding is the foundation of prediction, and prediction is the extension and application of understanding. These two are tightly coupled, enabling autonomous driving to evolve from a 'perception - reaction' mode to a higher level of 'understanding - reasoning - decision - making'. This is precisely the key to its technological transformative potential.

-- END --