The world model has gone through stages of system dynamics (1960s-2000), cognitive science (2001-2017), and deep learning (2018-present). However, its application to autonomous vehicles has only been proposed in recent years. Is the world model the right solution for the deployment of autonomous driving?

What is a World Model?

For autonomous vehicles, the world model is akin to drawing a map in the car's 'brain.' It represents both the current state and possible future evolutions of the environment, enabling the autonomous system not just to 'see the present' but also to 'think about what might happen next.'

In simple terms, the world model not only allows autonomous vehicles to know where lanes, traffic lights, and obstacles are but also to predict future changes in these obstacles, which is crucial for ensuring the safety of the autonomous system.

The world model can transform raw observational data collected by sensors (such as cameras, radar, LiDAR, and in-vehicle positioning systems) into a low-dimensional, abstract 'latent state' as an internal representation.

The model learns the dynamics of how this latent state evolves over time and uses this for prediction or planning. The world model can be an explicit physical or probabilistic model or a neural network model based on learning. It can be used for direct replay of future scenarios (simulation) or to generate probability distributions for the next moment to assist decision-making.

Core Functions of the World Model in Autonomous Driving

For autonomous driving systems, the world model can be applied in three main areas: prediction, planning, and verification. Prediction is the most intuitive use of the world model. Traditional perception can identify and locate surrounding objects, but this is only 'static' information.

The world model, by learning the behavioral patterns of traffic participants and the dynamics of the scene, can provide longer-term, multi-step predictions. For example, it can determine whether a cyclist will approach an intersection or whether a merging vehicle will cross paths with the ego vehicle over a time scale of tens of seconds.

Such predictions are not simple constant-velocity extrapolations but incorporate an understanding of intentions, interactions, and environmental constraints.

Planning involves evaluating the consequences of different actions and selecting a trajectory that is both safe and comfortable. The world model can rely on its built-in 'simulated environment' to 'rehearse' candidate trajectories several times, comparing their risks and benefits over the next few seconds.

Compared to relying solely on rules or short-term predictions, this world model-based planning is better at handling complex multi-agent interaction scenarios such as narrow road meetings, dense lane changes, or non-compliant traffic participants. It can also help the vehicle make more conservative or aggressive strategic choices and incorporate uncertainty into decision-making.

Training and verifying autonomous driving systems require a large number of scenarios, especially rare or dangerous ones. Collecting these scenarios in the real world is not only expensive but also dangerous.

The world model can generate high-quality synthetic scenarios or serve as part of a digital twin for large-scale virtual testing. By repeatedly simulating in the model, weaknesses in the autonomous driving system under long-tail scenarios can be identified, avoiding the deployment of dangerous behaviors in real vehicles.

How is the World Model Constructed?

To enable the model to 'imagine the future,' it must be fed a large amount of appropriate data. The world model for autonomous driving can learn from data provided by cameras (rich visual details), LiDAR (precise 3D geometric information), millimeter-wave radar (robust in adverse weather), and in-vehicle positioning and CAN bus (vehicle's own state).

After fusing these data, the model must learn to extract useful representations, a process known as representation learning. A good representation not only retains details important for decision-making (e.g., relative speed, passable space) but also compresses redundant information for easier subsequent prediction and planning.

After representation learning, dynamics modeling is required, which involves learning how the latent state changes over time. There are two mainstream approaches here.

One is an explicit method based on physical or graphical models, where rules or physical equations are written to describe the motion of vehicles and pedestrians, and observations are combined with these models through filters or Bayesian inference. The advantage of explicit methods is interpretability and ease of verification, but they often struggle with complex human behaviors.

The other is an end-to-end learning approach, using recurrent neural networks, variational autoencoders, or the recently popular temporal Transformers to directly learn the mapping from past observations to future latent states. Learning-based methods are more expressive in complex interactions but require large amounts of training data and attention to uncertainty representation.

Regardless of the architecture used, uncertainty modeling is crucial.

The world is not deterministic; pedestrians may hesitate, and drivers may suddenly change lanes. Representing predictions in a probabilistic form (e.g., representing future positions with probability distributions or generating multiple possible future trajectories with confidence levels) can make the decision-maker more robust. Incorporating causal reasoning or intention inference into the world model allows it to predict not just positions but also 'why things are the way they are,' which is crucial for handling unprecedented situations.

Several Typical Application Scenarios in Practice

We've been talking about concepts, so what are the specific application scenarios of the world model for autonomous driving? Imagine a scenario where there is a parked truck on the vehicle's right side, and there may be pedestrians preparing to cross behind it.

Relying solely on perception might not detect the pedestrian, but the world model can combine the road environment, past patterns of pedestrian appearances, and the purpose of parked vehicles to predict that 'someone might come out from behind,' prompting the decision-maker to slow down and reserve space.

During highway lane merging, the behavior of two vehicles signaling to merge is full of strategic interactions. The world model can observe changes in speed, acceleration, and steering amplitude of both parties, estimate their intentions, and predict multiple possible merging outcomes, thereby selecting a merging strategy that is safer in terms of time and space or choosing to slow down before merging.

In scenarios involving construction, temporary traffic guidance, or other abnormal signs, rule-driven systems are prone to errors. The world model can link temporary traffic cones, construction vehicles, and the behavioral patterns of traffic participants to determine that this is a temporarily diverted road and learn new feasible strategies in the short term rather than blindly following past rules.

Final Thoughts

Understanding the world model in the context of autonomous driving as a whole, its core value lies in connecting current perception with future decision-making. It does not simply treat perception results as facts but constructs a short-term, operable 'virtual world' in its 'mind' (model), where it repeatedly tries out actions, assesses risks, and selects actions. This approach significantly enhances the system's ability to handle complex interaction scenarios, occlusions, and long-tail events, as well as providing an important tool for offline large-scale verification.

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