At present, in the autonomous driving sector, automotive firms typically opt for the single-vehicle intelligence approach as their technological route. During the actual implementation, different companies have adopted distinct technical methods. Some automotive companies concentrate on the Vision-Language-Action model (VLA), while others are committed to developing and utilizing world models. What are the key differences between these two approaches?

What exactly is VLA, and what constitutes a world model?

Let's begin with VLA. VLA, which stands for Vision-Language-Action, amalgamates visual perception, language/semantic comprehension/reasoning, and action/control output into a single, end-to-end system.

VLA initially gathers environmental data through cameras (or other sensors), transforms it into feature vectors using a visual encoder, and then 'translates' these visual features into a semantic space that can be comprehended by language models (LLMs, large language models). The language model then conducts high-level reasoning and judgment (such as identifying lane lines, pedestrians, traffic signs, and even assessing pedestrian intentions, traffic rule priorities, and strategies for the current situation). The 'conclusions' drawn by the language model are forwarded to the action generation module, which directly outputs control commands (e.g., steering, acceleration/deceleration, trajectory planning).

The primary function of VLA is to equip autonomous vehicles with the capabilities of 'seeing, thinking, and doing.' It encompasses thinking, reasoning, and semantic understanding between visual information and action output, rather than relying on a simple, modular, rule-based approach of perception → planning → control.

Now, let's delve into world models. The essence of a world model is to construct a virtual, internal representation of the external world within the model's 'brain.' This means it not only perceives the current road conditions but also endeavors to comprehend the physical laws, traffic rules, and various dynamic changes in the world. It then simulates, infers, and predicts potential future scenarios within this internal model. For instance, it can forecast whether the car ahead will suddenly turn, whether pedestrians will dart out, or how weather or light changes will impact the situation. By predicting the traffic environment, it can aid in decision-making, planning, and even strategy verification.

World models are frequently employed for simulation and modeling, enabling large-scale simulations of extreme, rare, and long-tail scenarios to train, validate, and generate data for autonomous driving systems. They also empower the system to internally preview and assess risks, rather than solely depending on the currently visible scene.

In brief:

VLA = Vision + Language (Semantics) + Action, linking 'seeing, understanding, and doing' through an end-to-end system.

World Model = Constructing a model and simulation of the world in the 'brain,' enabling the system to envision the future, make predictions/reasoning, and assess risks.

Why do automotive companies pursue these two directions?

At this juncture, numerous automotive companies are investing in both directions, hoping that these two technologies will unlock more possibilities for the realization of autonomous driving. This is because autonomous driving imposes stringent requirements on complexity, uncertainty, safety, and long-tail scenarios. Traditional modular + rule/planning + static prediction models are inadequate to fully address real traffic scenarios.

Traditional autonomous driving systems generally adopt a modular design of 'perception → planning → control.' They gather environmental data through sensors such as cameras, millimeter-wave radars, and LiDAR, which is then processed by the perception module for target detection, classification, and tracking to identify crucial information such as pedestrians, vehicles, and lane lines. The planning module generates decisions on trajectory, speed, and acceleration/deceleration based on the perception results, combined with preset rules and prediction models. The control module executes specific steering, throttle, and braking commands based on these decisions.

However, as autonomous vehicles are increasingly deployed on roads, complex road conditions, dynamic scene changes, and the continuous emergence of edge cases have underscored the limitations of a series architecture based on fixed rules and static predictions. It is challenging to cover all potential scenarios, especially in long-tail and extreme situations, where the system's adaptability and robustness face significant hurdles.

Consequently, there is a desire for autonomous driving systems to, akin to experienced drivers, not only 'see' the world but also 'understand,' 'reason,' 'predict the future,' and 'flexibly respond to changes.' VLA and world models have emerged in response to these needs.

Advantages and Limitations

1) Advantages of VLA

Semantic Understanding + Interpretability

Since VLA 'translates' visual information into semantics (akin to language descriptions), it more closely mirrors how humans perceive the world. For complex traffic scenarios involving pedestrians, cyclists, traffic signs, and interaction intentions, VLA's language reasoning capabilities showcase their strengths.

End-to-End + Holistic Optimization

In an end-to-end model, the process from perception to action is integrated within a single model, with minimal manually set rules and module boundaries. This theoretically enables it to learn through extensive data training and develop driving reactions from experience, demonstrating robust generalization capabilities.

Suitable for Complex Semantic Scenarios + Human-Machine Interaction

Autonomous driving systems must achieve efficient collaboration with humans, such as accurately comprehending natural language instructions like 'please make a temporary stop at the convenience store ahead' or explaining decision reasons to users when necessary, such as braking due to a pedestrian suddenly approaching from the left. VLA technology's multimodal semantic alignment and natural language processing capabilities exhibit unique value. Its architecture naturally supports the parsing, reasoning, and generation of complex semantics, providing an intuitive and interpretable communication interface for human-machine interaction, thereby enhancing system interpretability and user experience.

2) Limitations of VLA

Weak Predictive Ability for Environmental Physical Dynamics + Long-Tail, Rare Scenarios

VLA fundamentally involves 'seeing + reasoning + outputting.' If it solely makes judgments based on the current scene without sufficient simulation and prediction of potential future changes (such as sudden emergency braking by the vehicle ahead, pedestrians darting out, rain, snow, or light changes), it may not react promptly or safely enough.

Sparse Supervisory Signals / Insufficient Learning

Some recent studies have highlighted that relying solely on action outputs (steering angle/acceleration/braking) as supervision may be insufficient for a large-capacity VLA model, potentially leaving much of the model's potential untapped. Recent research has proposed incorporating world modeling (predicting future scenes) into VLA training to obtain richer and denser supervisory signals.

Real-Time Performance, Computational Resource Consumption

End-to-end large models integrate multimodal perception and direct action generation. If additional requirements for short- and long-term prediction and complex scene reasoning capabilities are imposed, they will encounter challenges in computational power demand, real-time latency, and energy efficiency. This is particularly pronounced on vehicle-embedded platforms, posing a significant hurdle that must be overcome for practical implementation.

3) Advantages of World Models

Strong 'Prediction + Simulation + Planning' Capabilities for Future, Dynamic, and Complex Scenarios

By establishing an internal model of the world, the system can not only perceive the present but also infer the future, enabling predictions such as simulating potential braking by the vehicle ahead, pedestrians crossing, changes in light/weather, and vehicle lane changes. It can then proactively plan the safest/most prudent actions. This is particularly crucial for autonomous driving, as real road environments are replete with changes, uncertainties, and sudden events.

Suitable for Large-Scale Training / Long-Tail / Extreme Scenario Generation

In real traffic environments, certain dangerous or extreme situations are challenging to collect in large quantities (such as nighttime rain, snow, fog, extreme pedestrian behaviors, sudden obstacles). However, world models can 'simulate' these situations for training, validation, and testing of autonomous driving systems, enhancing their robustness and safety.

Provides Redundancy and Safety Verification Mechanisms

Even if the primary system (decision/action module) malfunctions, the world model can serve as a 'virtual brain' for redundant judgment, risk analysis, and simulation verification. Some designs also incorporate lightweight world models into the vehicle for verification and as a safety net.

4) Limitations of World Models

Complex Construction and Training

To accurately reflect real traffic environments, world models must perform high-fidelity modeling of multidimensional elements such as vehicle dynamics, traffic rules, uncertainty factors, and pedestrian behaviors. This high-precision simulation of physical, social, and dynamic rules imposes extremely stringent requirements on data quality, computational scale, and system design. As a result, early world models encountered numerous issues in achieving real-time reasoning and efficient deployment, especially under GPU computational acceleration and vehicle-grade latency constraints, significantly limiting their engineering applications.

Weak Integration with Semantic Understanding / Rules / Common Sense

Pure world models concentrate on physical + dynamic + prediction/simulation/planning but may not perform well in complex semantics, traffic rules, pedestrian intentions, and social interaction rules, which fall under the categories of semantics + common sense + rules + language. In scenarios requiring semantic understanding, rule judgment, explanation, and interaction, their performance may lack flexibility.

Poor Interpretability / Transparency

The core mechanism of world models lies in internal simulation and numerical probability deduction of physical laws and dynamic scenarios, with decision-making relying on high-dimensional implicit state space modeling and computation. However, this numerical simulation-based reasoning method is challenging to convert into semantically intuitive explanations for external output. In practical implementation requirements such as safety verification, regulatory compliance, liability determination, and system auditability for autonomous driving, this 'black box' characteristic poses a challenge that must be addressed.

Final Thoughts

VLA and world models represent two distinct 'brain design approaches' in the field of autonomous driving. VLA empowers vehicles with the capabilities of 'seeing + understanding + judging + acting,' while world models provide vehicles with the ability to create an 'internal virtual world + predict/simulate/deduce the future.' In terms of direction selection, Zhijia Zuiqianyan believes that integrating and complementing these two paths could potentially enable autonomous driving to be implemented safely, intelligently, and stably.

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