VLM, or Visual Language Model, can be simply understood as a model that processes both 'what is seen' and 'what is spoken' within the same framework. Typically, visual models handle tasks like detection, segmentation, and depth estimation from camera-captured images, while language models process speech or text.

VLM, however, trains visual and language signals together, enabling it to describe scenes in language and convert sentences into visual focus points and reasoning. For autonomous driving, this capability goes beyond merely adding a 'talking' model; it elevates simple pixel recognition to semantic understanding in complex scenarios. VLM informs the vehicle not just that 'there is an object ahead,' but also 'what the object's behavior and context imply, and whether it poses a danger.' Such semantic understanding is crucial for decision-making robustness and interpretability.

Several types of problems VLM can truly solve in autonomous driving

Integrating VLM into vehicles can directly enhance the ability to recognize and interpret 'unconventional, temporary, or non-standard information.' While common road signs and signals are familiar, autonomous driving faces challenges with unexpected elements like temporary construction, non-standard road signs, traffic police directions, temporary ground markings, and randomly placed obstacles.

Traditional object detection networks can identify these as 'objects' or 'unclassifiable anomalies,' but fail to conclude that they indicate a construction zone requiring deceleration and lane change. VLM combines visual evidence with linguistic priors (e.g., traffic rules, common construction signs, gesture meanings) for reasoning, enabling it to make reasonable semantic judgments in such long-tail scenarios, guiding subsequent decisions.

The second issue VLM improves is human-machine interaction and natural language navigation. Current in-vehicle voice systems mostly handle command-style requests like 'navigate to point A' or 'turn right at the next exit.' When users employ more colloquial or complex descriptions, traditional systems cannot link language with real-time visual context.

VLM aligns natural language instructions from drivers or passengers with scenes viewed by onboard cameras, understanding the meaning of such instructions under current road conditions. For example, it can translate vague expressions like 'this road often gets congested ahead; can we take the right exit and then make a U-turn?' into specific executable strategies. This makes communication between users and the autonomous driving system more natural and enhances the driving experience.

VLM also enhances the recognition of small objects and potential hazards. Many risk sources in traffic environments are not clear, large objects but small, inconspicuous obstacles, cyclists suddenly approaching the lane from the roadside, or distant moving objects.

VLM's advantage lies in its ability to infer not just whether an object is seen but also to combine inconspicuous visual cues with linguistic scene experience and context. For instance, when scattered debris is detected on the road, object detection confidence might suggest these are small, irregularly shaped objects posing low risk.

However, VLM can introduce semantic judgment, linking 'debris on the road' with the potential for these objects to be kicked up by preceding vehicles, posing secondary risks to following vehicles, thus interpreting the scene as a potentially dangerous state. Consequently, the autonomous driving system generates more cautious strategies rather than deciding solely based on detection scores for deceleration or avoidance.

VLM also provides autonomous driving systems with interpretable 'speaking ability.' During accident review, decision auditing, or explaining actions to passengers, VLM can output its perception and reasoning in natural language, explaining 'why I braked here' or 'why I did not change lanes.' This explanatory capability is highly beneficial for safety supervision and user trust. Compared to black-box deep models, systems capable of semantic explanation are more readily accepted.

What issues need to be addressed when integrating VLM into vehicles?

Many current VLM models have numerous parameters and high computational demands, making their reasoning unsuitable for millisecond-response vehicle control loops. To address this, VLM should not be directly placed in closed-loop control but used as 'slow logic' or an 'auxiliary cognitive module.' For instance, lightweight visual models and rules can handle routine, high-frequency perception-control loops, while VLM participates in decision-making, providing explanations and suggestions when encountering ambiguous scenes, anomalies, or requiring semantic reasoning. This balances real-time performance with deep understanding but also requires addressing information synchronization between the two systems, confidence fusion from different modules, and avoiding conflicting instructions.

During training, VLM learns extensive visual-language statistical patterns, but traffic scenes and rules vary regionally and culturally. The same gesture may have different meanings in different countries, and temporary road sign styles and semantics can change. Without targeted localization training or rule calibration, VLM may exhibit understanding biases in certain regions. Thus, VLM outputs must be coupled with explicit regulatory databases, map semantics, and localization rules to form a controllable semantic layer.

Although VLM can provide explanations, its internal reasoning still has black-box elements, especially in multimodal interactive reasoning, where models may draw conclusions based on complex feature combinations. For high-safety scenarios like autonomous driving, relying solely on implicit model explanations is insufficient. Verifiable redundancy mechanisms and formalized safety checks must be designed to ensure model outputs do not mislead controllers at critical moments.

Training powerful VLMs requires vast amounts of annotated or weakly supervised cross-modal data, such as in-vehicle videos, image annotations, speech, and text. Collecting, annotating, and using this data involves privacy, compliance, and annotation cost issues. Strict data governance strategies must be established, and data-efficient training methods like few-shot learning, transfer learning, or knowledge distillation should be employed to reduce reliance on large-scale annotated data.

How to integrate VLM with existing autonomous driving systems

To enable VLM to truly function in autonomous driving systems without introducing uncontrollable risks, a practical approach is not to let it directly take over control but to assign it an appropriate position in the system architecture.

A common approach is adopting a hierarchical collaboration method, maintaining the core vehicle perception and control loop as a high-frequency, low-latency system to handle most deterministic scenarios. VLM can be placed in a mid-to-low-frequency layer as a situational understanding and semantic reasoning module. When the system encounters complex or ambiguous scenes where rules are difficult to apply or perception results are ambiguous, VLM provides higher-level semantic judgments and risk warnings, which are then passed to the decision-making layer for reference. This ensures that real-time performance and safety baselines are still guaranteed by mature and reliable modules, with VLM's semantic capabilities intervening only when 'thinking' is required, without slowing overall response.

Building on this, VLM outputs must also be constrained. VLM results should be treated as reference opinions rather than final instructions. In other words, VLM can tell the system 'what I think this scene might mean,' but cannot directly decide how the vehicle should operate. Its judgments must be comprehensively evaluated alongside existing information in high-definition maps, explicitly stated traffic regulations, the vehicle's physical limitations, and more stable sensor data from radars and lidars. The autonomous driving system applies a clear, verifiable logic to compare this information, checking for consistency and obvious conflicts.

This approach ensures that if VLM makes inaccurate judgments in unfamiliar regions or rare scenarios, the entire system will not be misled. Once other sensors or rules provide clearer, more reliable signals, the system can reject risky operations and choose more conservative, safer behaviors.

To enable VLM to truly operate in vehicles, the model must undergo targeted compression and optimization, transforming its research-grade capabilities into versions suitable for in-vehicle deployment. Common methods include transferring semantic understanding capabilities to smaller models through knowledge distillation, combining pruning and quantization to reduce computational and storage requirements, retaining only the most valuable parts for driving decisions. If computational resources allow, an edge-cloud collaboration approach can be adopted, where complex, time-consuming reasoning is completed on edge computing resources outside the vehicle, with the vehicle responsible for invoking results, performing consistency checks, and short-term caching, achieving a balance between performance and real-time performance.

For autonomous driving systems, VLM's interpretability should be designed as a system-level capability rather than an additional model feature. Instead of merely outputting a conclusion, the model should provide semantic explanations for 'why it made this judgment,' recording these explanations along with corresponding visual evidence and timestamps to directly support accident analysis, system debugging, and regulatory compliance. Such design not only helps engineering teams understand and improve system behavior but also enhances user and regulatory trust in autonomous driving systems to a certain extent.

In this way, VLM is no longer an isolated large model but can be embedded into a bounded, constrained, and auditable autonomous driving architecture, leveraging its semantic understanding advantages while controlling risks within engineering-acceptable limits.

Final Remarks

The true value of VLM lies not in its 'greater knowledge' but in its ability to supplement autonomous driving with a layer of semantic understanding that was previously lacking. It enables the system to move beyond reacting solely to detection scores and rule triggers, attempting to answer 'what this scene means and what might happen next.' Incorporating VLM into autonomous driving allows the system to exhibit greater 'discernment' in the face of uncertainty, not only understanding scenes better but also knowing where to act cautiously.

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