Foundation CAN LM: A Pretrained Language Model For Automotive CAN Data

arXiv:2602.00866 · cs.AI, cs.CL · Submitted 2026-01-31 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: I'm Tom, and with me are Jane, Lu, senior AI researcher at Tsinghua, Meng, lead engineer at a mysterious AI startup and Lalam, the in-house Large Language Model.

Jane: Today's paper: "Foundation CAN LM".

Tom: The gist The foundation CAN model demonstrates multi-objective downstream generalization using a single pretrained backbone by treating CAN data as a language and adapting it to various automotive tasks.;

Jane: First, who's behind it and why it matters.

Title and authors: Tom: We’ve seen how the Foundation CAN LM works on a technical level, focusing on the unified tokenization and the MLM objective they use during pretraining. Now let’s talk about what this whole paper is actually saying in plain language regarding its title and authors.

Jane: The title itself tells us that they are aiming to build a language model specifically for automotive CAN data >

Lu: What this means, fundamentally, is that they are moving away from training separate models for every single car task and instead want one large model that learns the underlying structure of all CAN messages >

Meng: So if I’m thinking about practical application, does this mean a developer can use one core model and then just tweak it for collision detection or something else?

Tom: That’s exactly the point. They are demonstrating multi-objective downstream generalization using this single pretrained backbone >

Jane: It shows that the foundation modeling paradigm from NLP and computer vision isn't just theoretical; it can actually transfer meaningfully across different automotive applications >

Lu: The authors are showing a systematic study comparing this foundation model against established baselines like Generalized Linear Models and 1D Convolutional Neural Networks to prove its worth > <ref:2602.00866#pg1>

Meng: So, the authors are basically saying they built a single tool that you can repurpose for several different problems, which sounds efficient in terms of development time.

Tom: It’s about establishing a scalable, unified framework that bridges task-specific CAN modeling and foundation-style learning >

The paper's summary: Jane: Now let’s look at the actual summary of the paper to understand the core contribution they are making beyond just using an LLM analogy.

Tom: They summarize by focusing on how they handle the non-trivial nature of mixed discrete and continuous CAN signals through their unified tokenization >

Lu: The main takeaway is that this tokenization scheme provides interpretable mappings between feature values and token IDs, which emphasizes reproducibility and consistency across different datasets >

Meng: So, the complexity of mixing those signal types is being tackled by making a fixed schema for how everything gets converted into tokens >

Tom: They detail how continuous signals get empirical calibration before discretization into uniform bins based on temporal variation, while discrete variables are handled via one-to-one mapping or symbolic identifiers >

Jane: This means the model learns representations where different signal types have a consistent way of being represented in its internal structure, which is crucial for generalization >

Lu: The pretraining validation showed that tokens corresponding to semantically related signals actually form coherent clusters and also show moderate intra-clusters in the learned embeddings >

Tom: This suggests that the representation isn't just random noise; it captures meaningful relationships within the CAN data structure itself >

Jane: It’s a nice step because it validates that this structured way of tokenizing mixed signals actually works for learning general patterns in automotive data >

The paper's improvements: Tom: So, what are the specific improvements the authors propose to make this model even better, moving beyond just the initial setup?

Meng: I’m looking for something that addresses some of those initial weaknesses we saw in their evaluation, especially when things get really difficult.

Jane: They point out that their pretraining corpus is currently limited to just nine days of driving data, which doesn't capture longer-term environmental changes like snow or rain >

Lu: And they also flag a major weakness when they tested it on a one hundred to one collision detection stress test, because the F1 score dropped significantly to fifty-four point two percent compared to the baseline of sixty-five point three percent >

Tom: That imbalance really shows that while the encoder captures coarse-grained signal structure, it doesn't yet model those subtle temporal cues needed for detecting ultra-rare events under standard fine-tuning >

Jane: So, the suggested improvements focus on fixing those specific issues by extending the data or making the representation finer >

Lu: The authors suggest extending the temporal window to capture longer-range dependencies and adopting finer-grained bin quantization to encode subtle signal variations using a larger token inventory >

Meng: And they also suggest increasing the sampling rate from one Hertz up to five Hertz, which should give the model more detail to work with >

Tom: So they are proposing ways to refine the data input and the model structure itself, rather than just stopping at a good baseline performance on today's small dataset >

Conclusion: Jane: We’ve covered how this Foundation CAN LM uses unified tokenization to create a single backbone capable of adapting across tasks, and also what limitations they identified regarding data scope and extreme imbalance.

Tom: The big picture here is that this work establishes the viability of using properly decoded CAN data as a structured language for multi-objective generalization in automotive applications >

Lu: It shows that the foundation modeling paradigm works for CAN data just like it does for natural language because the structure of car data lends itself well to this approach >

Meng: From an engineering standpoint, it sets a clear path: if you want to build future systems, start thinking about shared representation learning across multiple automotive apps instead of isolated ones >

Lalam: I see this as a huge cultural shift for how we approach automotive AI development; it moves the focus toward building robust, general-purpose models rather than chasing task-specific solutions >

Tom: It’s a really interesting direction. We’re looking forward to seeing how these proposed improvements—like increasing the sampling rate—actually translate into better real-world performance >

HPCC Lab, University of North Texas, Denton, Texas, USA · Connected Analytic Services, Plano, Texas, USA · Toyota Insurance Management Solutions

cs.AI, cs.CL

Submitted: 2026-01-31

Updated: 2026-01-31

Importance score: 75/100

The gist: The gist The foundation CAN model demonstrates multi-objective downstream generalization using a single pretrained backbone by treating CAN data as a language and adapting it to various automotive

Key concepts

Unified Tokenization
This framework merges various CAN signal types—continuous and discrete—into a single vocabulary of about 1,420 tokens. It uses fixed binning for continuous signals and maps enumerated states to categorical tokens, ensuring consistent representation across different data streams.
Foundation Model Pretraining
A large Transformer encoder (BERT-style) is trained on massive amounts of unlabeled CAN data using a Masked Language Modeling (MLM) objective. The model learns the structure of CAN signals by predicting masked tokens, establishing a robust representation analogous to natural language.
Multi-objective Generalization
The core idea is that one large foundation model, trained broadly on CAN data, can be adapted via fine-tuning for many different specific tasks. This demonstrates its ability to transfer knowledge effectively to various downstream objectives in automotive applications.

Terminology

Summary

The gist The foundation CAN model demonstrates multi-objective downstream generalization using a single pretrained backbone by treating CAN data as a language and adapting it to various automotive tasks.; <ref:2602.00866#pg2>

How it works

The core of the approach involves treating CAN data as a language and applying the Large Language Model (LLM) paradigm, where a single foundation model is pretrained on large-scale, unlabeled decoded CAN data and subsequently adapted via fine-tuning to heterogeneous downstream objectives.; <ref:2602.00866#pg2>

The methodology follows a two-stage paradigm: (1) largescale pretraining on unlabeled decoded CAN signals using MLM, and (2) task-specific fine-tuning for heterogeneous downstream objectives.; <ref:2602.00866#pg2>

Unified Tokenization for Mixed CAN Signals

Central to this approach is a unified tokenization framework that bridges the gap between discrete linguistic representations and mixed discrete–continuous CAN signals.; <ref:2602.00866#pg2>

The tokenization scheme addresses the non-trivial nature of mixed signals by employing a fixed, predefined binning and enumeration schema to provide interpretable mappings between feature values and token IDs, emphasizing reproducibility and cross-dataset consistency.; <ref:2602.00866#pg2>

For continuous signals, an empirically calibrated quantization strategy is used which includes outlier handling, normalization using min–max bounds specific to each feature, and discretization into a fixed number of uniform bins based on temporal variation ri.; <ref:2602.00866#pg2>

Discrete variables are further divided into two subtypes: enumerated states mapped one-to-one to categorical tokens, and symbolic identifiers abstracted into meta-tokens like and to denote context shifts without inflating the vocabulary.; <ref:2602.00866#pg2>

All tokens derived from continuous and discrete variables are merged into a unified vocabulary of approximately 1,420 unique tokens, including infrastructure-level special tokens such as (timestamp marker) to explicitly encode temporal boundaries in the serialized token stream.; <ref:2602.00866#pg2>

Foundation Model Pretraining

The model is trained using a MLM objective, treating CAN signals as structured sequences analogous to natural language, specifically using a Bidirectional Encoder Representations from Transformers (BERT) style Transformer encoder without Next Sentence Prediction (NSP).; <ref:2602.00866#pg2>

The model minimizes the crossentropy loss between the predicted token distributions and the original masked tokens by randomly selecting fifteen percent of tokens in each sequence for masking, with 80% replaced by the special token.; <ref:2602.00866#pg2>

Pretraining validation is confirmed via MLM loss convergence and internal t-SNE visualization of learned embeddings, showing that tokens corresponding to semantically related signals form coherent clusters as well as moderate intra-clusters.; <ref:2602.00866#pg2>

Performance Evaluation

The evaluation focuses on how a large-scale pretrained representation transfers to specialized tasks with different label structures and class distributions, comparing the foundation model against established baselines like Generalized Linear Models (GLM) and 1D Convolutional Neural Networks (CNN).; <ref:2602.00866#pg2>

For binary classification, the Foundation CAN model achieved an F1 score of 76.1% in a 10:1 ratio setting, which was approximately 5% below the GLM baseline of 81.1%; <ref:2602.00866#pg2>

In the multi-class point-of-impact task, the foundation model attained a macro-F1 of 27.0% and a weighted F1 of 32.6%, outperforming the CNN baseline by approximately 3% and approximately 5%, respectively.; <ref:2602.00866#pg2>

Limitations and Future Work

The pretraining corpus is limited to nine days of driving data, which does not capture longer-term seasonal or environmental variability such as snow, rain, or regional driving conditions.; <ref:2602.00866#pg2>

The extremely imbalanced 100:1 collision detection stress test revealed the current model’s limitations, as the foundation model’s F1 score decreased to 54.2%, trailing the baseline’s 65.3% by approximately 11%; <ref:2602.00866#pg2>

These findings suggest that while the encoder captures coarse-grained CAN signal structure, it does not yet model the subtle temporal cues required for ultra-rare event discrimination under standard fine-tuning.; <ref:2602.00866#pg2>

Future directions include extending the temporal window to capture long-range dependencies, adopting finer-grained bin quantization to encode subtle signal variations using a larger token inventory, and increasing the sampling rate from 1 Hz to 5 Hz.; <ref:2602.00866#pg2>

Overall, the evaluation confirms that a single pretrained foundation CAN model can transfer meaningfully across tasks.; <ref:2602.00866#pg2>

The paper establishes the viability of multi-objective downstream generalization for automotive and auto insurance applications by treating properly decoded CAN data as a structured language.; <ref:2602.00866#pg2>

The foundation CAN LM presents a scalable, unified framework that bridges task-specific CAN modeling and foundation-style learning.; <ref:2602.

Improvements for AI systems

  1. Data Representation Improvement: Implement Unified Tokenization for Mixed CAN Signals to ensure semantic fidelity, reproducibility, and transferability across datasets. This allows models to handle mixed discrete–continuous signals by using a fixed binning and enumeration schema for continuous variables, thereby addressing the challenge of tokenizing non-trivial mixed signals.

  2. Model Architecture Enhancement: Utilize the proposed Foundation CAN Model that demonstrates multi-objective downstream generalization using a single pretrained backbone. This system can adapt to diverse predictive tasks by leveraging shared representations learned from large-scale pretraining on decoded signals, rather than training isolated task-specific models.

  3. Cross-Task Generalization Capability: The improved system can achieve cross-task generalization across heterogeneous objectives such as collision detection and point-of-impact prediction. This is validated by the results showing that the model's performance on a different task (e.g., Point of Impact) improves compared to a purely single-objective model, demonstrating that the foundation modeling paradigm, proven in NLP and CV, also holds for CAN data.

  4. Handling Extreme Imbalance: The system can be stress-tested under severe class imbalance (e.g., 100:1 collision detection ratio). While the current encoder shows limitations (does not yet model the subtle temporal cues required for ultra-rare event discrimination), this structure allows for targeted architectural refinement or more specialized task-aware finetuning approaches to address these specific temporal cues in future iterations.

  5. Transfer Learning Efficiency: The system enables a shift from task-specific modeling to a CAN foundation model, which reduces the need for redundant data preparation and training costs. This allows developers to benefit from shared representation learning across multiple automotive applications with a single pretrained backbone.

Sources

Related papers