Adaptive Modeling of User Preferences for Self-Driving Behaviors Using Verbal Interaction and AI
Bibliographic record
Abstract
This study proposes an adaptive framework for understanding and modeling user preferences in self-driving behaviors through natural language interaction. A user survey conducted in North America revealed strong demand for customizable autonomous vehicle (AV) features, motivating the need for dynamic preference modeling. To capture diverse and context-specific verbal expressions of user intent, we leverage speech recognition and fine-tune a lightweight T5-base language model to classify preferences across predefined AV behavior categories. Given the computational constraints of in-vehicle environments, we adopt the T5-base model due to its efficiency and suitability for embedded deployment, in contrast to larger-scale LLMs. To overcome data scarcity, we applied a data augmentation strategy using a teacher model, increasing classification accuracy from 25% to 97%. The framework can integrate vision-language models (e.g., BLIP-2, CLIP, etc.) and multimodal sensor fusion (camera, LiDAR, radar) to represent traffic situations and support context-aware interpretation of user input. This approach enables the system to generalize user preferences across similar traffic conditions through similarity-based propagation. By supporting condition-specific behavioral expressions, the system can interpret and adapt user preferences accordingly. The proposed framework facilitates scalable, context-aware, and user-centered adaptation of autonomous vehicle behaviors, contributing to improved personalization and may improve system usability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".