# Driving Style Recognition Like an Expert Using Semantic Privileged Information from Large Language Models

arXiv: https://arxiv.org/abs/2508.13881 · HTML: https://mlsanity.com/paper/2508.13881/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Chain-of-thought reasoning for driving, one of 17 directions in LLM reasoning.
- Builds on 23 of its 90 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Placed in

- LLM reasoning / [Chain-of-thought reasoning for driving](https://mlsanity.com/lr/chain-of-thought-reasoning-for-driving/)
- End-to-end autonomous driving / [Large language models for driving](https://mlsanity.com/driving/large-language-models-for-driving/)

## Builds on (most cited first, 23 of 23)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of Highly Automated Driving Systems](https://mlsanity.com/paper/1810.05642/) (2018)
- [Drive Like a Human: Rethinking Autonomous Driving with Large Language Models](https://mlsanity.com/paper/2307.07162/) (2023)
- [Self-Driving Cars: A Survey](https://mlsanity.com/paper/1901.04407/) (2019)
- [Drive as You Speak: Enabling Human-Like Interaction with Large Language Models in Autonomous Vehicles](https://mlsanity.com/paper/2309.10228/) (2023)
- [Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning](https://mlsanity.com/paper/1901.00569/) (2019)
- [A Physics-Informed Deep Learning Paradigm for Car-Following Models](https://mlsanity.com/paper/2012.13376/) (2020)
- [FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling](https://mlsanity.com/paper/2306.05381/) (2023)
- [Person Re-identification with Metric Learning using Privileged Information](https://mlsanity.com/paper/1904.05005/) (2019)
- [Driving Style Alignment for LLM-powered Driver Agent](https://mlsanity.com/paper/2403.11368/) (2024)
- [Driving Style Analysis Using Primitive Driving Patterns With Bayesian Nonparametric Approaches](https://mlsanity.com/paper/1708.08986/) (2017)
- [When does Privileged Information Explain Away Label Noise?](https://mlsanity.com/paper/2303.01806/) (2023)
- [Learning and Refining of Privileged Information-based RNNs for Action Recognition from Depth Sequences](https://mlsanity.com/paper/1703.09625/) (2017)
- [A Learning-Based Approach for Lane Departure Warning Systems with a Personalized Driver Model](https://mlsanity.com/paper/1702.01228/) (2017)
- [Shareable Driving Style Learning and Analysis with a Hierarchical Latent Model](https://mlsanity.com/paper/2310.15057/) (2023)
- [Drive Mode Optimization and Path Planning for Plug-in Hybrid Electric Vehicles](https://mlsanity.com/paper/1611.01032/) (2016)
- [Development and Evaluation of Two Learning-Based Personalized Driver Models for Car-Following Behaviors](https://mlsanity.com/paper/1703.03534/) (2017)
- [100 Drivers, 2200 km: A Natural Dataset of Driving Style toward Human-centered Intelligent Driving Systems](https://mlsanity.com/paper/2406.07894/) (2024)

## Abstract (arXiv)

Existing driving style recognition systems largely depend on low-level sensor-derived features for training, neglecting the rich semantic reasoning capability inherent to human experts. This discrepancy results in a fundamental misalignment between algorithmic classifications and expert judgments. To bridge this gap, we propose a novel framework that integrates Semantic Privileged Information (SPI) derived from large language models (LLMs) to align recognition outcomes with human-interpretable reasoning. First, we introduce DriBehavGPT, an interactive LLM-based module that generates natural-language descriptions of driving behaviors. These descriptions are then encoded into machine learning-compatible representations via text embedding and dimensionality reduction. Finally, we incorporate them as privileged information into Support Vector Machine Plus (SVM+) for training, enabling the model to approximate human-like interpretation patterns. Experiments across diverse real-world driving scenarios demonstrate that our SPI-enhanced framework outperforms conventional methods, achieving F1-score improvements of 7.6% (car-following) and 7.9% (lane-changing). Importantly, SPI is exclusively used during training, while inference relies solely on sensor data, ensuring computational efficiency without sacrificing performance. These results highlight the pivotal role of semantic behavioral representations in improving recognition accuracy while advancing interpretable, human-centric driving systems.