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Paper Abstract and Keywords
Presentation 2026-03-03 13:03
A Quantitative Analysis of Driving Advice Capabilities of the Vision-Language Model LLaVA -- An Evaluation Using Real-World Naturalistic Driving Data --
Kaito Asai, Bin Zhou (Kyushu Univ.), Jianyu Huang (IM Motors), Arakawa Yutaka, Tsunenori Mine (Kyushu Univ.) ITS2025-82
Abstract (in Japanese) (See Japanese page) 
(in English) Vision-Language Models (VLMs) can generate natural-language driving advice
from visual scene understanding; however, the internal relationship between
scene recognition and advice generation has not been sufficiently quantified
in real-world driving scenarios.
This study presents a statistical analysis of driving advice generated by
LLaVA-1.5-7B using 983 high-risk pre-event frames extracted from the
SHRP2 naturalistic driving dataset.
We analyze the relationship between situational recognition and driving advice
using cross-tabulation, conditional probability estimation, chi-square tests,
Cramér’s~$V$, and regularized logistic regression.
The results show that driving advice is internally consistent with the model's
own scene recognition, while being strongly influenced by salient visual cues,
particularly brake lights.
These findings provide quantitative insights into the decision characteristics
of VLM-based driving assistance in safety-critical scenarios.
Keyword (in Japanese) (See Japanese page) 
(in English) Vision-Language Model / Driving Advice / Saliency Bias / Internal Consistency / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 380, ITS2025-82, pp. 7-12, March 2026.
Paper # ITS2025-82 
Date of Issue 2026-02-24 (ITS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF ITS2025-82

Conference Information
Committee ITS IEE-ITS  
Conference Date 2026-03-03 - 2026-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Centrair, Meeting Room 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ITS 
Conference Code 2026-03-ITS-ITS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Quantitative Analysis of Driving Advice Capabilities of the Vision-Language Model LLaVA 
Sub Title (in English) An Evaluation Using Real-World Naturalistic Driving Data 
Keyword(1) Vision-Language Model  
Keyword(2) Driving Advice  
Keyword(3) Saliency Bias  
Keyword(4) Internal Consistency  
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1st Author's Name Kaito Asai  
1st Author's Affiliation Kyushu University (Kyushu Univ.)
2nd Author's Name Bin Zhou  
2nd Author's Affiliation Kyushu University (Kyushu Univ.)
3rd Author's Name Jianyu Huang  
3rd Author's Affiliation IM Motors (IM Motors)
4th Author's Name Arakawa Yutaka  
4th Author's Affiliation Kyushu University (Kyushu Univ.)
5th Author's Name Tsunenori Mine  
5th Author's Affiliation Kyushu University (Kyushu Univ.)
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Speaker Author-1 
Date Time 2026-03-03 13:03:00 
Presentation Time 13 minutes 
Registration for ITS 
Paper # ITS2025-82 
Volume (vol) vol.125 
Number (no) no.380 
Page pp.7-12 
#Pages 6 
Date of Issue 2026-02-24 (ITS) 

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