TY  - JOUR
T1  - ViT-Based Future Road Image Prediction: Evaluation via VLM
AU  - Kim, Donghyun 
AU  - Kwon, Jaerock 
AU  - Nam, Haewoon 
JO  - The Journal of Korean Institute of Communications and Information Sciences
PY  - 2025
DA  - 2025/1/1
DO  - 10.7840/kics.2025.50.10.1532
KW  - Autonomous Driving
KW  - Vision-
KW  - Language Model
KW  - Semantic
KW  - Evaluation
KW  - Vision Transformer
AB  - This paper proposes a Vision Transformer (ViT)-based model for predicting future driving scenes. The proposed ViT architecture processes input images as patches and leverages the attention mechanism to efficiently learn global visual information, while also integrating control inputs to effectively capture correlations between visual context and driving actions. Experimental results show that the ViT-based model generates sharper images than the baseline and achieves higher semantic similarity in explanation evaluations using a Vision-Language Model (VLM). These results suggest that the ViT architecture is effective not only for future prediction but also for explainable autonomous driving control.