Quantifying Risk Levels for Active Safety Systems in Autonomous Forest Machinery Using Vision Language Models

Forestry is recognized as one of the most dangerous industries in the world. To enhance forestry safety, autonomous machinery and safety systems for such machinery are essential. This study aims to introduce large language models (LLMs)—especially their extensions to images, vision–language models (VLMs)—to enable human-like decision-making for autonomous forest machinery. This research focused on VLMs as an active safety system that can adapt to environments and evaluated the effectiveness of a system that quantitatively makes decisions regarding hazard levels using contrastive language–image pretraining (CLIP). The results of industry type, tree state, and road state classification using pretrained models showed that for three tasks—forestry identification, hung-up tree detection, and road collapse sensing—the target classes consistently exhibited higher similarity with disaster texts compared with nontarget classes. Although the F1 scores were 0.693, 0.324 and 0.634, respectively—indicating that the system is insufficient as a direct active safety system—the application of a similarity threshold optimized to maintain a recall of 0.9 yielded F1 scores of 0.291 and 0.584 for tree state and road state, respectively. These results suggest that the system can potentially be used as a quantitative indicator of hazard by setting a threshold on the similarity score.