## 3 Results
### Keyword Co-Occurrence and Clustering
#### 3.4.2 Knowledge Domain Clustering and Temporal Development
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cluster ID** & **Size** & **Silhouette** & **Mean (Year)** & **Label (LLR)** \\ \hline
0 & 24 & 0.895 & 2017 & mobility performance (2.61, 0.5); street crossing (2.61, 0.5); simulation (2.61, 0.5); cycling (2.61, 0.5); precise alarm (2.61, 0.5) \\ \hline
1 & 24 & 0.866 & 2017 & obstacle avoidance (8.96, 0.005); sensory substitution (4.44, 0.05); cyclist safety (3.25, 0.1); obstacle detection (2.97, 0.1); visually impaired (2.88, 0.1) \\ \hline
2 & 16 & 0.74 & 2019 & human computer interaction (hci) (7.77, 0.01); virtual reality (7.77, 0.01); human-centered computing (7.77, 0.01); pedestrians (4.25, 0.05); child cyclists (3.86, 0.05) \\ \hline
3 & 14 & 0.796 & 2021 & cyclist safety (18.63, 0.0001); augmented reality (5.55, 0.05); head-up display prototype (4.54, 0.05); vehicle2x communication (4.54, 0.05); mixed reality simulation (4.54, 0.05) \\ \hline
4 & 12 & 0.974 & 2019 & blind navigation (5.46, 0.05); indoor/outdoor navigation (5.46, 0.05); deviation detection (5.46, 0.05); temporal alignment (5.46, 0.05); mono camera (5.46, 0.05) \\ \hline
5 & 6 & 0.956 & 2020 & smart glasses (10.54, 0.005); legged locomotion (5.21, 0.05); walking came (5.21, 0.05); zebra crossing (5.21, 0.05); artificial intelligence of the internet of things (aicot) (5.21, 0.05) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Keyword cluster analysis results (LLR labeling method). severity analysis, forming a systematic evolution from basic assistance to precise prevention. The #1 obstacle avoidance cluster similarly began with basic research on \"navigation system\", integrated \"haptic feedback\" and \"collision avoidance\" technologies in 2010, developed into \"electronic travel aids\" and \"smart glasses\" applications in 2015, and focused on \"additive manufacturing\" and \"smart helmets\" in 2020, demonstrating a transformation from single-function to diversified intelligent solutions. Notably, the multiple dense connections between clusters #0 and #1 reveal the close collaborative development relationship between road safety for the visually impaired and obstacle avoidance research. The cross-cluster connections in Figure 8 reveal the disciplinary integration characteristics among key research hotspots. The medium-sized nodes in the #2 virtual reality cluster linking \"virtual reality\" with \"pedestrians\" and \"cyclists\" pioneered a new paradigm of immersive safety training; the #3 cyclist safety cluster, centered on the prominent node \"cyclist safety\", evolved from the basic needs identification of \"pedestrian safety\" to the technological application of \"head-mounted display\" and \"augmented reality\", with recent \"attention guidance\" and \"multimodal interaction\" further marking a key transition from passive protection to active perception enhancement; the high-frequency nodes \"blind navigation\" and \"object detection\" in the #4 blind navigation cluster evolving toward future \"artificial intelligence\" and \"active system\" further established intelligence as the dominant development direction for VRU safety technology. The overall timeline clearly demonstrates the evolution of research focus from early basic assistive tools to perception feedback technologies, then to the integration of virtual and augmented reality technologies, and finally projected to intelligent personalized systems, reflecting the co-deepening development process of VRU safety technology and practical needs, although the timeline labeling in the figure may need more precise calibration based on actual literature distribution. Figure 8: Timeline plot of 6 clusters of VRU wearable safety technology keywords.