Optimized Design of Driver-Assisted Navigation System for Complex Road Scenarios
Xiaolong Zhang, Yang Bian, Jushang Ou, Xiaohua Zhao, Jianling Huang, Yuheng Li · 2024
The driver-assisted navigation system is an invaluable tool. However, in intricate scenarios, drivers frequently commit navigation errors. To mitigate this issue, this study focuses on the F-type intersection with the highest incidence of deviations as the research subject. Road scenarios are replicated, and driver behavior data is collected through driving simulator technology. Speed and speed standard deviation are indicators for investigating the influence of driveway distance (DD), navigation prompt timing (NPT), and driver attributes on driving efficiency and safety stability using a generalized linear mixed model (GLMM). Findings reveal that excessively large or small driveway distances and navigation messages that are either premature or delayed negatively affect driving efficiency and safety stability. Consequently, it is recommended to adhere to a driveway distance range of 15-30m, accompanied by the prompt mode of {-300m, -150m, Confirmation}. Furthermore, although no random effects of driver attributes were identified, it is essential to recognize that driver attributes heavily influence their driving behavior in complex road scenarios. This study lays the foundation for optimizing the design of road facilities and navigation systems.