This study explores the application of Large Language Model (LLM)-based voice assistants, such as ChatGPT-4, in mitigating passive driving fatigue, while aiming to enhance driving performance and safety. Employing an empirical approach with a driving simulator and the LLM-based voice assistant "Driver Mate," the study focuses on conversation complexity and frequency as independent variables, and utilizes electroencephalogram (EEG), driving simulator data, and scales for measures. The findings reveal the effectiveness of in-vehicle LLM-based voice assistants, highlighting that low-complexity, high-frequency conversation is optimal for driver alertness and acceptance, while low-complexity, low-frequency interactions significantly improve driving performance. This study innovatively investigates the role of LLM-based voice assistants in alleviating driving fatigue, offering practical suggestions for future in-vehicle systems.

As the number of traffic accident casualties continues to rise globally, this study aims to enhance traffic safety during highway emergency repairs. Based on the fundamentals of human vision, this study designed a novel interactive barricade design, R-barricade, which aims to improve the visibility and warning effect of the barricade, as well as to enhance the interaction with the operator in order to reduce the risk of accidents. We established a comprehensive visual criteria framework, combined with eye-tracking technology, to systematically evaluate the R-barricade. The evaluation results show that the design effectively improves the driver’s attention to the barricade and effectively extends the gaze time, significantly improves the reaction time, and effectively improves the safety of the barricade. This study provides new perspectives for evaluating and improving traffic safety measures during highway emergency repairs and contributes scientific support to the advancement of interactive transport systems and road safety management.