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.

With the advancement of autonomous driving technology, especially the growing adoption of SAE Level 3 and above systems, drivers are transitioning from active controllers to supervisors who must take over in emergencies. For hearing-impaired drivers in a fatigued state, conventional voice alert systems often fail to provide timely and effective warnings, increasing safety risks. This study proposes an adaptive in-vehicle interface that combines visual and tactile feedback to address these challenges. Experiments were conducted to evaluate response accuracy, reaction time, and cognitive load under varying levels of driver fatigue. The findings show that the integration of visual and tactile cues significantly improves takeover efficiency and reduces mental strain in fatigued drivers. These results highlight the potential of multimodal designs in enhancing the safety and driving experience for hearing-impaired individuals. By providing practical strategies and evidence-based insights, this research contributes to the development of more inclusive and effective interaction designs for future autonomous driving systems.

In order to effectively curb the rapid growth trend in Spartina alterniflora in coastal cities of China, this study proposes an innovative mechanical equipment design scheme for eradicating Spartina alterniflora. Based on literature analysis and field research, the AHP (analytic hierarchy process) model is constructed to quantify and prioritize the diverse needs of users for control equipment. Subsequently, the House of Quality (HOQ) in QFD (Quality Function Deployment) is used to analyze the key components and structure of the equipment to ensure its performance and feasibility in practical applications. Finally, combined with the Theory of Inventive Problem Solving (TRIZ), the potential problems encountered in the structural design of the equipment are analyzed, and the corresponding creative principles are applied to solve the contradictions and complete the optimal scheme design. This study, via the acquisition of user needs and further analysis of the machinery’s structure, proposes a scheme that can address many problems related to Spartina alterniflora in China and provide new technical ideas for the field of wetland environmental protection.

The survivability of out-of-hospital cardiac arrest patients in China is notably low, at less than 1%, primarily due to difficulties in accessing nearby Automated External Defibrillator (AED) devices during emergencies. Therefore, the strategic deployment of AEDs is crucial for improving patient survival. This study adopts a novel hybrid methodology that integrates the Fuzzy Analytic Network Process (FANP) and Grey Relational Analysis (GRA) to assess and prioritize the critical factors for optimizing AED deployment in China. In the FANP model, the rate of cardiopulmonary resuscitation training among individuals in the area, the rate of AED training among individuals in the area, and records of AED device usage within the area are key factors for improving the effectiveness of AED deployment. Furthermore, in the GRA model, the factors with higher gray relational degrees are Visibility, Social Environment, and Accessibility. This analysis sheds light on which secondary indicators can have the most significant impact on improving specific primary indicators. The insights from this study can guide the optimization of AED deployment strategies in China and offer practical solutions for enhancing the deployment of relevant public medical facilities.