## 1 Introduction  
Fatigue driving has become one of the major threats to road traffic safety worldwide. Drivers in a fatigued state experience a range of physiological and cognitive impairments, such as decreased attention, slower reaction times, impaired judgment, and operational errors. These factors significantly increase the likelihood of traffic accidents [1, 2]. Data indicate that fatigue driving accounts for 35% to 45% of road traffic accidents, resulting in approximately 1550 fatalities, 71,000 injuries, and an economic loss of USD 12.5 billion annually [3]. Professional driver groups--particularly long-haul truck drivers, taxi drivers, and individuals with long commuting hours--are especially susceptible to chronic fatigue due to irregular work schedules, limited opportunities for rest, and prolonged periods of high-intensity driving. These factors place them at high risk of fatigue-related driving incidents, and industry-specific attitudes and contextual factors further influence the prevalence of such behavior [4]. Consequently, accurately detecting driver fatigue and implementing effective intervention strategies has become a critical topic in interdisciplinaryresearch spanning intelligent transportation systems, human factors' engineering, and behavioral sciences. At present, interventions for fatigue driving primarily focus on two aspects: fatigue state detection and real-time alert mechanisms. Fatigue detection technologies have evolved from early-stage subjective questionnaires and physiological indicator methods [5] to intelligent recognition systems centered on behavioral data and image processing. Features such as electroencephalogram (EEG) signals [6], eye-tracking [7], facial expression analysis [8], and driving behavior [9] have been widely employed in the development of fatigue detection models. The integration of deep learning algorithms has significantly enhanced the accuracy and timeliness of these systems. For instance, a recent study proposed a novel method combining deep convolutional neural networks with emotional state analysis to detect driver fatigue, improving the safety and reliability of intelligent driving assistance systems under varying lighting conditions [10]. In addition, several automotive companies have introduced integrated driving assistance systems. Cooperative Advanced Driver Assistance Systems (CADAS), for example, enhance road safety and mitigate fatigue by providing traffic information several kilometers ahead [11]. However, most existing systems rely on fixed-threshold judgments and uniform alert strategies, lacking the capacity to perceive drivers' psychological states and interaction preferences. This often results in issues such as \"over-reminding\" or \"delayed response\", ultimately affecting user acceptance. In addition to detection technologies, multimodal stimulation-based intervention approaches have become a key focus of research. Scholars have explored non-contact sensory stimuli--such as auditory, olfactory, and visual cues--to awaken drivers. In terms of auditory stimulation, music has been shown to enhance task performance during driving and effectively increase driver alertness [12; 13]. For olfactory stimulation, studies have found that refreshing scents such as peppermint and citrus can reduce subjective feelings of fatigue and delay the onset of fatigue [14; 15; 16]. Visual interventions, including speed alerts and flashing warnings in the mid-peripheral visual field, have also been confirmed to improve wakefulness during driving [17; 18]. The i-DREAMS fatigue intervention strategy aims to keep drivers within a safe driving zone by providing both real-time and post-trip interventions, incorporating gamification elements to enhance engagement [19].