Dynamic Optimization of Adaptive Vehicle Lighting Systems: A Multimodal Assessment of Driver Performance and Well-being

Chai, Ruizhuo; Wu, W · 2025 · Crossref

DOI: 10.54941/ahfe1005855

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Summary

This study addresses the optimization of Adaptive Vehicle Lighting Systems (AVLS) by developing a multimodal assessment framework to evaluate driver performance and well-being. Motivated by the need to improve road safety through active warning systems that adapt to dynamic conditions, the research seeks to identify personalized lighting and auditory signal parameters that enhance situational awareness while minimizing cognitive load and stress. The authors argue that existing research often isolates warning channels or parameters, lacking a comprehensive evaluation of how audiovisual combinations affect human-machine interaction. The methodology employs a simulated driving experiment involving 20 participants with at least two years of driving experience. Using a laboratory setup with an eye tracker and E-Prime 3.0 software, participants responded to hazard scenarios presented via front and rear camera footage. The study manipulated four independent variables in a 3×2×3×2 factorial design: sound loudness (60, 65, 70 dB), sound warning frequency (4, 8 Hz), color saturation (30%, 65%, 100%), and visual flashing frequency (1, 2 Hz). Dependent variables were measured across three dimensions: subjective emotional states (pleasure, arousal, dominance) via Likert scales; objective performance (reaction time, task completion rate); and physiological indicators (pupil diameter for cognitive load, gaze duration for distraction). The results revealed significant interactions among signal parameters. For subjective pleasure, the optimal configuration involved high loudness with low warning frequency, medium loudness with high saturation, and medium saturation with low flashing frequency. Arousal was significantly enhanced by high flashing frequency, with a three-way interaction involving loudness, warning frequency, and saturation. Dominance peaked under medium loudness, high warning frequency, medium saturation, and low flash frequency. Physiologically, cognitive load (pupil diameter) was not significantly impacted by any parameters, though the lowest load occurred with medium loudness, low warning frequency, high saturation, and low flash frequency. Cognitive distraction was minimized by high color saturation and low flashing frequency. Objectively, higher audio loudness significantly reduced task reaction times, while task completion rates remained consistently high (>85%) across all conditions. The study identified an overall optimal combination of high audio loudness, low audio warning frequency, medium color saturation, and high flashing frequency. The significance of this work lies in its provision of a multidimensional evaluation method for AVLS, moving beyond single-channel assessments. By integrating subjective, objective, and physiological metrics, the study offers precise guidance for designing warning systems that balance alertness with driver comfort. The findings suggest that moderate parameter intensities facilitate efficient task performance while maintaining a positive driving experience. The authors conclude that this approach advances the scientific understanding of AVLS, offering practical implications for developing smarter, safer vehicle lighting systems that adapt to real-world driving environments, although they note limitations regarding the simulated nature of the data and the need for future real-world validation.

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Topics

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• dark adaptation mesopic
• sensory abilities
• feedback modes
• visual occlusion
• adaptive driving beam
• ehmi external hmi

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software, validation psychometrics