Employing synesthesia-based warnings to enhance road safety during an automated driving

Zhuo, Liang; Zhao, Danhua · 2022 · Crossref

DOI: 10.54941/ahfe1001932

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Summary

This study addresses the challenge of designing effective warning systems for Advanced Driver Assistance Systems (ADAS) in Level 2–3 autonomous vehicles. As drivers engage in secondary tasks during automated driving, their perception of emergencies may diminish, increasing accident risk. While previous research has demonstrated that multimodal warnings (combining visual and auditory signals) reduce response times, most studies focus merely on the superposition of sensory channels. This paper investigates the impact of *synesthesia*—the psychological correlation between different senses—on driver workload and reaction speed. The authors hypothesize that aligning auditory and visual stimuli according to synesthetic principles (e.g., matching sound frequency with specific colors and visual areas) will enhance information transmission efficiency and reduce cognitive load compared to non-synesthetic multimodal warnings. To test this hypothesis, the researchers conducted a controlled experiment using a high-fidelity driving simulator with 30 licensed participants. The study employed a between-subjects design, dividing participants into a non-synesthesia group (Group A) and a synesthesia group (Group B). Participants experienced two types of scenarios: Low Load Scenarios (LLS), such as steering reminders, and High Load Scenarios (HLS), such as emergency braking for pedestrians. In Group A, warnings consisted of standard white lights and generic beep tones. In Group B, warnings were designed based on synesthetic correlations: low-frequency sounds were paired with blue lights and small HUD areas for LLS, while high-frequency sounds were paired with red lights and large HUD areas for HLS. Participants performed secondary tasks (e.g., using a mobile phone) to simulate distraction. Data collection included quantitative measures of braking reaction time and NASA-TLX workload scores, as well as qualitative post-experiment interviews. The results indicated that synesthesia-based warnings significantly improved driver performance. Quantitative analysis revealed a significant correlation between the experimental condition and braking reaction time ($p < 0.01$). Drivers in the synesthesia group exhibited faster reaction times than those in the non-synesthesia group, particularly in high-load emergency scenarios. Furthermore, the NASA-TLX results showed that the synesthesia group reported significantly lower overall workload, specifically in mental demand, temporal demand, effort, and frustration ($p < 0.01$ for the first three; $p < 0.05$ for frustration). Qualitative interviews supported these findings, with participants in the synesthesia group showing a strong preference for the high-frequency/red/large-area combination during emergencies, citing its effectiveness in capturing attention. The study concludes that incorporating synesthetic principles into ADAS warning designs enhances road safety by accelerating driver response times and reducing cognitive workload during automated driving. The findings suggest that the coherence between auditory and visual channels, rather than their mere presence, is critical for effective human-machine interaction. This approach offers a practical framework for designing early warning systems in future autonomous vehicles, though the authors note that further validation in real-world driving environments is necessary to confirm these simulator-based results.

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Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• multimodal feedback
• auditory warnings
• feedback modes
• multisensory crossmodal
• situational awareness
• haptic feedback

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• Applied Guidance: design guidelines
• Methodological Resource: tool software
• Theoretical Contribution: theory or model