Assessing Energy-Related Situation Awareness Using Self-Controlled Occlusion During Electric Vehicle Driving Scenes
DOI: 10.54941/ahfe1005219
archive: archived pipeline: cataloged verified
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Summary
This pilot study investigates Energy Dynamics Awareness (EDA), a theoretical construct describing how drivers perceive and understand energy flows in electric vehicles (EVs). Optimal eco-driving is challenging due to volatile, bidirectional energy flows that humans cannot directly sense, necessitating effective visual feedback displays. The research addresses the gap in measuring EDA under varying cognitive workloads, particularly in online settings where eye-tracking hardware is unavailable. The authors aimed to validate a novel method for assessing gaze behavior using self-controlled occlusion and to examine the relationship between visual attention, cognitive load, and EDA measures. The study employed a video-based online experiment with 29 participants who viewed driving scenes from a Renault ZOE EV. Participants performed a parallel visuospatial n-back task to induce low (0-back) or high (1-back) cognitive workload. To measure visual attention without eye-trackers, the researchers implemented self-controlled occlusion, allowing participants to manually toggle the visibility of the windshield view or the energy display using a keyboard. EDA was assessed through three methods: performance-based tasks (estimating absolute consumption and identifying efficient driving strategies), a subjective EDA rating scale, and gaze behavior metrics derived from the occlusion data (sampling period and uncertainty). Results indicated that the n-back task successfully manipulated cognitive workload, evidenced by lower accuracy and higher mental load ratings in the high-workload condition. High workload significantly impaired performance-based EDA; participants’ absolute consumption estimates were less accurate under high load. Gaze behavior was also affected, with significantly longer sampling periods (time between display checks) and higher uncertainty metrics in the high-workload condition, suggesting reduced visual attention to energy information. However, subjective EDA ratings and efficiency identification accuracy did not differ between workload conditions. Crucially, no significant correlations were found between the subjective, performance-based, and gaze-based EDA measures. The findings highlight a divergence between subjective and objective measures of situation awareness, suggesting that drivers’ self-assessed awareness may not reflect their actual comprehension or visual attention. The study validates self-controlled occlusion as a promising, low-cost method for collecting gaze indicators in online studies. Practically, the results imply that cognitive workload reduces both the accuracy of energy consumption understanding and the frequency of display monitoring. This underscores the need for adaptive energy displays that account for driver workload, potentially shifting from complex informational displays to simpler, action-oriented feedback in high-load scenarios. The authors recommend future research with larger samples and real-world driving simulations to enhance ecological validity.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | pdftotext | — | — | 4 | 2026-08-10 |
| clean | success | clean | — | — | 2 | 2026-08-10 |
| chunk | success | chunk | — | — | 2 | 2026-08-10 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 2 | 2026-08-10 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.6-27b-nvidia | summ-v5 | 2 | 2026-08-10 |
| tag | success | vector_similarity | — | — | 17 | 2026-08-11 |
| verify | success | — | — | — | 2 | 2026-08-10 |
Summary generated by qwen3.6-27b-nvidia on 2026-08-10; verification: verified.
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