Biased energy efficiency perception based on instantaneous consumption displays - Indication for heuristic energy
Vivien Esther Moll1, Thomas Franke1
1Institute for Multimedia and Interactive Systems, Engineering Psychology and Cognitive Ergonomics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
Applied Ergonomics
|March 18, 2021
Summary
Drivers using instantaneous consumption displays (ICDs) may misjudge energy efficiency due to biased perceptions. This study found drivers often overestimate average fuel consumption from ICDs, relying on heuristics rather than accurate data.
Area of Science:
- Human-Computer Interaction
- Automotive Engineering
- Cognitive Psychology
Background:
- Instantaneous consumption displays (ICDs) are central to understanding driving energy efficiency.
- A critical question is whether drivers can accurately interpret driving strategy efficiency from ICDs.
- Drivers' consumption judgments may be subject to biases, similar to the time-saving bias.
Purpose of the Study:
- To examine the accuracy of drivers in deriving average fuel consumption from dynamic ICD sequences.
- To investigate potential biases in drivers' perception of energy efficiency based on ICD feedback.
- To explore the relationship between display characteristics and estimation accuracy.
Main Methods:
- A controlled experiment involving 55 participants viewing videos of schematic ICDs with systematically varied maximum instantaneous consumption.
- Multilevel modeling was used to analyze the nested data structure.
- Analysis focused on overestimation of average consumption and correlation with heuristic vs. correct efficiency rankings.
Main Results:
- Participants consistently overestimated average consumption values presented on ICDs.
- The empirical ranking of consumption sequences correlated with heuristic judgments, not with actual efficiency.
- Higher peak consumption and shorter peak duration on ICDs led to greater estimation differences.
Conclusions:
- ICDs can lead to biased perceptions of energy efficiency, with drivers employing simplifying heuristics.
- Factors like knowledge and affinity for technology influence estimation bias, while prior experience with displays does not.
- Future research should explore debiasing interfaces and more naturalistic driving simulation settings.
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