Understanding the knowledge gaps in ecodriving: analysis of knowledge accuracy, uncertainty, and action regulation

Engineering notes

Key topics: autonomous driving, bev. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

<title>Abstract</title> Supporting ecodriving in battery-electric vehicles (BEVs) requires feedback aligned with drivers’ mental representations, as effective regulation depends on both know-how (strategies) and know-why (system understanding). When mental representations are inadequate or confidence exceeds actual knowledge, this can undermine performance and feedback processing. This study examined (i) drivers’ mental representations of ecodriving via thematic analysis, focusing on knowledge gaps (missing beliefs, situational references, reasoning depth) and references to input–comparator–output information, and (ii) effects of feedback approach on (a) knowledge accuracy, (b) uncertainty due to a lack of knowledge, (c) driving behaviour, and (d) performance.In a driving simulator study, participants (<italic>N</italic> = 63) drove under one of three conditions: no feedback (G1), real-time consumption trace (G2), or optimal speed recommendation (G3). Afterwards, they provided ecodriving tips and technical explanations, offering insights into their understanding. (i) Qualitative analysis showed broad familiarity with general ecodriving principles (e.g., smooth driving) but little precise or technically grounded guidance. Misconceptions were common, especially on regenerative braking, acceleration, and pedal use. (ii) The feedback approach had: (a) no effect on knowledge accuracy; (b) lower reported uncertainty in G3 vs. G1; (c) selective behavioural effects; (d) no effect on energy consumption. G2 used mechanical braking more and regenerative braking less than G3, while G3 drove slower in constant-speed phases than G1.Findings indicate that cognitively aligned feedback must go beyond prescribing speed selection or energy raw data. To foster robust ecodriving and reduce uncertainty, systems should support causal understanding and accurate, transferable mental representations.

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