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Exploring the impact of haptic feedback locations and mid-air haptic technology on driver's takeover performance and user experience in automated vehicles

Ruiheng Lan et al.

International Journal of Industrial Ergonomics2026https://doi.org/10.1016/j.ergon.2026.103878article
AJG 2
Weight
0.50

What the paper says

As automated vehicles (AVs) increasingly allow drivers to engage in non-driving-related tasks (NDRTs), effective takeover request (TOR) systems become critical for safety. This study evaluates how four haptic feedback configurations—two technologies (vibrotactile vs. ultrasonic mid-air haptics, UMH) delivered to two locations (hand vs. face)—shape driver takeover performance and user experience in Level 3 driving. In a simulator study with 32 participants, vibrotactile cues consistently produced faster reaction and takeover times than UMH, demonstrating clear advantages for time-critical TORs. Feedback location predominantly influenced subjective experience: hand-based cues were rated as more pleasant, whereas face-based cues offered high perceptual salience but lower comfort. Despite slower responses, UMH was valued for its contactless convenience, though concerns about tracking reliability limited trust. These findings show that haptic configuration—not just modality—critically determines TOR effectiveness, and they provide actionable guidance for designing safer, more intuitive, and user-centered AV HMIs. The results also highlight key technical barriers for UMH that must be addressed to support broader adoption in future AVs. • Comparison of haptic feedback delivered by wearables on the hand and face in AV HMIs. • Comparison between vibrotactile feedback and mid-air haptic feedback in AV HMIs. • Design recommendations for integrating haptic feedback into AV HMIs.

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https://doi.org/https://doi.org/10.1016/j.ergon.2026.103878

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@article{ruiheng2026,
  title        = {{Exploring the impact of haptic feedback locations and mid-air haptic technology on driver's takeover performance and user experience in automated vehicles}},
  author       = {Ruiheng Lan et al.},
  journal      = {International Journal of Industrial Ergonomics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.ergon.2026.103878},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.