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    Human-robot take-over requests (TORs) in automated vehicles (AVs) require drivers to regain situational awareness (SA). Haptic feedback significantly improves the speed and safety of control transfers, with optimal reaction times between 2.6 and 6 seconds.

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    Area of Science:

    • Human-robot interaction
    • Automated vehicle safety
    • Human factors engineering

    Background:

    • The transition towards fully automated vehicles (AVs) necessitates effective human-robot take-over requests (TORs).
    • Failure in TORs can compromise driver situational awareness (SA) and lead to safety risks.
    • Understanding the impact of feedback modalities on control transfer is crucial for AV safety.

    Purpose of the Study:

    • To investigate the correlation between reaction time, multimodal feedback, and successful transfer of control in AVs.
    • To evaluate the effectiveness of different feedback modalities (audio, visual, haptic) during TORs.
    • To determine optimal reaction time windows for safe control transitions.

    Main Methods:

    • Experiments conducted in a full-sized driving simulator with 19 human participants.
    • Drivers engaged in a secondary task while the vehicle was in self-driving mode.
    • Seven feedback modalities for TORs were tested, including novel soft robotic seat haptics, with a 7-second window for regaining SA.

    Main Results:

    • Reaction times below 2.6 seconds or above 6 seconds led to unsuccessful control transfers.
    • Haptic feedback, when combined with existing audio and visual cues, resulted in faster and safer control transfers.
    • Multimodal feedback enhances driver's ability to regain situational awareness and react to incidents.

    Conclusions:

    • Specific reaction time windows are critical for successful TORs in AVs.
    • Haptic feedback offers a significant advantage in improving the timeliness and safety of control transfers.
    • Integrating advanced feedback mechanisms is essential for mitigating risks associated with AV automation level transitions.