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Browsing by Author "Sanghavi, Harsh Kamalesh"

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    Exploring the Influence of Anger on Takeover Performance in Semi-automated Vehicles
    Sanghavi, Harsh Kamalesh (Virginia Tech, 2020-05-22)
    As autonomy in vehicles increases, the role of the driver will diminish, moving on to more non-driving related tasks. We are at a juncture at which cars have the ability to drive themselves, but only if the driver is ready to take over control of the vehicle when required (e.g., Tesla autopilot). Therefore, it is important that adequate alerts are used to warn drivers in various contexts to take control back from these semi-automated vehicles. Considerable research has been conducted to design the safest alerts for the takeover transition. However, more systematic research is still required to accurately predict driver responses to different parameters of the alerts. Also, takeover research has not considered drivers' states (e.g., emotions). Anger is one of the emotions that has been shown to impair driver judgment and performance. There is limited research on how anger might influence takeover performance in semi-automated driving. This study aimed to investigate the influence of anger on takeover reaction time and safety by comparing angry and neutral drivers. Additionally, the effects of increased perceived urgency of auditory alarms on takeover reaction time were measured. Data from this research was used to help test mathematical driver behavior modeling using the QN-MHP cognitive architecture. Using a motion-based simulator, 36 participants performed takeovers in semi-automated vehicle on a 3-lane highway. Between takeovers, participants performed a secondary task (i.e., online game) on a tablet. There were no significant differences in takeover reaction time between angry and neutral drivers. However, angry drivers drove faster which can lead to dangerous collisions. Angry drivers took longer to change lanes with lower steering wheel angles. Neutral drivers' slower speeds and higher steering wheel angles indicated that they initiated the lane change earlier, and thus, made safer lane changes. As expected, higher frequency and more repetitions of the auditory takeover displays led to faster takeover reaction times. QN-MHP model predictions of takeover reaction times resulted in a 68.92% correlation with the empirical data collected. The results of this study suggest that angry drivers perform riskier than neutral drivers when taking over control of a semi-automated vehicle. This study is expected to make a significant contribution to research on the influence of emotion, specifically, anger on takeover performance in semi-automated vehicles as well as takeover display design.