Effects of unreliable automation, non-driving related task, and takeover time budget on drivers’ takeover performance and workload

Shahini, Farzaneh; Park, JunHo; Welch, Kyle; Zahabi, Maryam

doi:10.1080/00140139.2022.2069868

Cited by 12 publications

(2 citation statements)

References 69 publications

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“…In fact, Gold et al have shown that driver reactions are generally of poorer quality when they are faster [51]. To evaluate the TO quality, most researchers considered minimal time to collision (TTC), i.e., the minimum of the measured current times it would take a vehicle to collide with an obstacle, assuming constant speed and acceleration [27,30,39,43,50,[52][53][54]. In addition, various measures of lane deviation are usually considered: steering wheel angle variability (standard deviation) [29,53,55], maximal lateral acceleration (deceleration) [30,39,43,54], standard deviation of lane position [31,39,44,56], distance to centerline [34], and slope of obstacle avoiding trajectory [50].…”

Section: Related Work On Common Take-over Parametersmentioning

confidence: 99%

Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving

Gruden

Jakus

2023

Applied Sciences

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In conditionally automated driving, a vehicle issues a take-over request when it reaches the functional limits of self-driving, and the driver must take control. The key driving parameters affecting the quality of the take-over (TO) process have yet to be determined and are the motivation for our work. To determine these parameters, we used a dataset of 41 driving and non-driving parameters from a previous user study with 216 TOs while performing a non-driving-related task on a handheld device in a driving simulator. Eight take-over quality aspects, grouped into pre-TO predictors (attention), during-TO predictors (reaction time, solution suitability), and safety performance (off-road drive, braking, lateral acceleration, time to collision, success), were modeled using multiple linear regression, support vector machines, M5’, 1R, logistic regression, and J48. We interpreted the best-suited models by highlighting the most influential parameters that affect the overall quality of a TO. The results show that these are primarily maximal acceleration (88.6% accurate prediction of collisions) and the TOR-to-first-brake interval. Gradual braking, neither too hard nor too soft, as fast as possible seems to be the strategy that maximizes the overall TO quality. The position of the handheld device and the way it was held prior to TO did not affect TO quality. However, handling the device during TO did affect driver attention when shorter attention times were observed and drivers held their mobile phones in only one hand. In the future, automatic gradual braking maneuvers could be considered instead of immediate full TOs.

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Section: Related Work On Common Take-over Parametersmentioning

confidence: 99%

Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving

Gruden

Jakus

2023

Applied Sciences

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“…While subjective measures are convenient and provide direct insights, the results obtained are prone to be influenced by internal factors of the participant, such as their emotional state, motivation, and social environment, among other factors (Ariely & Berns, 2010). Moreover, since subjective measures are typically collected after the interaction, they may not be able to capture the real‐time feelings and experiences of the driver during their interaction with the in‐vehicle intelligent agent (Shahini et al, 2022). Therefore, certain studies have endeavored to adopt a more objective approach, utilizing physiological measures to explore the driver's internal state.…”

Section: Introductionmentioning

confidence: 99%

The effect of in‐vehicle agent embodiment on drivers' perceived usability and cognitive workload: Evidence from subjective reporting, ECG, and fNIRS

Ren,

Guo,

et al. 2024

Hum Ftrs & Erg Mfg Svc

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To improve the interaction between drivers and the in‐vehicle information system (IVIS), various intelligent agents, such as robot agents, virtual agents, and voice‐only agents, have been integrated into vehicles. However, it is not yet clear which type of in‐vehicle agent is best suited to the driving context. This study aims to investigate the effect of in‐vehicle agent embodiment on drivers' perceived usability and cognitive workload. In a within‐subject simulated driving experiment, 22 participants interacted with three different in‐vehicle agents (smartphone agent, robot agent, and virtual agent). Functional near‐infrared spectroscopy and electrocardiogram (ECG) were used to record prefrontal cortex activation and electrical changes associated with cardiac activity during simulated driving, respectively. The results show that the smartphone agent had the lowest perceived usability scores, oxygenated hemoglobin concentration variation (ΔHbO), and maximum ECG signal variation compared to baseline. There were no statistical differences in cognitive workload, perceived usability scores, brain area activation, and ECG signals between the robot agent and the virtual agent. The research findings demonstrate the positive effects of the anthropomorphic appearance of in‐vehicle agents on perceived usability and contribute to improving the design of in‐vehicle intelligent agents.

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Effects of levels of automation and non-driving related tasks on driver performance and workload: A review of literature and meta-analysis

Shahini

Zahabi

2022

Applied Ergonomics

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Effects of unreliable automation, non-driving related task, and takeover time budget on drivers’ takeover performance and workload

Cited by 12 publications

References 69 publications

Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving

Determining Key Parameters with Data-Assisted Analysis of Conditionally Automated Driving

The effect of in‐vehicle agent embodiment on drivers' perceived usability and cognitive workload: Evidence from subjective reporting, ECG, and fNIRS

Effects of levels of automation and non-driving related tasks on driver performance and workload: A review of literature and meta-analysis

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