Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance

Lu, Zhenji; Zhang, Bo; Feldhütter, Anna; Happee, Riender; Martens, Marieke; Winter, Joost de

doi:10.1016/j.trf.2019.03.018

Cited by 69 publications

(26 citation statements)

References 39 publications

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“…Based on those elements, drivers can be classified as Trustful or Distrustful according to their initial level of trust (Manchon et al, submitted). Dynamic learned trust is expected to fluctuate during actual interaction with automation (Bueno et al, 2016;Feldhütter et al, 2016;Gold et al, 2015), depending on AD's features (Miller et al, 2016;Payre et al, 2017), performance (Abe et al, 2018;Morris et al, 2017), and type of feedback provided (Häuslschmid et al, 2017;Koo et al, 2015;Lu et al, 2019;Wintersberger et al, 2017). These factors are expected to induce periodic trust recalibrations.…”

Section: Introductionmentioning

confidence: 99%

Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automation Driving Style?

Manchon¹,

Bueno²,

Navarro³

2021

Preprint

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Automated driving is becoming a reality, such technology raises new concerns about human-machine interaction on-road. Sixty-one drivers participated in an experiment aiming to better understand the influence of initial level of trust (Trustful vs Distrustful) on drivers’ behaviors and trust calibration during simulated Highly Automated Driving (HAD). The automated driving style was manipulated as positive (smooth) or negative (abrupt) to investigate human-machine early interactions. Trust was assessed over time through questionnaires. Drivers’ visual behaviors and take-over performances during an unplanned take-over request were also investigated. Results showed an increase of trust in automation over time, for both Trustful and Distrustful drivers regardless the automated driving style. Trust was also found to fluctuate over time depending on the specific events handled by the automated vehicle. Take-over performances were not influenced by the initial level of trust nor automated driving style.

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Section: Introductionmentioning

confidence: 99%

Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automation Driving Style?

Manchon¹,

Bueno²,

Navarro³

2021

Preprint

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“…Three measures were extracted to evaluate the drivers’ takeover performance. (1) hands-on-steering-wheel time (in seconds) is an indicator of motor readiness, which was measured from the moment the warning (the first warning in our study) was issued until the participants placed at least one hand on the steering wheel ( Lu et al, 2019 ); (2) takeover time (in seconds) is the minimum time between the issuance of the warning (the second warning in our study) and when the steering angle is greater than 2° or the brake percentage exceeded 10% ( Gold et al, 2013 ); (3) maximum resulting acceleration (in meters per square second) is the takeover quality indicator, which was defined as:…”

Section: Methodsmentioning

confidence: 99%

“…They found that the two-stage warning system can lead to better takeover performance, higher SA, less driving stress, and higher subjective ratings than the single-stage warning systems. Lu et al (2019) compared the effect of TOR-only with the request, which provided a monitoring request (MR) (aims to let drivers achieve a monitoring transition) before a possible TOR (i.e., MR + TOR). They found that drivers spent a shorter takeover time and a longer minimum time to collision in the MR + TOR condition than in the TOR-only.…”

Section: Related Workmentioning

confidence: 99%

Optimal Time Intervals in Two-Stage Takeover Warning Systems With Insight Into the Drivers’ Neuroticism Personality

et al. 2021

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Conditional automated driving [level 3, Society of Automotive Engineers (SAE)] requires drivers to take over the vehicle when an automated system’s failure occurs or is about to leave its operational design domain. Two-stage warning systems, which warn drivers in two steps, can be a promising method to guide drivers in preparing for the takeover. However, the proper time intervals of two-stage warning systems that allow drivers with different personalities to prepare for the takeover remain unclear. This study explored the optimal time intervals of two-stage warning systems with insights into the drivers’ neuroticism personality. A total of 32 drivers were distributed into two groups according to their self-ratings in neuroticism (high vs. low). Each driver experienced takeover under the two-stage warning systems with four time intervals (i.e., 3, 5, 7, and 9 s). The takeover performance (i.e., hands-on-steering-wheel time, takeover time, and maximum resulting acceleration) and subjective opinions (i.e., appropriateness and usefulness) for time intervals and situation awareness (SA) were recorded. The results showed that drivers in the 5-s time interval had the best takeover preparation (fast hands-on steering wheel responses and sufficient SA). Furthermore, both the 5- and 7-s time intervals resulted in more rapid takeover reactions and were rated more appropriate and useful than the 3- and 9-s time intervals. In terms of personality, drivers with high neuroticism tended to take over immediately after receiving takeover messages, at the cost of SA deficiency. In contrast, drivers with low neuroticism responded safely by judging whether they gained enough SA. We concluded that the 5-s time interval was optimal for drivers in two-stage takeover warning systems. When considering personality, drivers with low neuroticism had no strict requirements for time intervals. However, the extended time intervals were favorable for drivers with high neuroticism in developing SA. The present findings have reference implications for designers and engineers to set the time intervals of two-stage warning systems according to the neuroticism personality of drivers.

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“…Radlmayr et al [11] used the maximum longitudinal acceleration as a takeover quality measure, whereas Zeeb et al [12] used the maximum lateral acceleration and centerline deviation. In many other studies, takeover quality is quantified using the minimum time to collision (MTTC) [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Takeover Quality: Assessing the Effects of Time Budget and Traffic Density with the Help of a Trajectory-Planning Method

Doubek

Loosveld

Happee

et al. 2020

Journal of Advanced Transportation

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In highly automated driving, the driver can engage in a nondriving task but sometimes has to take over control. We argue that current takeover quality measures, such as the maximum longitudinal acceleration, are insufficient because they ignore the criticality of the scenario. This paper proposes a novel method of quantifying how well the driver executed an automation-to-manual takeover by comparing human behaviour to optimised behaviour as computed using a trajectory planner. A human-in-the-loop study was carried out in a high-fidelity 6-DOF driving simulator with 25 participants. The takeover required a lane change to avoid roadworks on the ego-lane while taking other traffic into consideration. Each participant encountered six different takeover scenarios, with a different time budget (5 s, 7 s, or 20 s) and traffic density level (low or medium). Results showed that drivers exhibited a considerably higher longitudinal and lateral acceleration than the optimised behaviour, especially in the short time budget scenarios. In scenarios of medium traffic density, the trajectory planner showed a moderate deceleration to let a vehicle in the left lane pass; many participants, on the other hand, did not decelerate before making a lane change, resulting in a dangerous emergency brake of the left-lane vehicle. In conclusion, our results illustrate the value of assessing human takeover behaviour relative to optimised behaviour. Using the trajectory planner, we showed that human drivers are unable to behave optimally in urgent scenarios and that, in some conditions, a medium deceleration, as opposed to a maximal or minimal deceleration, is optimal.

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Beyond mere take-over requests: The effects of monitoring requests on driver attention, take-over performance, and acceptance

Cited by 69 publications

References 39 publications

Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automation Driving Style?

Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automation Driving Style?

Optimal Time Intervals in Two-Stage Takeover Warning Systems With Insight Into the Drivers’ Neuroticism Personality

Takeover Quality: Assessing the Effects of Time Budget and Traffic Density with the Help of a Trajectory-Planning Method

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