A risk field-based metric correlates with driver’s perceived risk in manual and automated driving: A test-track study

Driver monitoring may become a standard safety feature to discourage distraction in vehicles with or without automated driving functions. Research to date has focused on technology for identifying driver distraction—little is known about how drivers will respond to monitoring systems. An exploratory online survey assessed the perceived risk and reasonableness associated with driving distractions as well as the perceived fairness of potential consequences when a driver monitoring system detects distractions under either manual driving or Level 2 automated driving. Although more re- search is needed, results suggested: (1) fairness was associated with perceived risk; (2) alerts generally were viewed as fair; (3) more severe consequences (feature lockouts, insurance reporting, automation lockouts, involuntary takeovers) generally were viewed as less fair; (4) fairness ratings were similar for manual versus Level 2 driving, with some potential exceptions; and (5) perceived risk of distractions was slightly lower with automated driving.

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Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track

Human Factors The Journal of the Human Factors and Ergonomics Society ◽

10.1177/00187208211051435 ◽

2021 ◽

pp. 001872082110514

Author(s):

Philipp Wintersberger ◽

Clemens Schartmüller ◽

Shadan Shadeghian-Borojeni ◽

Anna-Katharina Frison ◽

Andreas Riener

Keyword(s):

Cognitive Load ◽

Situation Awareness ◽

Reaction Times ◽

Gaze Behavior ◽

Automated Driving ◽

Test Track ◽

Handheld Device ◽

Critical Transitions ◽

Real Risk ◽

Task Modality

Objective Investigating take-over, driving, non-driving related task (NDRT) performance, and trust of conditionally automated vehicles (AVs) in critical transitions on a test track. Background Most experimental results addressing driver take-over were obtained in simulators. The presented experiment aimed at validating relevant findings while uncovering potential effects of motion cues and real risk. Method Twenty-two participants responded to four critical transitions on a test track. Non-driving related task modality (reading on a handheld device vs. auditory) and take-over timing (cognitive load) were varied on two levels. We evaluated take-over and NDRT performance as well as gaze behavior. Further, trust and workload were assessed with scales and interviews. Results Reaction times were significantly faster than in simulator studies. Further, reaction times were only barely affected by varying visual, physical, or cognitive load. Post-take-over control was significantly degraded with the handheld device. Experiencing the system reduced participants’ distrust, and distrusting participants monitored the system longer and more frequently. NDRTs on a handheld device resulted in more safety-critical situations. Conclusion The results confirm that take-over performance is mainly influenced by visual-cognitive load, while physical load did not significantly affect responses. Future take-over request (TOR) studies may investigate situation awareness and post-take-over control rather than reaction times only. Trust and distrust can be considered as different dimensions in AV research. Application Conditionally AVs should offer dedicated interfaces for NDRTs to provide an alternative to using nomadic devices. These interfaces should be designed in a way to maintain drivers’ situation awareness. Précis This paper presents a test track experiment addressing conditionally automated driving systems. Twenty-two participants responded to critical TORs, where we varied NDRT modality and take-over timing. In addition, we assessed trust and workload with standardized scales and interviews.

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Methodological Approach towards Evaluating the Effects of Non-Driving Related Tasks during Partially Automated Driving

Information ◽

10.3390/info11070340 ◽

2020 ◽

Vol 11 (7) ◽

pp. 340 ◽

Cited By ~ 1

Author(s):

Cornelia Hollander ◽

Nadine Rauh ◽

Frederik Naujoks ◽

Sebastian Hergeth ◽

Josef F. Krems ◽

...

Keyword(s):

Road Safety ◽

Methodological Approach ◽

Driver Distraction ◽

Automated Driving ◽

Test Protocol ◽

Lateral Control ◽

Test Track ◽

Future Studies ◽

Traffic Efficiency ◽

Level 2

Partially automated driving (PAD, Society of Automotive Engineers (SAE) level 2) features provide steering and brake/acceleration support, while the driver must constantly supervise the support feature and intervene if needed to maintain safety. PAD could potentially increase comfort, road safety, and traffic efficiency. As during manual driving, users might engage in non-driving related tasks (NDRTs). However, studies systematically examining NDRT execution during PAD are rare and most importantly, no established methodologies to systematically evaluate driver distraction during PAD currently exist. The current project’s goal was to take the initial steps towards developing a test protocol for systematically evaluating NDRT’s effects during PAD. The methodologies used for manual driving were extended to PAD. Two generic take-over situations addressing system limits of a given PAD regarding longitudinal and lateral control were implemented to evaluate drivers’ supervisory and take-over capabilities while engaging in different NDRTs (e.g., manual radio tuning task). The test protocol was evaluated and refined across the three studies (two simulator and one test track). The results indicate that the methodology could sensitively detect differences between the NDRTs’ influences on drivers’ take-over and especially supervisory capabilities. Recommendations were formulated regarding the test protocol’s use in future studies examining the effects of NDRTs during PAD.

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Effects of explanation-based knowledge regarding system functions and driver’s roles on driver takeover during conditionally automated driving: A test track study

Transportation Research Part F Traffic Psychology and Behaviour ◽

10.1016/j.trf.2020.11.015 ◽

2021 ◽

Vol 77 ◽

pp. 1-9

Author(s):

Huiping Zhou ◽

Keita Kamijo ◽

Makoto Itoh ◽

Satoshi Kitazaki

Keyword(s):

Automated Driving ◽

Test Track

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Evaluation of a Human–Machine Interface for Motion Sickness Mitigation Utilizing Anticipatory Ambient Light Cues in a Realistic Automated Driving Setting

Information ◽

10.3390/info12040176 ◽

2021 ◽

Vol 12 (4) ◽

pp. 176

Author(s):

Rebecca Hainich ◽

Uwe Drewitz ◽

Klas Ihme ◽

Jan Lauermann ◽

Mathias Niedling ◽

...

Keyword(s):

Motion Sickness ◽

Mitigation Strategy ◽

Human Machine Interface ◽

Mitigation Strategies ◽

Ambient Light ◽

Automated Vehicles ◽

Automated Driving ◽

Test Track ◽

Physical Discomfort ◽

Machine Interface

Motion sickness (MS) is a syndrome associated with symptoms like nausea, dizziness, and other forms of physical discomfort. Automated vehicles (AVs) are potent at inducing MS because users are not adapted to this novel form of transportation, are provided with less information about the own vehicle’s trajectory, and are likely to engage in non-driving related tasks. Because individuals with an especially high MS susceptibility could be limited in their use of AVs, the demand for MS mitigation strategies is high. Passenger anticipation has been shown to have a modulating effect on symptoms, thus mitigating MS. To find an effective mitigation strategy, the prototype of a human–machine interface (HMI) that presents anticipatory ambient light cues for the AV’s next turn to the passenger was evaluated. In a realistic driving study with participants (N = 16) in an AV on a test track, an MS mitigation effect was evaluated based on the MS increase during the trial. An MS mitigation effect was found within a highly susceptible subsample through the presentation of anticipatory ambient light cues. The HMI prototype was proven to be effective regarding highly susceptible users. Future iterations could alleviate MS in field settings and improve the acceptance of AVs.

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Take-over expectation and criticality in Level 3 automated driving: a test track study on take-over behavior in semi-trucks

Cognition Technology & Work ◽

10.1007/s10111-020-00626-z ◽

2020 ◽

Vol 22 (4) ◽

pp. 733-744

Author(s):

Alexander Lotz ◽

Nele Russwinkel ◽

Enrico Wohlfarth

Keyword(s):

High Speed ◽

Driver Behavior ◽

Reaction Times ◽

Multiple Time ◽

Automated Driving ◽

Passenger Cars ◽

Test Track ◽

Driving Assistance ◽

Level 3 ◽

Critical Situations

Abstract With the introduction of advanced driving assistance systems managing longitudinal and lateral control, conditional automated driving is seemingly in near future of series vehicles. While take-over behavior in the passenger car context has been investigated intensively in recent years, publications on semi-trucks with professional drivers are sparse. The effects influencing expert drivers during take-overs in this context lack thorough investigation and are required to design systems that facilitate safe take-overs. While multiple findings seem to cohere in passenger cars and semi-trucks, these findings rely on simulated studies without taking environments as found in the real world into account. A test track study was conducted, simulating highway driving with 27 professional non-affiliated truck drivers. The participants drove an automated Level 3 semi-truck while a non-driving-related task was available. Multiple time critical take-over situations were initiated during the drives to investigate four main objectives regarding driver behavior. (1) With these results, comparison of reaction times and behavior can be drawn to previous simulator studies. The effect of situation criticality (2) and training (3) of take-over situations is investigated. (4) The influence of warning expectation on driver behavior is explored. Results obtained displayed very quick time to hands on steering and time to first reaction all under 2.4 s. Highly critical situations generate very quick reaction times M = 0.81 s, while the manipulation of expectancy yielded no significant variation in reaction times. These reaction times serve as a reference of what can be expected from drivers under optimal take-over conditions, with quick reactions at high speed in critical situations.

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