Prediction of take-over time in highly automated driving by two psychometric tests

Körber, Moritz; Weißgerber, Thomas; Blaschke, Christoph; Farid, Mehdi; Kalb, Luis

doi:10.15446/dyna.v82n193.53496

Cited by 31 publications

(7 citation statements)

References 27 publications

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“…RTs are typically recorded from the start of a takeover request (Blommer et al, 2017;Dogan et al, 2017;Eriksson, Banks, & Stanton, 2017;Eriksson & Stanton, 2017a;Feldhütter et al, 2016;Gold, Damböck, Lorenz, & Bengler, 2013;Körber, Weißgerber, Kalb, Blaschke, & Farid, 2015;Lorenz, Kerschbaum, & Schumann, 2014;Melcher, Rauh, Diederichs, Widlroither, & Bauer, 2015;Naujoks et al, 2017;Naujoks, Mai, & Neukum, 2014;Naujoks, Purucker, Neukum, Wolter, & Steiger, 2015;Payre, Cestac, Dang, Vienne, & Delhomme, 2017;Petermeijer, Bazilinskyy, Bengler, & de Winter, 2017;Politis, Brewster, & Pollick, 2017;Radlmayr, Gold, Lorenz, Farid, & Bengler, 2014;Telpaz, Rhindress, Zelman, & Tsimhoni, 2015;van der Meulen, Janssen, & Kun, 2016;Vogelpohl et al, 2018;Wan & Wu, 2018;Zeeb, Buchner, & Schrauf, 2015) up to the point when the AV system becomes deactivated by the user (most commonly by the execution of a driving action: movement of the wheel or pedals, or a button press). Sometimes a takeover request is not present, so some studies identify RTs as starting from the moment that the event which precipitates the handover is initiated (e.g., a parked car becomes visible or a crosswind begins; Johns, Mok, Talamonti, Sibi, & Ju, 2017;Larsson, Kircher, Hultgren, & Andersson, 2014;Louw, Markkula, Boer, Madigan, Carsten, & Merat, 2017;Shen & Neyens, 2017;Strand et al, 2014).…”

Section: Reaction Times When Responding During Transitionsmentioning

confidence: 99%

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

Mole

Lappi

Giles³

et al. 2019

Hum Factors

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Objective: To present a structured, narrative review highlighting research into human perceptual-motor coordination that can be applied to automated vehicle (AV)–human transitions. Background: Manual control of vehicles is made possible by the coordination of perceptual-motor behaviors (gaze and steering actions), where active feedback loops enable drivers to respond rapidly to ever-changing environments. AVs will change the nature of driving to periods of monitoring followed by the human driver taking over manual control. The impact of this change is currently poorly understood. Method: We outline an explanatory framework for understanding control transitions based on models of human steering control. This framework can be summarized as a perceptual-motor loop that requires (a) calibration and (b) gaze and steering coordination. A review of the current experimental literature on transitions is presented in the light of this framework. Results: The success of transitions are often measured using reaction times, however, the perceptual-motor mechanisms underpinning steering quality remain relatively unexplored. Conclusion: Modeling the coordination of gaze and steering and the calibration of perceptual-motor control will be crucial to ensure safe and successful transitions out of automated driving. Application: This conclusion poses a challenge for future research on AV-human transitions. Future studies need to provide an understanding of human behavior that will be sufficient to capture the essential characteristics of drivers reengaging control of their vehicle. The proposed framework can provide a guide for investigating specific components of human control of steering and potential routes to improving manual control recovery.

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Section: Reaction Times When Responding During Transitionsmentioning

confidence: 99%

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

Mole

Lappi

Giles³

et al. 2019

Hum Factors

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“…However, sub-second viewing times are probably too short for processing dynamic traffic scenes that require visual search by means of multiple fixations and saccades (see Rayner, 2009 for a review on eye movements and visual search). Lead times that are typically used in driving simulator research range between 2 s and 12 s (Gold et al, 2013;K€ orber et al, 2015Melcher et al, 2015;Mok et al, 2015;Samuel et al, 2016). In summary, our range of video lengths encompasses lead times that are commonly used, and range from extremely short (1 s) to longer than has been studied before (20 s).…”

Section: Introductionmentioning

confidence: 99%

How much time do drivers need to obtain situation awareness? A laboratory-based study of automated driving

2017

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Drivers of automated cars may occasionally need to take back manual control after a period of inattentiveness. At present, it is unknown how long it takes to build up situation awareness of a traffic situation. In this study, 34 participants were presented with animated video clips of traffic situations on a three-lane road, from an egocentric viewpoint on a monitor equipped with eye tracker. Each participant viewed 24 videos of different durations (1, 3, 7, 9, 12, or 20 s). After each video, participants reproduced the end of the video by placing cars in a top-down view, and indicated the relative speeds of the placed cars with respect to the ego-vehicle. Results showed that the longer the video length, the lower the absolute error of the number of placed cars, the lower the total distance error between the placed cars and actual cars, and the lower the geometric difference between the placed cars and the actual cars. These effects appeared to be saturated at video lengths of 7-12 s. The total speed error between placed and actual cars also reduced with video length, but showed no saturation up to 20 s. Glance frequencies to the mirrors decreased with observation time, which is consistent with the notion that participants first estimated the spatial pattern of cars after which they directed their attention to individual cars. In conclusion, observers are able to reproduce the layout of a situation quickly, but the assessment of relative speeds takes 20 s or more.

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“…After taking over, the drivers were required to brake or steer to avoid a collision. Scenario 3 (critical) was similar to scenario 2, but the drivers' visual field was blocked by a large SUV ahead-not until the SUV changed lanes was the obstacle in front visible, and then the system issued an urgent TOR ( Figure 3) [38]. In all three scenarios, the takeover request lead time (TORLt) was defined as 7 seconds, which was set according to previous studies [6], [22].…”

Section: Experiments Designmentioning

confidence: 99%

“…For critical level design of take-over scenarios, in most experiments, drivers avoid collisions by braking or changing lanes [6], [22], [23], [29], [35]- [37]. Some researchers even use a large vehicle to block obstacles in advance to increase the criticality of the scenario [38]- [40]. Although it is of utmost importance to know how quickly a driver can respond to a take-over request (TOR) and what the shortest TOR times are in emergencies, there is a paucity of research exploring the time it takes a driver to take-over control in normal, noncritical scenarios.…”

mentioning

confidence: 99%

Take-Over Performance and Safety Analysis Under Different Scenarios and Secondary Tasks in Conditionally Automated Driving

Wu¹,

Wu²,

Lyu³

et al. 2019

IEEE Access

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In conditionally automated driving, the driver is required to takeover control of a vehicle if a takeover request is issued due to possible system limitations. This study investigates the effect of roadway environments and secondary tasks on takeover performance and safety. The experiment was conducted in a real vehicle-based driving simulator. Participants experienced three different traffic scenarios, including a non-critical scenario and two critical scenarios. Manual driving, a 1-back cognitive secondary task and a letter game task were each tested in each scenario. Results indicate that different driving scenarios and secondary tasks impact takeover characteristics; in particular, they have a strong effect on takeover time and driver workload, which impacts takeover safety. Specifically, the steering reaction was generally slower than the braking reaction, indicating that lateral operation requires more cognitive and decision-making time. In extreme cases, braking operation alone was insufficient to ensure safety, and steering operation was required. When obstacles were difficult to detect, or when the driver was engaged in a visual secondary task, the steering reaction time increased significantly. This study provides data for takeover safety evaluations and best practices of conditionally automated driving. INDEX TERMS Automated driving, control transitions, takeover time, test scenarios, secondary tasks. I. INTRODUCTION Automated driving has developed rapidly in recent years. Following the introduction of automated driving systems by Google and Tesla, traditional automakers, such as BMW, Mercedes and Audi, have also released similar systems and carried out road tests [1]. However, there are still many problems that need to be solved for driver acceptance and driving safety [2]. According to a Google report, over 14 months of testing, its self-driving cars were 'out of automated status' 272 times, and there were 69 instances in which drivers chose to cancel the automated status [3]. This shows that the automated system still relies on human intervention. According to SAE's J3016 standard, most mass-produced automated vehicles are still under Level 3, i.e., conditionally automated driving [4]. This means that the system is only suitable for The associate editor coordinating the review of this manuscript and approving it for publication was Daxin Tian.

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Prediction of take-over time in highly automated driving by two psychometric tests

Cited by 31 publications

References 27 publications

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

How much time do drivers need to obtain situation awareness? A laboratory-based study of automated driving

Take-Over Performance and Safety Analysis Under Different Scenarios and Secondary Tasks in Conditionally Automated Driving

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