Take-Overs in Level 3 Automated Driving – Proposal of the Take-Over Performance Score (TOPS)

Radlmayr, Jonas; Ratter, Madeleine; Feldhütter, Anna; Körber, Moritz; Prasch, Lorenz; Schmidtler, Jonas; Yang, Yucheng; Bengler, Klaus

doi:10.1007/978-3-319-96074-6_46

Cited by 8 publications

(7 citation statements)

References 17 publications

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“…Highly automated driving studies have used several performance measures for vehicle handling during take-over scenarios (Radlmayr et al, 2019). Several studies identified time to collision (C. Körber et al, 2016;Radlmayr et al, 2014), longitudinal acceleration (C. Radlmayr et al, 2014), braking (Körber et al, 2016;Larsson et al, 2014), minimum speed (Larsson et al, 2014) and occurred collisions (Radlmayr et al, 2014;Wandtner et al, 2018).…”

Section: Performance Measures Of Driver's Responsesmentioning

confidence: 99%

In a heart beat: Using driver’s physiological changes to determine the quality of a takeover in highly automated vehicles

Alrefaie

Summerskill

Jackon

2019

Accident Analysis & Prevention

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Developing conditionally automated driving systems is on the rise. Vehicles with full longitudinal and latitudinal control will allow drivers to engage in secondary tasks without monitoring the roadway, but users may be required to resume vehicle control to handle critical hazards. The loss of driver's situational awareness increases the potential for accidents. Thus, the automated systems need to estimate the driver's ability to resume control of the driving task. The aim of this study was to assess the physiological behaviour (heart rate and pupil diameter) of drivers. The assessment was performed during two naturalistic secondary tasks. The tasks were the email and the twenty questions task in addition to a control group that did not perform any tasks. The study aimed at finding possible correlations between the driver's physiological data and their responses to a takeover request. A driving simulator study was used to collect data from a total of 33 participants in a repeated measures design to examine the physiological changes during driving and to measure their takeover quality and response time. Secondary tasks induced changes on physiological measures and a small influence on response time. However, there was a strong observed correlation between the physiological measures and response time. Takeover quality in this study was assessed using two new performance measures called PerSpeed and PerAngle. They are identified as the mean percentage change of vehicle's speed and heading angle starting from a takeover request time. Using linear mixed models, there was a strong interaction between task, heart rate and pupil diameter and PerSpeed, PerAngle and response time. This, in turn, provided a measurable understanding of a driver's future responses to the automated system based on the driver's physiological changes to allow better decision making. The present findings of this study emphasised the possibility of building a driver mental state model and prediction system to determine the quality of the driver's responses in a highly automated vehicle. Such results will reduce accidents and enhance the driver's experience in highly automated vehicles.

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Section: Performance Measures Of Driver's Responsesmentioning

confidence: 99%

In a heart beat: Using driver’s physiological changes to determine the quality of a takeover in highly automated vehicles

Alrefaie

Summerskill

Jackon

2019

Accident Analysis & Prevention

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“…Similarly, the Takeover Performance Score (TOPS) combines three variables related to vehicle guidance, mental processing, and subjective rating. ese three variables can be combined into a single quality score [10].…”

Section: Discussionmentioning

confidence: 99%

“…We computed a variety of takeover measures that have been used in the literature before (e.g., [19,38,39]), as shown in Table 2. Four subjective dependent variables were included as well, based on Radlmayr et al [10]. After each takeover, participants were asked to rate the scenario, on a 7-point scale rating for criticality (very noncritical-very critical), complexity (very uncomplicated-very complicated), discomfort (very comfortablevery uncomfortable), and subjective time budget (more than enough-way too little).…”

Section: Dependent Variablesmentioning

confidence: 99%

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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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“…However, the monitor in the center console has also been used as part of the aHMI [19]. Although semantics have been used in auditory aHMI (e.g., [25,33]), the aHMI design has been characterized by warning tones that have been used in the case of a request to intervene (e.g., [30,[34][35][36][37]) due to their fast perception. Haptic-tactile warnings during a request to intervene in automated driving, so far, have been implemented primarily by vibration of the driver's seat (e.g., [33,38]).…”

Section: Automation Hmi (Ahmi)mentioning

confidence: 99%

From HMI to HMIs: Towards an HMI Framework for Automated Driving

et al. 2020

Self Cite

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During automated driving, there is a need for interaction between the automated vehicle (AV) and the passengers inside the vehicle and between the AV and the surrounding road users outside of the car. For this purpose, different types of human machine interfaces (HMIs) are implemented. This paper introduces an HMI framework and describes the different HMI types and the factors influencing their selection and content. The relationship between these HMI types and their influencing factors is also presented in the framework. Moreover, the interrelations of the HMI types are analyzed. Furthermore, we describe how the framework can be used in academia and industry to coordinate research and development activities. With the help of the HMI framework, we identify research gaps in the field of HMI for automated driving to be explored in the future.

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Take-Overs in Level 3 Automated Driving – Proposal of the Take-Over Performance Score (TOPS)

Cited by 8 publications

References 17 publications

In a heart beat: Using driver’s physiological changes to determine the quality of a takeover in highly automated vehicles

In a heart beat: Using driver’s physiological changes to determine the quality of a takeover in highly automated vehicles

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

From HMI to HMIs: Towards an HMI Framework for Automated Driving

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