Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants

Petermeijer, Sebastiaan M.; Doubek, Fabian; Winter, Joost de

doi:10.1109/smc.2017.8122827

Cited by 74 publications

(47 citation statements)

References 13 publications

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“…A driving simulator study by Petermeijer et al [19,36] showed that drivers become quicker and more fluent in taking over control with increasing takeover experience. Payre et al [48] recommended that drivers should be trained in how to operate their automated driving system.…”

Section: Discussionmentioning

confidence: 99%

“…Activation was confirmed by the grey steering wheel icon (automation available) turning green (automation active) (see Figure 2). e participants performed an audiovisual nondriving task in the form of watching a comedy television series (see also [36]). e video automatically started playing on the 10.9-inch centre-display when the automated driving function was enabled.…”

Section: Human-machine Interfacementioning

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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Section: Discussionmentioning

confidence: 99%

Section: Human-machine Interfacementioning

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

Self Cite

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“…Due to the small sample size the result was not statistically significant, even though the difference in average reaction times was close to 0.5 seconds. Most previous studies [17,35,39,50] have suggested that visual TORs take more time to respond than auditory or haptic ones, especially if the driver performs a secondary task. In our study, this was not the case.…”

Section: Discussionmentioning

confidence: 99%

“…Previous studies show that on average, engaging any kind of secondary task significantly slows down take-over times [13] and that tasks requiring visual attention result in significant amounts of driving errors [49]. In addition, visual TOR may not evoke as fast reactions as auditory, haptic, or multimodal ones [4,35,39,50]. For example, in a previous study [38] visual text-based TOR took on average 6.9 seconds to be noticed while auditory, tactile, and multimodal TOR required on average less than 3 seconds to be noticed.…”

Section: Introductionmentioning

confidence: 99%

Unimodal and Multimodal Signals to Support Control Transitions in Semiautonomous Vehicles

Salminen

Farooq

Rantala

et al. 2019

Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

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Semiautonomous driving still requires the driver's control and attention in certain situations. Especially control transitions, i.e. takeover and handover situations, are important for safety. Our aim was to study control transitions supported by unimodal (i.e. visual, auditory, or haptic) or multimodal (i.e. visual, auditory and haptic) signals indicating change from manual to autonomous driving and vice versa. The signals were abstract visual blinks, auditory beeps, or haptic vibrations. The task was to take over driving while either looking through the windshield or playing a game. In addition, in half of the control transitions a feedback signal indicated successful control transition. The results showed that a secondary task slowed down the reaction times, but there was a great variation between individuals. In general, the response to auditory signal was slower than to visual, haptic, or multimodal signals. Moreover, users preferred feedback during control transitions but this slowed down the reaction time.

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“…As a visual HMI element, the instrument cluster, in particular, is considered the primary interaction element for communicating the system state and further detailed information in automated driving [18]. The instrument cluster is used in almost all studies on automated driving (e.g., [8,[19][20][21][22]). In addition, head-up displays have become established that can inform passengers in the primary field of view about system status, emerging situations, and maneuvers without eyes off road (e.g., [8,19,[23][24][25]).…”

Section: Automation Hmi (Ahmi)mentioning

confidence: 99%

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

et al. 2020

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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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Driver response times to auditory, visual, and tactile take-over requests: A simulator study with 101 participants

Cited by 74 publications

References 13 publications

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

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

Unimodal and Multimodal Signals to Support Control Transitions in Semiautonomous Vehicles

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

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