The Critical Tracking Task

Petzoldt, Tibor; Bellem, Hanna; Krems, Josef F.

doi:10.1177/0018720813501864

Cited by 13 publications

(8 citation statements)

References 44 publications

(55 reference statements)

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“…• SuRT. Mean response time and number of correct answers of the SuRT (Petzoldt et al, 2014). Lower response times and a higher number of correct answers were assumed to be indicative of a higher driver devotion to the secondary task.…”

Section: Dependent Measuresmentioning

confidence: 99%

Haptic Lane-Keeping Assistance for Truck Driving: A Test Track Study

et al. 2020

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Objective This study aims to compare the effectiveness and subjective acceptance of three designs for haptic lane-keeping assistance in truck driving. Background Haptic lane-keeping assistance provides steering torques toward a reference trajectory, either continuously or only when exceeding a bandwidth. These approaches have been previously investigated in driving simulators, but it is unclear how these generalize toward real-life truck driving. Method Three haptic lane-keeping algorithms to assist truck drivers were evaluated on a 6.3-km-long oval-shaped test track: (1) a single-bandwidth (SB) algorithm, which activated assistance torques when the predicted lateral deviation from lane center exceeded 0.4 m; (2) a double-bandwidth (DB) algorithm, which activated as SB, but deactivated after returning within 0.15 m lateral deviation; and (3) an algorithm providing assistance torques continuously (Cont) toward the lane center. Fifteen participants drove four trials each, one trial without and one for each haptic assistance design. Furthermore, participants drove with and without a concurrent visually distracting task. Results Compared to unsupported driving, all three assistance systems provided similar safety benefits in terms of decreased absolute lateral position and number of lane departures. Participants reported higher satisfaction and usability for Cont compared to SB. Conclusion The continuous assistance was better accepted than bandwidth assistance, a finding consistent with prior driving simulator research. Research is still needed to investigate the long-term effects of haptic assistance on reliance and after-effects. Application The present results are useful for designers of haptic lane-keeping assistance, as driver acceptance and performance are determinants of reliance and safety, respectively.

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Section: Dependent Measuresmentioning

confidence: 99%

Haptic Lane-Keeping Assistance for Truck Driving: A Test Track Study

et al. 2020

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“…We used a within-subject design in this experiment. Each participant conducted ten trials of 90 seconds each, since this duration has shown to provide sufficient information for meaningful CTT calculations without overloading participants (Petzoldt et al, 2014). Table 4.1 illustrates all trials, which differ in the tasks used, the modalities used, and the sequence of tasks.…”

Section: Methodsmentioning

confidence: 99%

“…The task poses similar demands as to basic driving skills such as steering, accelerating and braking, which reflect the operational level of driving (Petzoldt, Bellem, & Krems, 2014). Just like those skills, the CTT requires a sufficient amount of attention in order to be operated properly.…”

Section: Critical Tracking Taskmentioning

confidence: 99%

“…It allows to assess differences between visual and cognitive distraction, and differentiates between IVISs of varying (visual) demand (Petzoldt et al, 2014). However, due to the abstraction of the task, results will not be able to give a direct estimation of real world distraction, but they can represent relative patterns that should be similar in all experimental settings (Petzoldt et al, 2014). Overall, they conclude that the CTT can serve as a valid method to assess driver distraction.…”

Section: Critical Tracking Taskmentioning

confidence: 99%

“…While driving, maneuvering the car is the primary task; other activities such as interacting with in-vehicle systems are secondary tasks (W. W. . We used the Critical Tracking Task (Petzoldt et al, 2014) as primary task, which abstracts from the primary driving task. In addition, we used two specific secondary tasks to be performed parallel to the primary task.…”

Section: Experimental Tasksmentioning

confidence: 99%

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Natural Multimodal Interaction in the Car - Generating Design Support for Speech, Gesture, and Gaze Interaction while Driving

Roider¹

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Driving a modern car is more than just maneuvering the vehicle on the road. At the same time, drivers want to listen to music, operate the navigation system, compose, and read messages and more. Future cars are turning from simple means for transportation into smart devices on wheels. This trend will continue in the next years together with the advent of automated vehicles. However, technical challenges, legal regulations, and high costs slow down the penetration of automated vehicles. For this reason, a great majority of people will still be driving manually at least for the next decade. Consequently, it must be ensured that all the features of novel infotainment systems can be used easily, efficiently without distracting the driver from the task of driving and still provide a high user experience. A promising approach to cope with this challenge is multimodal in-car interaction. Multimodal interaction basically describes the combination of different input and output modalities for driver-vehicle interaction. Research has pointed out the potential to create a more flexible, efficient, and robust interaction. In addition to that, the integration of natural interaction modalities such as speech, gestures and gaze, the communication with the car could increase the naturalness of the interaction. Based on these advantages, the researcher community in the field of automotive user interfaces has produced several interesting concepts for multimodal interaction in vehicles. The problem is that the resulting insights and recommendations are often easily applicable in the design process of other concepts because they too concrete or very abstract. At the same time, concepts focus on different aspects. Some aim to reduce distraction while others want to increase efficiency or provide a better user experience. This makes it difficult to give overarching recommendations on how to combine natural input modalities while driving. As a consequence, interaction designers of in-vehicle systems are lacking adequate design support that enables them to transfer existing knowledge about the design of multimodal in-vehicle applications to their own concepts. This thesis addresses this gap by providing empirically validated design support for multimodal in-vehicle applications. It starts with a review of existing design support for automotive and multimodal applications. Based on that we report a series of user experiments that investigate various aspects of multimodal in-vehicle interaction with more than 200 participants in lab setups and driving simulators. During these experiments, we assessed the potentials of multimodality while driving, explored how user interfaces can support speech and gestures, and evaluated novel interaction techniques. The insights from these experiments extend existing knowledge from literature in order to create the first pattern collection for multimodal natural in-vehicle interaction. The collection contains 15 patterns that describe solutions for reoccurring problems when combining natural input with speech, gestures, or gaze in the car in a structured way. Finally, we present a prototype of an in-vehicle information system, which demonstrates the application of the proposed patterns and evaluate it in a driving-simulator experiment. This work contributes to field of automotive user interfaces in three ways. First, it presents the first pattern collection for multimodal natural in-vehicle interaction. Second, it illustrates and evaluates interaction techniques that combine speech and gestures with gaze input. Third, it provides empirical results of a series of user experiments that show the effects of multimodal natural interaction on different factors such as driving performance, glance behavior, interaction efficiency, and user experience.

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