Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving

Zeeb, Kathrin; Buchner, Axel; Schrauf, Michael

doi:10.1016/j.aap.2016.04.002

Cited by 330 publications

(212 citation statements)

References 24 publications

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“…Conversely, on grippy, dry roads (µ = 0.8 and µ = 1.0), this difference is not very significant (587 versus 545 and 613 versus 498, respectively), implying that the PID model provides good enough steering of the car in these road conditions. Algorithms 2018, 11, x FOR PEER REVIEW 12 of 17 The lack of generality is one of the well-documented drawbacks of solutions obtained via GP [4,5,26] that hinders the applicability of this algorithm to real-world problems. Indeed, we could not be sure about how well the SAF that was evolved in a single car driven at a fixed speed on a fixed track featuring a fixed coefficient of friction would perform in different situation(s).…”

Section: Resultsmentioning

confidence: 99%

“…Ultimately, we should have considered an evolving SAF that performs (nearly) equally well on several fitness cases that correspond to these different conditions. Moreover, in order to bridge the inevitable reality gap, we should have implemented an evolutionary adaptation of a set of the best SAFs, evolved on the The lack of generality is one of the well-documented drawbacks of solutions obtained via GP [4,5,26] that hinders the applicability of this algorithm to real-world problems. Indeed, we could not be sure about how well the SAF that was evolved in a single car driven at a fixed speed on a fixed track featuring a fixed coefficient of friction would perform in different situation(s).…”

Section: Resultsmentioning

confidence: 99%

“…The proposed approach of employing GP implies that the SAF is automatically designed-without an a priori defined structure nor complexity-by a computer system via simulated evolution through selection and survival of the fittest in a way similar to the evolution of species in nature [4,5,29]. Adopting GP offers the following advantages:…”

Section: Evolving the Steering Controller Via Gpmentioning

confidence: 99%

“…Indeed, in a case of an unforeseen traffic situation or road condition, the control might have to be transferred to the presumably more dependable human driver [4]. We believe that the automation should abstain from unconditionally ceding the control of the car to the human driver, especially in heavy traffic situations or in challenging (such as, for example, slippery) road conditions due to the major drawback of this transfer: the reduced cognitive load of the (passive) human driver might result in a rough transition of control between the automation and the human [5]. In such situations, the behavior of the human might be either inadequate or too slow as they might be subjected to a sharp increase of their cognitive load and the psychological stress of dealing with the suddenly arising, challenging traffic situation (e.g., a suddenly appearing obstacle in front of the car in dense multi-lane traffic).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Алексеева¹,

Tanev²,

Shimohara³

2018

Algorithms

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Abstract:The most important characteristics of autonomous vehicles are their safety and their ability to adapt to various traffic situations and road conditions. In our research, we focused on the development of controllers for automated steering of a realistically simulated car in slippery road conditions. We comparatively investigated three implementations of such controllers: a proportional-derivative (PD) controller built in accordance with the canonical servo-control model of steering, a PID controller as an extension of the servo-control, and a controller designed heuristically via the most versatile evolutionary computing paradigm: genetic programming (GP). The experimental results suggest that the controller evolved via GP offers the best quality of control of the car in all of the tested slippery (rainy, snowy, and icy) road conditions.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Evolving the Steering Controller Via Gpmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Алексеева¹,

Tanev²,

Shimohara³

2018

Algorithms

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show abstract

“…[15]). Successful human-automation cooperation requires fast and effective communication of the need for manual intervention in these cases (e.g., [16][17][18][19][20][21][22][23]). Therefore, getting the driver back into the loop as fast as possible has been the focus of a large body of research (see [24], for an overview), as this function of the in-vehicle HMI can be viewed as a key element for the safety of automated vehicles.…”

Section: 2mentioning

confidence: 99%

Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

Naujoks

Forster

Wiedemann

et al. 2017

Journal of Advanced Transportation

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During conditionally automated driving (CAD), driving time can be used for non-driving-related tasks (NDRTs). To increase safety and comfort of an automated ride, upcoming automated manoeuvres such as lane changes or speed adaptations may be communicated to the driver. However, as the driver's primary task consists of performing NDRTs, they might prefer to be informed in a nondistracting way. In this paper, the potential of using speech output to improve human-automation interaction is explored. A sample of 17 participants completed different situations which involved communication between the automation and the driver in a motion-based driving simulator. The Human-Machine Interface (HMI) of the automated driving system consisted of a visualauditory HMI with either generic auditory feedback (i.e., standard information tones) or additional speech output. The drivers were asked to perform a common NDRT during the drive. Compared to generic auditory output, communicating upcoming automated manoeuvres additionally by speech led to a decrease in self-reported visual workload and decreased monitoring of the visual HMI. However, interruptions of the NDRT were not affected by additional speech output. Participants clearly favoured the HMI with additional speech-based output, demonstrating the potential of speech to enhance usefulness and acceptance of automated vehicles.

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Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

Niu

Sun

et al. 2020

Hum Ftrs & Erg Mfg Svc

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A high degree of automated driving distracts drivers more easily, resulting in slow recognition of critical events during driving and slow responses to emergencies. Automated driving and manual switching processes are also prone to erroneous decisions. We conducted a simulated automated driving experiment to study participants' visual perception characteristics during the takeover of vehicle control. The present study used dynamic videos to imitate the driving situations when drivers returned their gaze from the distractive source to the road. We collected the drivers' eye movement data to analyze the search strategy and physiological characteristics of the drivers after the takeover reminder. The results showed that the instant information search method of the drivers was scanning of the driving scene. When the degree of distraction deepened and the hazard level of scenes increased, the pupil diameter of the drivers increased and the fixation duration became longer. These findings can help to design takeover warnings and support more intelligent automated driving systems to judge whether measures should be taken to interfere with the driver's operation to avoid collisions. Furthermore, the drivers' fixation point distribution focused on the left side and the lower side of the scene. We suggest that the takeover warning is displayed in the head-up display. This study provides a better understanding of drivers' visual perception characteristics when drivers' eyesight returns from other distractors to the driving scene and a good theoretical basis for the design of hazard warning information for automated driving.

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Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving

Cited by 330 publications

References 24 publications

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Evolving the Controller of Automated Steering of a Car in Slippery Road Conditions

Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

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