Influence of automated driving on driver’s own localization: a driving simulator study

Umeno, Ryuichi; Itoh, Makoto; Kitazaki, Satoshi

doi:10.1108/jicv-08-2018-0006

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…An untrusted and not user-friendly system may lead to improper control transitions, e.g. early hand over before requested [12].…”

Section: Introductionmentioning

confidence: 99%

Developing and evaluating an human-automation shared control takeover strategy based on Human-in-the-loop driving simulation

Zhou¹,

Chai²,

Yin³

et al. 2021

Preprint

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The purpose of this paper is to develop a shared control takeover strategy for smooth and safety control transition from an automation driving system to the human driver and to approve its positive impacts on drivers' behavior and attitudes. A "human-in-the-loop" driving simulator experiment was conducted to evaluate the impact of the proposed shared control takeover strategy under different disengagement conditions. Results of thirty-two drivers showed shared control takeover strategy could improve safety performance at the aggregated level, especially at non-driving related disengagements. For more urgent disengagements caused by another vehicle's sudden brake, a shared control strategy enlarges individual differences. The primary reason is that some drivers had higher self-reported mental workloads in response to the shared control takeover strategy. Therefore, shared control between driver and automation can involve driver's training to avoid mental overload when developing takeover strategies.

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“…An untrusted and not user-friendly system may lead to improper control transitions, e.g. early hand over before requested [12].…”

Section: Introductionmentioning

confidence: 99%

Developing and evaluating an human-automation shared control takeover strategy based on Human-in-the-loop driving simulation

Zhou¹,

Chai²,

Yin³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The advent of automated vehicles eventually led to the consideration of relegating steering to the vehicle for the benefit of persons with disabilities, although vulnerability of vehicle computers to hackers, along with the high cost and susceptibility to severe weather conditions of advanced automation sensors, such as lidar, can compromise the safety of drivers, passengers, and other people in the vehicle environment [3][4][5]. An additional problem with vehicle automation is the possibility of accidents resulting from inappropriate driver input when transitioning from automated driving to manual mode during unexpected scenarios, such as road construction or suddenly approaching road obstacles [6][7][8]. Furthermore, many current automobiles rely primarily on conventional steering wheel interfaces.…”

Section: Introductionmentioning

confidence: 99%

Surface Electromyography-Controlled Automobile Steering Assistance

Nacpil

Nakano

2020

Sensors

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Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMG-controlled steering assistance by validating the Myo armband, a mass-produced sEMG-based interface, with respect to the path-following accuracy of a commercially available automobile. It was hypothesized that one-handed remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of low-speed field testing indicate that the Myo armband had lower path-following accuracy than the steering wheel during a 90° turn and wide U-turn at twice the minimum turning radius, the Myo armband had superior path-following accuracy for a narrow U-turn at the minimum turning radius and a 45° turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies.

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“…Scheltes et al ( 26 ) proposed an agent-based Automated Last-Mile Transport simulation model to evaluate the effectiveness of a fleet of small, fully automated electric vehicles in solving the last-mile problem of public transport travel. Umeno et al ( 27 ) used a driving simulator to study the impact of automated driving on the driver’s ability to operate during the take-over process. The experimental results suggested that it is not enough to issue a TOR before the take-over performance, and the driver should be told via the HMI what situation they are in and what they should do next.…”

mentioning

confidence: 99%

Impact of Secondary Task Immersion Duration on Take-Over Process of Conditional (L3) Automated Vehicles in an Off-Ramp Area

Zhang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Exploring the influence of secondary task immersion duration on the take-over process is an important task in the research on conditional automated driving (SAE, L3). In this paper, with the tool of driving simulation, two different secondary task types and take-over request (TOR) times were designed for the ramp area take-over scenario. Forty-two participants were recruited to conduct driving simulation experiments. Based on the K-means algorithm, different immersion durations of secondary tasks were clustered. Three driving indicators, namely speed, lateral offset, and accelerator pedal depth, were selected to characterize the take-over performance. Then the influence of immersion duration on the take-over response time and take-over performance was analyzed. The effect of different immersion durations on the section traffic operation during the take-over process was then investigated with the help of VISSIM software. The results showed that the TOR time had no significant effect on the take-over response time, and the take-over response time was positively correlated with the immersion duration. At the beginning of the take-over process, the take-over performance of drivers between different immersion durations was different, for example, the average speed of the vehicle is lower when the immersion duration is low. In the take-over process, the driver’s take-over speed and accelerator pedal operation intensity are negatively correlated with take-over urgency. The immersion duration of secondary tasks has different degrees of influence on the efficiency of section traffic flow. With the improvement of immersion duration level, the average vehicle delay on the road section also increases to a certain extent.

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Influence of automated driving on driver’s own localization: a driving simulator study

Cited by 8 publications

References 20 publications

Developing and evaluating an human-automation shared control takeover strategy based on Human-in-the-loop driving simulation

Developing and evaluating an human-automation shared control takeover strategy based on Human-in-the-loop driving simulation

Surface Electromyography-Controlled Automobile Steering Assistance

Impact of Secondary Task Immersion Duration on Take-Over Process of Conditional (L3) Automated Vehicles in an Off-Ramp Area

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