Quality of control takeover following disengagements in semi-automated vehicles

Favaró, Francesca; Seewald, Philipp; Scholtes, Maike; Eurich, Sky O.

doi:10.1016/j.trf.2019.05.004

Cited by 20 publications

(22 citation statements)

References 15 publications

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“…Other works assessing take-over maneuvers can be found in [110] where different drivers are evaluated during a failure of the autonomous system, and in [111] where truck drivers face time critical take-over situations while performing a nondriving tasks. In [112], the authors evaluate how scheduled manual driving affects drowsiness and contribute to better take-over maneuvers when needed.…”

Section: B Autonomous Systems Developmentmentioning

confidence: 99%

A Review of Driving Simulation Technology and Applications

Bruck

Haycock

Emadi

2021

IEEE Open J. Veh. Technol.

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Driving simulation has become a very useful tool for vehicle design and research in industry and educational institutes. This paper provides a review of driving simulator components, including the vehicle dynamics model, the motion system, and the virtual environment, and how they interact with the human perceptual system in order to create the illusion of the driving. In addition, a sample of current state-of-the-art vehicle simulators and algorithms are described. Finally, current applications are discussed, such as driver-centered studies, chassis and powertrain design, and autonomous systems development.

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Section: B Autonomous Systems Developmentmentioning

confidence: 99%

A Review of Driving Simulation Technology and Applications

Bruck

Haycock

Emadi

2021

IEEE Open J. Veh. Technol.

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“…Studies measuring the quality of takeover [10][11][12] used indicators such as TTC, steering wheel, and vehicle position in the lane. It was thought that TTC would be difficult to use when the distance from the vehicle in front is wide in a low traffic volume condition, such as LOS A. e vehicle position in the lane was excluded because the driver thought that it would be more difficult for the driver to determine his vehicle position than when driving the actual vehicle due to the characteristics of the simulator.…”

Section: No Level Of Service Curvaturementioning

confidence: 99%

Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

Park

Kim

et al. 2021

Journal of Advanced Transportation

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This study investigates the impacts of road traffic conditions and driver’s characteristics on the takeover time in automated vehicles using a driving simulator. Automated vehicles are barely expected to maintain their fully automated driving capability at all times based on the current technologies, and the automated vehicle system transfers the vehicle control to a driver when the system can no longer be automatically operated. The takeover time is the duration from when the driver requested the vehicle control transition from the automated vehicle system to when the driver takes full control of the vehicle. This study assumes that the takeover time can vary according to the driver’s characteristics and the road traffic conditions; the assessment is undertaken with various participants having different characteristics in various traffic volume conditions and road geometry conditions. To this end, 25 km of the northbound road section between Osan Interchange and Dongtan Junction on Gyeongbu Expressway in Korea is modeled in the driving simulator; the experiment participants are asked to drive the vehicle and take a response following a certain triggering event in the virtual driving environment. The results showed that the level of service and road curvature do not affect the takeover time itself, but they significantly affect the stabilization time, that is, a duration for a driver to become stable and recover to a normal state. Furthermore, age affected the takeover time, indicating that aged drivers are likely to slowly respond to a certain takeover situation, compared to the younger drivers. With these findings, this study emphasizes the importance of having effective countermeasures and driver interface to monitor drivers in the automated vehicle system; therefore, an early and effective alarm system to alert drivers for the vehicle takeover can secure enough time for stable recovery to manual driving and ultimately to achieve safety during the takeover.

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“…For this purpose we refer to the work presented by Eboli et al [6], where by combining vehicle speed, and longitudinal and lateral accelerations, they classify any given driving moment as safe or unsafe according to the dynamic equilibrium of the vehicle. In particular, taking the value of the acceleration vector (ā) lying on the plane of the vehicle motion and its speed (V), the authors split out the plane (V,ā) into two areas representing safe and unsafe driving domains according to the Equation (1).…”

Section: Labeling the Driving Behaviormentioning

confidence: 99%

“…Acceleration values that exceed |ā max | are unsafe points because in these conditions the car tires are unable to sustain the forces generated and there is a loss of grip with possible car skidding. Using the vehicle speed obtained by the OBD-II signals and a lat and a lon from the smartphone accelerometer, each time window of collected tracks has been marked with a binary label generated by means of Equation (1). Labeled records have then been used to train the classifiers and to evaluate the resulting performances.…”

Section: Labeling the Driving Behaviormentioning

confidence: 99%

See 1 more Smart Citation

Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion

Lattanzi¹,

Castellucci²,

Freschi³

2020

Applied Sciences

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Most road accidents occur due to human fatigue, inattention, or drowsiness. Recently, machine learning technology has been successfully applied to identifying driving styles and recognizing unsafe behaviors starting from in-vehicle sensors signals such as vehicle and engine speed, throttle position, and engine load. In this work, we investigated the fusion of different external sensors, such as a gyroscope and a magnetometer, with in-vehicle sensors, to increase machine learning identification of unsafe driver behavior. Starting from those signals, we computed a set of features capable to accurately describe the behavior of the driver. A support vector machine and an artificial neural network were then trained and tested using several features calculated over more than 200 km of travel. The ground truth used to evaluate classification performances was obtained by means of an objective methodology based on the relationship between speed, and lateral and longitudinal acceleration of the vehicle. The classification results showed an average accuracy of about 88% using the SVM classifier and of about 90% using the neural network demonstrating the potential capability of the proposed methodology to identify unsafe driver behaviors.

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Quality of control takeover following disengagements in semi-automated vehicles

Cited by 20 publications

References 15 publications

A Review of Driving Simulation Technology and Applications

A Review of Driving Simulation Technology and Applications

Investigating the Impacts of Road Traffic Conditions and Driver’s Characteristics on Automated Vehicle Takeover Time and Quality Using a Driving Simulator

Improving Machine Learning Identification of Unsafe Driver Behavior by Means of Sensor Fusion

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