Automated Driving using Shared and Cooperative Guidance and Control: Revisiting past Research with a new Model

Altendorf, Eugen; Baltzer, Marcel; Canpolat, Yigiterkut; López, Daniel; Schreck, Constanze; Weßel, Gina; Flemisch, Frank

doi:10.1016/j.ifacol.2016.10.480

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…ey also provided a basic utility formula that they believed is reasonable. To allow the analysis of the behavior of the human-machine system, they considered that the utility function is influenced by the vehicle's own position, speed, and the position of obstacles and other vehicles [31].…”

Section: Utility Functionmentioning

confidence: 99%

Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles Testing

Zhang

et al. 2022

Journal of Advanced Transportation

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Virtual simulation-based testing of autonomous vehicles (AVs) needs massive challenging corner cases to reach high testing accuracy. Current methods achieve this goal by finding testing scenarios with low sampling frequency in the empirical distribution. However, these methods neglect modeling heterogeneous driving behavior, which actually is crucial for finding corner cases. To fill this gap, we propose an interpretable and operable method for sampling corner cases. Firstly, we initialize a testing scenario and allocate testing tasks to AV. Then, to simulate the variability in driving behaviors, we design utility functions with several hyperparameters and generate aggressive, conservative, and normal driving strategies by adjusting hyperparameters. By changing the heterogeneous driving behavior of surrounding vehicles (SVs), we can sample the challenging corner cases in the scenario. Finally, we conduct a series of simulation experiments in a typical lane-changing scenario. The simulation results reveal that by adjusting the occurrence frequency of heterogeneous SVs in the testing scenario, more corner cases can be found in limited rounds of simulations.

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Section: Utility Functionmentioning

confidence: 99%

Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles Testing

Zhang

et al. 2022

Journal of Advanced Transportation

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“…Parasuraman et al [5] promoted human-centered automation and proposed a hierarchy of different levels of human involvement in the decision process, ranging from choosing between different options proposed by the system to vetoing a decision of the system or not being involved at all. In the automotive area, relevant work has been done by Flemish and colleagues [6][7][8][9]. This work is mostly focused on investigating how to complement human and system ability and to ascertain graceful transitions.…”

Section: Related Workmentioning

confidence: 99%

Toward Shared Control Between Automated Vehicles and Users

Terken

Pfleging

2020

Automot. Innov.

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Technological developments in the domain of vehicle automation are targeted toward driver-less, or driver-out-of-the-loop driving. The main societal motivation for this ambition is that the majority of (fatal) accidents with manually driven vehicles are due to human error. However, when interacting with technology, users often experience the need to customize the technology to their personal preferences. This paper considers how this might apply to vehicle automation, by a conceptual analysis of relevant use cases. The analysis proceeds by comparing how handling of relevant situations is likely to differ between manual driving and automated driving. The results of the analysis indicate that full out-of-the-loop automated driving may not be acceptable to users of the technology. It is concluded that a technology that allows shared control between the vehicle and the user should be pursued. Furthermore, implications of this view are explored for the concrete temporal dynamics of shared control, and general characteristics of human machine interface that support shared control are proposed. Finally, implications of the proposed view and directions for further research are discussed.

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Shared control is the sharp end of cooperation: Towards a common framework of joint action, shared control and human machine cooperation

et al. 2016

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As an introduction to the session of shared and cooperative control, this article will briefly look into the history, start with definitions and sketch a common framework of shared and cooperative control that sees the two phrases not as different concepts, but as different perspectives or foci on a common design space of shared intentionality, control and cooperation between humans and machines. One working hypothesis which the session will explore is that shared control can be understood as cooperation at the control level, while human machine cooperation can include shared control, but can also extend towards cooperation at higher levels, e.g. of guidance and navigation, of maneuvers and goals. We propose to view the relationship between shared control and human-machine cooperation as being similar to the relationship between the sharp, pointy tip and the (blunt) shaft of a spear. Shared control is where cooperation comes sharply into effect at the control level, but to be truly effective it should be supported by cooperation on all levels of interaction beyond the control level, e.g. on the guidance and navigation level

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Automated Driving using Shared and Cooperative Guidance and Control: Revisiting past Research with a new Model

Cited by 6 publications

References 4 publications

Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles Testing

Heterogeneous Driver Modeling and Corner Scenarios Sampling for Automated Vehicles Testing

Toward Shared Control Between Automated Vehicles and Users

Shared control is the sharp end of cooperation: Towards a common framework of joint action, shared control and human machine cooperation

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