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Integrating Neurophysiological Sensors and Driver Models for Safe and Performant Automated Vehicle Control in Mixed Traffic
In future mixed traffic Highly Automated Vehicles (HAV) will have to res...
02/13/2019 ∙ by Werner Damm, et al. ∙
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Are Automated Vehicles Safer than Manually Driven Cars?
Are automated vehicles really safer than manually driven vehicles? If so...
05/16/2019 ∙ by Lionel Peter Robert Jr, et al. ∙
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A Maneuver-based Urban Driving Dataset and Model for Cooperative Vehicle Applications
Short-term future of automated driving can be imagined as a hybrid scena...
05/28/2020 ∙ by Behrad Toghi, et al. ∙
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On the Application of ISO 26262 in Control Design for Automated Vehicles
Research on automated vehicles has experienced an explosive growth over ...
04/12/2018 ∙ by Georg Schildbach, et al. ∙
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Implicit Cooperation: Emotion Detection for Validation and Adaptation of Automated Vehicles' Driving Behavior
Human emotion detection in automated vehicles helps to improve comfort a...
03/29/2020 ∙ by Henrik Detjen, et al. ∙
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Teaching Vehicles to Anticipate: A Systematic Study on Probabilistic Behavior Prediction using Large Data Sets
Observations of traffic participants and their environment enable humans...
10/17/2019 ∙ by Florian Wirthmüller, et al. ∙
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Challenges in Architecting Fully Automated Driving; with an Emphasis on Heavy Commercial Vehicles
Fully automated vehicles will require new functionalities for perception...
12/04/2019 ∙ by Naveen Mohan, et al. ∙
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Developing Robot Driver Etiquette Based on Naturalistic Human Driving Behavior
08/01/2018 ∙ by Xianan Huang, et al. ∙
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Automated vehicles can change the society by improved safety, mobility and fuel efficiency. However, due to the higher cost and change in business model, over the coming decades, the highly automated vehicles likely will continue to interact with many human-driven vehicles. In the past, the control/design of the highly automated (robotic) vehicles mainly considers safety and efficiency but failed to address the "driving culture" of surrounding human-driven vehicles. Thus, the robotic vehicles may demonstrate behaviors very different from other vehicles. We study this "driving etiquette" problem in this paper. As the first step, we report the key behavior parameters of human driven vehicles derived from a large naturalistic driving database. The results can be used to guide future algorithm design of highly automated vehicles or to develop realistic human-driven vehicle behavior model in simulations.
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