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  4. Watch Out Car, He’s Drunk! How Passengers of Vehicles Perceive Risky Crossing Situations Based on Situational Parameters
 
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2022
Conference Paper
Title

Watch Out Car, He’s Drunk! How Passengers of Vehicles Perceive Risky Crossing Situations Based on Situational Parameters

Abstract
Automated vehicles promise enhanced road safety for their passengers, other vehicles, and vulnerable road user (VRU). To do so, automated vehicles must be designed to reliably detect potentially critical situations. Humans can detect such situations using context cues. Context cues allow humans drivers to anticipate unexpected crossings, e.g., of intoxicated night owls in a street full of bars and clubs on a Friday night and, consequently, to decelerate in advance to prevent critical incidents.
We used the “Incident Detector” to identify possible context cues that human drivers might use to assess the criticality of traffic situations in which a car encounters a VRU. Investigated potential predictors include VRUs’ mode of transport, VRUs’ speed, VRUs’ age, VRUs’ predictability of behavior, and visibility obstruction of VRUs by parked cars.
In an online study, 133 participants watched videos of potentially risky crossing situations with VRUs from the driver’s point of view. In addition, the participants’ age, gender, status of driver’s license, sense of presence, and driving style were queried.
The results show that perceived risk correlates significantly with age, speed, and predictability of VRUs behavior, as well as with visibility obstruction and participants’ age. We will use the results to include detected influence factors on perceived subjective risk into virtual test scenarios. Automated vehicles will need to pass these virtual test scenarios to be deemed acceptable regarding objective and subjective risk. These test scenarios can support road safety and thus, greater acceptance of automated vehicles.
Author(s)
Bopp-Bertenbreiter, Anja Valeria  
Univ. Stuttgart, Institut für Arbeitswissenschaft und Technologiemanagement -IAT-  
Bähr, Sabina
Univ. Stuttgart, Institut für Arbeitswissenschaft und Technologiemanagement -IAT-  
Albrecht, Simon
Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO  
Freudenmann, Thomas
EDI GmbH - Engineering Data lntelligence
El-Haji, Mohanad
EDI GmbH - Engineering Data lntelligence
Martin, Manuel  
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Anh, Natalya
IPG Automotive GmbH
Rauber, Stephan
IPG Automotive GmbH
Mainwork
HCI in Mobility, Transport, and Automotive Systems. 4th International Conference, MobiTAS 2022. Proceedings  
Conference
International Conference on HCI in Mobility, Transport and Automotive Systems 2022  
International Conference on Human-Computer Interaction (HCI International) 2022  
DOI
10.1007/978-3-031-04987-3_23
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Fraunhofer-Institut für Arbeitswirtschaft und Organisation IAO  
Keyword(s)
  • Driver behavior modeling

  • Realistic traffic flow simulation

  • Active safety systems

  • Autonomous driving and ADAS algorithms

  • Dynamic Risk Management

  • Safety Cushion Time

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