Investigation of drivers’ thresholds of a subjectively accepted driving performance with a focus on automated driving

Lately, the development and implementation of automated driving moved to the center of interest in the automotive industry. In this context, one of the central issues – the configuration of adequate trajectories – is mainly tackled using a technical approach. However, it appears that a technically ideal driving performance does not necessarily coincide with the drivers’ subjective preferences. This study strives to determine thresholds of a subjectively accepted driving performance regarding lateral vehicle control. A second objective is to analyze the influence of selected personal and situational factors on these thresholds. An empirical online survey with 161 participants rating video sequences of driving performances was conducted. The video sequences differed not only with regard to the lateral offset of the ego-vehicle but also concerning the weather (sun/rain) and traffic conditions (existence/driving behavior of oncoming traffic). Additionally, the participants’ driving experience and sensation seeking were considered in the data evaluation. To analyze the data, binary logistic regression analyses were calculated. They revealed that the subjective evaluation of driving performances varies primarily depending on the lateral offset of both the ego-vehicle and the oncoming traffic. The results indicate that regarding the lateral offset certain thresholds of subjectively accepted driving performances do exist. Regarding the development of automated driving systems, two issues need to be considered in order to ultimately guarantee user acceptance. First, the subjective thresholds need to be integrated into the systems’ trajectory planning. Second, the oncoming traffic’s driving behavior has to be considered.

• Record URL:
  https://doi.org/10.1016/j.trf.2018.04.024
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S1369847817305144
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/13698478
• Supplemental Notes:
  • Abstract reprinted with permission of Elsevier.
• Authors:
  • Vo�, Gudrun M I
  • Keck, Caroline M
  • Schwalm, Maximilian
• Publication Date: 2018-7

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 280-292
• Serial:
  • Transportation Research Part F: Traffic Psychology and Behaviour
  • Volume: 56
  • Publisher: Elsevier
  • ISSN: 1369-8478
  • Serial URL: http://www.sciencedirect.com/science/journal/13698478

Subject/Index Terms

• TRT Terms: Acceptance; Automated vehicle control; Behavior; Driver performance; Drivers; Logistic regression analysis; Technology
• Subject Areas: Data and Information Technology; Highways; Planning and Forecasting;

Filing Info

• Accession Number: 01675433
• Record Type: Publication
• Files: TRIS
• Created Date: Jul 19 2018 2:45PM