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Identifying Traffic Scenarios to Evaluate Driver Readiness in Automated Driving: An Exploratory Study
Technical Paper
2018-01-0502
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Automated vehicle technology is rapidly increasing in capability and the adoption of these technologies will become more widespread in the future. In the intermediate stages of automation where the driver is required to supplement the automated technology, it may be necessary to evaluate the driver’s readiness to take-over a part or of all the dynamic driving task (SAE, 2016). Specifically, while driving with a level 2 or 3 automated driving feature, a challenge may be that drivers with low readiness fail to take over in an appropriate manner.
One important implication of assessing driver readiness is to assess driver state. In this study, we investigated candidate for a driver readiness index which was compared between manual driving (Level 0) and ACC driving (Level 1).
Additionally, one more method to evaluate the readiness of the driver is to measure whether the driver anticipates potential hazards (i.e., does their foot hover over the brake or throttle). To encourage this type of behavior, vehicles could include a human-machine interface (HMI) that supports the driver to understand where potential hazards exist; however, this would need to be designed to prevent annoyance. The hypothesis for the series of studies was that showing overall traffic status allows the driver to more rapidly prepare for potential hazards when compared with no additional information (i.e., next lane vehicle turn signal). This part of the current study measured driver behavior and traffic data along a designated route in a naturalistic setting. Several traffic scenarios were identified that include overt anticipatory behavior.
In this paper, we had two research questions. One is how to measure driver readiness level. Two is what type of information would be useful for maintaining readiness.
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Authors
Citation
Fukui, T., Remtema, T., Austin, B., and Domeyer, J., "Identifying Traffic Scenarios to Evaluate Driver Readiness in Automated Driving: An Exploratory Study," SAE Technical Paper 2018-01-0502, 2018, https://doi.org/10.4271/2018-01-0502.Data Sets - Support Documents
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References
- SAE J3016 Surface Vehicle Recommended Practice: Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles Warrendale SAE International 2016
- 2017
- Lenneman , J. 2017
- Stahl , P. Supporting Anticipation in Driving through Attentional and Interpretational In-Vehicle Displays Accident Analysis and Prevention 103 113 2016
- Stahl , P. 2014
- Phillip , L.M. 2017
- Claus Marberger 2017
- Stahl , P. 2013