Abstract
The goal of this study is to provide a framework, using hidden semi-Markov models, for modeling a driver's response time after an alert is provided in manual driving. Given the plethora of alerts and warning within a vehicle, there is a need to understand when a driver will respond after an alert is provided. Data from a previous driving simulator study, where drivers were interacting with an in-vehicle information system (IVIS) were used for model training. The final data set included 16 participants, with 288 task initiations. The proposed model could predict a driver's response time accurately using only a small portion of the available data, and had a mean absolute error of 0.51 seconds with 84% of predictions within an absolute error of 1 second. This framework has applicability in mitigating the risk of transitions in driver distraction. This includes transitions from the road to the secondary task and back to the road.
Original language | English (US) |
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Pages (from-to) | 4739-4745 |
Number of pages | 7 |
Journal | IEEE Transactions on Intelligent Transportation Systems |
Volume | 23 |
Issue number | 5 |
DOIs | |
State | Published - May 1 2022 |
Keywords
- hidden semi-Markov model
- In-vehicle alert
- prediction
- warning
ASJC Scopus subject areas
- Automotive Engineering
- Mechanical Engineering
- Computer Science Applications