Our paper was accepted by IJHCI
Our paper Modeling Driver Situational Awareness in Takeover Scenarios Using Multimodal Data and Machine Learning has been published in the JCR Q1 journal International Journal of Human–Computer Interaction.
- Title
- Modeling Driver Situational Awareness in Takeover Scenarios Using Multimodal Data and Machine Learning
- Authors
- Lesong Jia, Na Du
- Abstract
- In conditionally automated driving, drivers out of the control loop may lack situational awareness (SA), leading to inappropriate takeovers. Monitoring a driver's SA and providing alerts for overlooked objects is critical to enhancing the takeover safety and efficiency. This study aimed to construct predictive models for drivers' SA of objects during takeover transitions. The model features include drivers' physiological data before and after takeover requests as well as the environment and object attributes. The ground truth was obtained through a scene reconstruction task, yielding binary SA labels. The Support Vector Machine delivered the best model performance, achieving a macro F1 score of 0.75 and an accuracy of 0.77, when applied with a time window of 2-second pre-takeover request and 4-second post-takeover request. Our model predicts drivers' SA of specific objects across diverse traffic conditions using short time windows, supporting timely and generalizable driver monitoring and takeover assistance.