Nico Epple, Harshit Chopra, Andreas Riener,
"How Do Drivers Observe Surrounding Vehicles in Real-World Traffic? Estimating the Drivers Primary Observed Traffic Objects"
: 2021 IEEE Intelligent Vehicles Symposium (IV), IEEE Xplore, 11-2021
How Do Drivers Observe Surrounding Vehicles in Real-World Traffic? Estimating the Drivers Primary Observed Traffic Objects
Sprache des Titels:
2021 IEEE Intelligent Vehicles Symposium (IV)
Even though the safeguarding of automated driving functions is very present in automotive research, semiautomated and manually controlled systems will continue to be predominant in the next decade. Still, automation features such as driver assistance systems are becoming more prevalent and can operate more intuitively with the driver in a closed loop. Efficient interaction with the driver in partially automated systems can further improve road safety in the meantime. In this context, assessing the driver's perception of nearby traffic is crucial to making driver assistance safer and more collaborative. We propose a data-driven method to detect traffic-objects a driver is visually observing in the headway and side traffic using a multilabel neural-network as a classifier. This functionality is necessary to distinguish well controlled anticipatory driving from missing perception in partially automated driving. This could be an early reaction to decelerating vehicles ahead, even before a critical situation arises, or verification of the driver's perception of a cut-in. We validate the method using a data-driven ground truth from wearable eye trackers and automatically generated labels. Additional information comes from cameras installed in the vehicles to monitor the driver and traffic, as well as from measurement equipment that records CAN bus data. The proposed method achieves a Hamming accuracy of 69%, outperforming previous geometric models.