Sensors Know When to Interrupt You in the Car: Detecting Driver Interruptibility Through Monitoring of Peripheral Interactions

May 15, 2015

CHI2015_picture.pngInterruptions while driving can be quite dangerous, whether these are self-interruptions or external interruptions. They increase driver workload and reduce performance on the primary driving task. Being able to identify when a driver is interruptible is critical for building systems that can mediate these interruptions. In this paper, we collect sensor and human-annotated data from 15 drivers, including vehicle motion, traffic states, physiological responses and driver motion. We demonstrate that this data can be used to build a machine learning classifier that can determine interruptibility every second with a 94% accuracy. We present both population and individual models and discuss the features that contribute to the high performance of this system. Such a classifier can be used to build systems that mediate when drivers use technology to self-interrupt and when drivers are interrupted by technology.

paper copy


 Anind K Dey Jaemin Chun SeungJun Kim

CHI 2015

Associated Project:
Human-Vehicle Interaction


Paper Type:
Full paper