2017Unpublished venueRequires access

The Driver Distraction Detection Algorithm AttenD

Katja Kircher, Christer Ahlström

Open publisher page 23 citations

Abstract

Driver distraction is a contributing factor to many crashes, and a real-time distraction warning system has the potential to mitigate or circumvent many of these crashes. The objective of this chapter is to describe in detail the distraction detection algorithm AttenD and explain the theory underlying different design choices. Future aspects and distraction warning strategies are discussed also. In summary, AttenD is an eye-tracker-based distraction detection algorithm which identifies visual distraction in real time based on single long glances as well as repetitive glances. The core idea of the algorithm is a two-second time buffer which is decremented when the driver looks away from the road and incremented when the driver looks back at the road. If the buffer runs empty, the driver’s state is classified as distracted.

About this research paper

What this paper is about

Driver distraction is a contributing factor to many crashes, and a real-time distraction warning system has the potential to mitigate or circumvent many of these crashes. The objective of this chapter is to describe in detail the distraction detection algorithm AttenD and explain the theory underlying different design choices. Future aspects and distraction warning strategies are discussed also. In summary, AttenD is an eye-tracker-based distraction detection algorithm which identifies visual distraction in real time based on single long glances as well as repetitive glances. The core idea of the algorithm is a two-second time buffer which is decremented when the driver looks away from the road and incremented when the driver looks back at the road. If the buffer runs empty, the driver’s state is classified as distracted.

Why it matters

OpenAlex reports 23 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Driver distraction is a contributing factor to many crashes, and a real-time distraction warning system has the potential to mitigate or circumvent many of these crashes. The objective of this chapter is to describe in detail the distraction detection algorithm AttenD and explain the theory underlying different design choices. Future aspects and distraction warning strategies are discussed also. In summary, AttenD is an eye-tracker-based distraction detection algorithm which identifies visual distraction in real time based on single long glances as well as repetitive glances. The core idea of the algorithm is a two-second time buffer which is decremented when the driver looks away from the road and incremented when the driver looks back at the road. If the buffer runs empty, the driver’s state is classified as distracted.

Key concepts: Distraction, Computer science, Psychology, Algorithm, Cognitive psychology

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