Facing Driver Frustration: Towards Real-Time In-Vehicle Frustration Estimation Based on Video Streams of the Face

Drivers frequently experience frustration when facing traffic jams, red lights or badly designed in-vehicle interfaces. Frustration can lead to aggressive behaviors and negative influences on user experience. Affect-aware vehicles that recognize the driver’s degree of frustration and, based on this, offer assistance to reduce the frustration or mitigate its negative effects promise remedy. As a prerequisite, this needs a real-time estimation of current degree of frustration. Consequently, here we describe the development of a classifier that can recognize whether a frustrated facial expression was shown based on video streams of the face. For demonstration of its real-time capabilities, a demonstrator of a frustration-aware vehicle including the classifier, the Frust-O-Meter, is presented. The system is integrated into a driving simulator and consists of (1) a webcam, (2) a preprocessing unit, (3) a user model, (4) an adaptation unit and (5) a user interface. In the current version, a happy song is played once a high degree of frustration is detected. The Frust-O-Meter can form the basis for the development of frustration-aware vehicles and is foreseen to be extended to more modalities as well as more user need-oriented adaption strategies in the near future.

The software package Emotient FACET (Imotions, Copenhagen, Denmark) was used to extract information regarding the evidence of activity of 19 facial action units (AUs 1 ) frame-wise from the facial videos.

Affect-aware vehicles Empathic systems Automated facial expression analysis Driver frustration


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