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Sophisticated user interaction in the automotive industry is a fast emergingtopic. Mid-air gestures and speech already have numerous applications fordriver-car interaction. Additionally, multimodal approaches are being developedto leverage the use of multiple sensors for added advantages. In this paper, wepropose a fast and practical multimodal fusion method based on machine learningfor the selection of various control modules in an automotive vehicle. Themodalities taken into account are gaze, head pose and finger pointing gesture.Speech is used only as a trigger for fusion. Single modality has previouslybeen used numerous times for recognition of the user's pointing direction. We,however, demonstrate how multiple inputs can be fused together to enhance therecognition performance. Furthermore, we compare different deep neural networkarchitectures against conventional Machine Learning methods, namely SupportVector Regression and Random Forests, and show the enhancements in the pointingdirection accuracy using deep learning. The results suggest a great potentialfor the use of multimodal inputs that can be applied to more use cases in thevehicle.

You Have a Point There: Object Selection Inside an Automobile Using Gaze, Head Pose and Finger Pointing