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