Multimodal Fusion Using Deep Learning Applied to Driver's Referencing of Outside-Vehicle Objects
This work addresses the need for more natural driver-car interaction in the automotive industry, though it is incremental in applying deep learning to multimodal fusion for this specific domain.
The paper tackled the problem of precisely predicting objects outside a vehicle referenced by drivers using multimodal sensing, achieving improved accuracy by fusing gaze, head pose, and finger pointing features to overcome modality-specific limitations.
There is a growing interest in more intelligent natural user interaction with the car. Hand gestures and speech are already being applied for driver-car interaction. Moreover, multimodal approaches are also showing promise in the automotive industry. In this paper, we utilize deep learning for a multimodal fusion network for referencing objects outside the vehicle. We use features from gaze, head pose and finger pointing simultaneously to precisely predict the referenced objects in different car poses. We demonstrate the practical limitations of each modality when used for a natural form of referencing, specifically inside the car. As evident from our results, we overcome the modality specific limitations, to a large extent, by the addition of other modalities. This work highlights the importance of multimodal sensing, especially when moving towards natural user interaction. Furthermore, our user based analysis shows noteworthy differences in recognition of user behavior depending upon the vehicle pose.