# Multimodal Driver Referencing: A Comparison of Pointing to Objects Inside and Outside the Vehicle

arXiv: https://arxiv.org/abs/2202.07360 · HTML: https://mlsanity.com/paper/2202.07360/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 15 Feb 2022.

## Builds on (most cited first, 6 of 6)

- [Efficient Low-rank Multimodal Fusion with Modality-Specific Factors](https://mlsanity.com/paper/1806.00064/) (2018)
- [You Have a Point There: Object Selection Inside an Automobile Using Gaze, Head Pose and Finger Pointing](https://mlsanity.com/paper/2012.13449/) (2020)
- [ML-PersRef: A Machine Learning-based Personalized Multimodal Fusion Approach for Referencing Outside Objects From a Moving Vehicle](https://mlsanity.com/paper/2111.02327/) (2021)
- [Studying Person-Specific Pointing and Gaze Behavior for Multimodal Referencing of Outside Objects from a Moving Vehicle](https://mlsanity.com/paper/2009.11195/) (2020)
- [Multi-modal Conditional Attention Fusion for Dimensional Emotion Prediction](https://mlsanity.com/paper/1709.02251/) (2017)
- [Multimodal Fusion Using Deep Learning Applied to Driver's Referencing of Outside-Vehicle Objects](https://mlsanity.com/paper/2107.12167/) (2021)

## Abstract (arXiv)

Advanced in-cabin sensing technologies, especially vision based approaches, have tremendously progressed user interaction inside the vehicle, paving the way for new applications of natural user interaction. Just as humans use multiple modes to communicate with each other, we follow an approach which is characterized by simultaneously using multiple modalities to achieve natural human-machine interaction for a specific task: pointing to or glancing towards objects inside as well as outside the vehicle for deictic references. By tracking the movements of eye-gaze, head and finger, we design a multimodal fusion architecture using a deep neural network to precisely identify the driver's referencing intent. Additionally, we use a speech command as a trigger to separate each referencing event. We observe differences in driver behavior in the two pointing use cases (i.e. for inside and outside objects), especially when analyzing the preciseness of the three modalities eye, head, and finger. We conclude that there is no single modality that is solely optimal for all cases as each modality reveals certain limitations. Fusion of multiple modalities exploits the relevant characteristics of each modality, hence overcoming the case dependent limitations of each individual modality. Ultimately, we propose a method to identity whether the driver's referenced object lies inside or outside the vehicle, based on the predicted pointing direction.