Touchless Selection Schemes for Intelligent Automotive User Interfaces With Predictive Mid-Air Touch

Abstract

Predictive touch technology aims to improve the usability and performance of in-vehicle displays under the influence of perturbations due to the road and driving conditions. It fundamentally relies on predicting and early in the freehand pointing movement, the interface item the user intends to select, using a novel Bayesian inference framework. This article focusses on evaluating facilitation schemes for selecting the predicted interface component whilst driving, and without physically touching the display, thus touchless. Initially, several viable schemes were identified in a brainstorming session followed by an expert workshop with 12 participants. A simulator study with 24 participants using a prototype predictive touch system was then conducted. A number of collected quantitative and qualitative measures show that immediate mid-air selection, where the system autonomously auto-selects the predicted interface component, may be the most promising strategy for predictive touch.