A crowdsourcing approach to natural language automotive command interfaces - Parr, Jonathan (2017)
Modern vehicles present drivers with a wide range of control options, the quantity of which have increased with technologies such as satellite navigation and advanced in-car entertainment systems. Conventional controls can cause safety-related effects such as requiring drivers to look away from the road. Voice interfaces offer the potential to reduce or eliminate visual distraction; however industry surveys show current implementations to be disliked by many users who consequently revert to conventional controls. This research considers why users reject voice interfaces and develops methods to address these concerns. Focus is given to the use of crowdsourcing methods to obtain geographically tagged command samples across a broad area. A simple token-based pattern-matching command processor is developed which bases command interpretation on command samples which are selected by geographic region and which therefore increase habitability by better matching each user’s choice of command words. Crowdsourcing methods to enable user-selection of system response thresholds are also considered.