A Speech-Based On-Demand Intersection Assistant Prototype.

We have recently proposed a speech-based on-demand intersection assistant which helps the driver to handle urban intersections by informing him of the traffic situation on the right hand side and recommending suitable gaps in traffic. In a previous user study, conducted in a simulator, we could show that the system is in general well accepted and preferred by drivers compared to driving without assistance or with only visual support. In this paper, we report on an implementation of this system and its evaluation in real urban traffic. We use LIDAR sensors for the perception of the traffic environment. A scene analyzer estimates the gaps between the vehicles in real time. The result of this analysis is provided to a dialog manager, which uses it to inform the driver of approaching vehicles and suitable gaps. While approaching the intersection, the driver can activate the system via a wake-up-word and control it with subsequent speech commands. The design of the data analyzer and dialog manager is based on evaluations at real intersections. The resulting system can provide suitable support to the driver in a wide range of traffic situations.