Analysis of a Speech-Based Intersection Assistant in Real Urban Traffic

Previously, we have presented a speech-based intersection assistant prototype. The system is activated on-demand by the driver and gives afterwards, via speech, information on suitable gaps between the traffic vehicles approaching from the right. It is comparable to a front seat passenger which helps in the maneuver decision for an intended turn left. This system has assumed a more or less constant flow of the traffic. To also handle situations of more dynamic urban traffic, including vehicles that may be slowing down or stopping, we have now extended our previous approach by a dynamic vehicle model. This model predicts the future traffic vehicle state based on second-order vehicle dynamics. We perform an in depth analysis of our system on a set of recordings under various traffic conditions. In this analysis we compare in particular the previous and the novel vehicle model. Both approaches lead to a correct recommendation in approximately 90% of the cases. Unexpectedly, the dynamic model does not lead to significant improvements in the system behavior, despite its increased accuracy.