INFERRING LEFT-TURN INFORMATION FROM MOBILE CROWDSENSING

摘要

Left turns are known to be one of the most dangerous driving maneuvers. An effective way to mitigate this safety risk is to install a left-turn enforcement — for example, a protected left-turn signal or all-way stop signs — at every turn that preserves a traffic phase exclusively for left turns. Although this protection scheme can significantly increase the driving safety, information on whether or not a road segment (e.g., intersection) has such a setting is not yet available to the public and navigation systems. This disclosure presents a system that exploits mobile crowdsensing and deep learning to classify the protection settings of left turns.