Automated Intersection Mapping From Crowd Trajectory Data

Abstract

Driver assistance systems and automated driving are known to strongly benefit from digital maps. Keeping map attributes up to date is a challenge, particularly for the current manual measuring approach. In this paper, we present methods to extract information about intersections and traffic lights through a crowdsourcing approach. We use position and dynamic data from a fleet of test vehicles with close-to-market sensors. A statistical hypothesis test is proposed to identify groups of driving directions at an entry of an intersection, which have synchronous traffic light signaling. This information is used to improve the detection of the relevant traffic light signal in case there is a different signaling for the driving directions. Based on a test data set, we classified whether the signaling is synchronous or not with an accuracy of 93.8%. To assess the usefulness of our mapping scheme, we have investigated its contribution to a camera-based traffic light recognition system. An evaluation of the use of additional map information for the traffic light detection was performed on a set of 344 logged intersection crossings from this vehicle. We showed that there is an improvement in the accuracy up to 5.2%, dependent on the test conditions.