Road Intensity Based Mapping using Radar Measurements with a Probability Hypothesis Density Filter, Report no. LiTH-ISY-R-2994

Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) lter framework is applied to automotive imagery sensor data for constructing such a map, where the main advantages are that it avoids the detection, the data association and the track handling problems in conventional multiple-target tracking, and that it gives a parsimonious representation of the map in contrast to grid based methods. Two original contributions address the inherent complexity issues of the algorithm: First, a data clustering algorithm is suggested to group the components of the PHD into di erent clusters, which structures the description of the prior and considerably improves the measurement update in the PHD lter. Second, a merging step is proposed to simplify the map representation in the PHD lter. The algorithm is applied to multi-sensor radar data collected on public roads, and the resulting map is shown to well describe the environment as a human perceives it.