Investigation on Misclassification of Pedestrians as Poles by Simulation.

High-precision self-localization is one of the most important capabilities of automated vehicles. Not only accuracy but also localization robustness are crucial for self-driving vehicles in urban environments. The localization robustness decreases by misclassifications of landmarks and therefore false matches between dynamic objects and static landmarks listed in an a priori map. Here we show in the CARLA simulation environment, that the usage of semantic information prevents misclassifications of pedestrians as poles and so increases robustness in urban scenarios. In a simulated scenario of a road intersection pedestrians misclassified without semantic information could be filtered out by class label. In the presented experiments no mismatches of dynamic objects and map landmarks occurred and therefore the localization robustness was increased. Not only pole-like dynamic objects but also semi-static objects like parking cars or freight containers in terminal applications can be detected and excluded from map-based position estimation. The findings of this work show that the introduction of semantic class information leads to a higher self-localization robustness in urban scenarios and therefore should be included into current localization methods.