There is a gap in holistic urban scene understanding between multi-modal datasets for segmentation and object detection on the one hand and traffic light datasets on the other hand. The role of traffic lights in the former is not labelled, making it difficult to use them for higher-level tasks and leave critical information of an intersection scene blank. Including traffic lights from traffic light specific datasets into the comprehensive semantic data introduces a penalty from the domain shift. We close this gap by providing semantically annotated traffic lights for the Cityscapes dataset. We demonstrate the domain shift penalty by using a traffic light dataset from a similar domain and show superior performance on data labelled in the original domain. We demonstrate an application by training a real-time capable network for semantic segmentation and object detection which can now additionally make sense of traffic lights, delivering an F1- Score of 66.4% on the important class of traffic lights relevant to the ego vehicle. The network is made publicly available at https://github.com/joeda/NNAD and the data at https://github.com/KIT-MRT/cityscapes-t1.

In this paper, we consider the transformation of laser range measurements into a top-view grid map representation to approach the task of LiDAR-only semantic segmentation. Since the recent publication of the SemanticKITTI data set, researchers are now able to study semantic segmentation of urban LiDAR sequences based on a reasonable amount of data. While other approaches propose to directly learn on the 3D point clouds, we are exploiting a grid map framework to extract relevant information and represent them by using multi-layer grid maps. This representation allows us to use well-studied deep learning architectures from the image domain to predict a dense semantic grid map using only the sparse input data of a single LiDAR scan. We compare single-layer and multi-layer approaches and demonstrate the benefit of a multi-layer grid map input. Since the grid map representation allows us to predict a dense, 360° semantic environment representation, we further develop a method to combine the semantic information from multiple scans and create dense ground truth grids. This method allows us to evaluate and compare the performance of our models not only based on grid cells with a detection, but on the full visible measurement range.

An essential task for Intelligent Transportation System is to obtain precise knowledge of local environments as well as the local (within structured environment) and global vehicle pose. Market entry and large-scale production of autonomous driving functions postulate two elementary constraints. First, the utilized sensor setup has to be both cost-efficient and space-saving. Second, the system has to be fail-safe according to Automotive Safety Integrity Level1D. This paper presents an approach to robustly estimate the vehicles pose both within the current lane and a digital map via pose graph optimization. Outliers and ambiguities are rejected by a suitable loss function and therefore remove the necessity of refined statistical tests. Fall-back solutions, when single sensors are permanently corrupted, are provided by solving various graphs simultaneously. Experimentally, the applicability and performance of the presented approach is demonstrated using an Opel Insignia and its 2D dynamic sensors, with an additional gray-scale camera mounted at the front and at the rear window, a low-cost GNSS receiver and a previously recorded digital map. The graph-based approach has a mean solver time of 14.48 ms and a maximal lateral error below 27.03 cm with a Standard Deviation of 10.05 cm and outperforms the previously presented Extended Kalman Filter and Particle Filter approaches [1].