This paper discusses the current methods for vehicle self-localization and compares previous findings to the use for urban public traffic vehicles. In specific, requirements for autonomous buses approaching a bus stop are defined. An autonomous system capable of reliable vehicle self-localization running in real-time in a city scenario shall be developed in a future work based on this paper. The comparison of filter-based estimation and graph-based optimization techniques shows that the latter suits the the automated approach to a bus stop in an urban environment the best. Based on these findings, a concept for self-localization of public transport vehicles equipped with a variety of imaging sensors with the help of a digital high definition map is presented. A current method is shown and a concept of improving the localization by inferring semantic information into landmark detection by low-level data fusion is provided. Validation and verification of the proposed fusion approach have to be carried out in the future, but a validation scenario is presented in this work.
Vehicle self-localization is one of the most important capabilities for automated driving. Current localization methods already provide accuracy in the centimeter range, so robustness becomes a key factor, especially in urban environments. There is no commonly used standard metric for the robustness of localization systems, but a set of different approaches. Here, we show a novel robustness score that combines different aspects of robustness and evaluate a graph-based localization method with the help of fault injections. In addition, we investigate the influence of semantic class information on robustness with a layered landmark model. By using the perturbation injections and our novel robustness score for test drives, system vulnerabilities or possible improvements are identified. Furthermore, we demonstrate that semantic class information allows early discarding of misclassified dynamic objects such as pedestrians, thus improving false-positive rates. This work provides a method for the robustness evaluation of landmark-based localization systems that are also capable of measuring the impact of semantic class information for vehicle self-localization.
Abstract. In this paper, we demonstrate the inclusion of a top-view camera system mounted on a city bus in an existing sensor setup. A novel sensor setup with five down-facing cameras is mounted on the roof of a MAN Lion’s City 12 city bus to extract landmarks in road scene images. Its positioning is validated by an exemplary detection of lane markings. The concept for further landmark detection with the help of the presented camera system is explained in this paper and sensor data fusion methods are proposed. Based on our previous findings (Albrecht et al., 2019), strengths of the novel sensor system are introduced to improve the current environment perception system. For now, only a qualitative observation of the capability to detect lane markings and other landmarks can be presented. Future work will use the current findings for landmark detection for a vehicle self-localization system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.