Leon Tolksdorf

Tobias Kern, Leon Tolksdorf, Christian Birkner

Physically reduced-scale vehicles are emerging to accelerate the development of advanced automated driving functions. In this paper, we investigate the effects of scaling on self-localization accuracy with visual and visual-inertial algorithms using cameras and an inertial measurement unit (IMU). For this purpose, ROS2-compatible visual and visual-inertial algorithms are selected, and datasets are chosen as a baseline for real-sized vehicles. A test drive is conducted to record data of reduced-scale vehicles. We compare the selected localization algorithms, OpenVINS, VINS-Fusion, and RTAB-Map, in terms of their pose accuracy against the ground-truth and against data from real-sized vehicles. When comparing the implementation of the selected localization algorithms to real-sized vehicles, OpenVINS has the lowest average localization error. Although all selected localization algorithms have overlapping error ranges, OpenVINS also performs best when applied to a reduced-scale vehicle. When reduced-scale vehicles were compared to real-sized vehicles, minor differences were found in translational vehicle motion estimation accuracy. However, no significant differences were found when comparing the estimation accuracy of rotational vehicle motion, allowing RSVRs to be used as testing platforms for self-localization algorithms.

Daniel Weihmayr, Fatih Sezgin, Leon Tolksdorf et al.

Pedestrians are among the most endangered traffic participants in road traffic. While pedestrian detection in nominal conditions is well established, the sensor and, therefore, the pedestrian detection performance degrades under adverse weather conditions. Understanding the influences of rain and fog on a specific radar and lidar sensor requires extensive testing, and if the sensors' specifications are altered, a retesting effort is required. These challenges are addressed in this paper, firstly by conducting comprehensive measurements collecting empirical data of pedestrian detection performance under varying rain and fog intensities in a controlled environment, and secondly, by introducing a dedicated Weather Filter (WF) model that predicts the effects of rain and fog on a user-specified radar and lidar on pedestrian detection performance. We use a state-of-the-art baseline model representing the physical relation of sensor specifications, which, however, lacks the representation of secondary weather effects, e.g., changes in pedestrian reflectivity or droplets on a sensor, and adjust it with empirical data to account for such. We find that our measurement results are in agreement with existent literature related to weather degredation and our WF outperforms the baseline model in predicting weather effects on pedestrian detection while only requiring a minimal testing effort.