Posted in Webinars

Load Matrix™ is a flexible and for all technologies adaptable solution which supports product development by combining many years of experience with several important development domains (like statistics, engineering, material science, chemical engineering and computational science) to ensure the best possible product results by using market and end customer usage relevant driving profiles.
Posted in ArchitectECA2030 Conferences

At the beginning of September ArchitectECA2030 will be presented at the 14th Graz Symposium Virtual Vehicle in Austria, organized by project partners: Virtual Vehicle Research GmbH and Graz University of technology.
The Graz Symposium Virtual Vehicle 2021 serves as a platform to discuss recent advances in systems integration and virtual validation and its optimal coexistence with physical testing. The event focuses on methods, tools, data, and processes for virtual validation.
More information about the event and registration
Posted in ArchitectECA2030 Conferences

Don't miss out and register now! Join us online to discover, network, learn and shape ‘Our Digital Future’ at the European Forum for Electronic Components and Systems (EFECS) 2020! The fourth edition of this international event is going to be virtual and unique, taking place on 25-26 November 2020.
Continue Reading
Posted in Papers
Birgit Schlager, Thomas Goelles, Stefan Muckenhuber, Daniel Watzenig
Abstract: Lidar sensors play an essential role in the perception system of automated vehicles. Fault Detection, Isolation, Identification, and Recovery (FDIIR) systems are essential for increasing the reliability of lidar sensors. Knowing the influence of different faults on lidar data is the first crucial step towards fault detection for lidar sensors in automated vehicles. We investigate the influences of sensor cover contaminations on the output data, i.e., on the lidar point cloud and full waveform. Different contamination types were applied (dew, dirt, artificial dirt, foam, water, and oil) and the influence on the output data of the single beam lidar RIEGL LD05-A20 and the automotive mechanically spinning lidar Ouster OS1-64 was evaluated. The LD05-A20 measurements show that dew, artificial dirt, and foam lead to unwanted reflections at the sensor cover. Dew, artificial dirt over the entire transmitter, and foam measurements lead to severe faults, i.e., complete sensor blindness. The OS1-64 measurements also show that dew can lead to almost complete sensor blindness. The results look promising for further studies on fault detection and isolation, since the different contamination types lead to different symptom combinations.
Continue Reading
Posted in Papers
Adam Ligocki, Ales Jelinek, Ludek Zalud
Abstract.This paper presents our latest extension of the Brno Urban Dataset (BUD), the Winter Extension (WE). The dataset contains data from commonly used sensors in the automotive industry, like four RGB and single IR cameras, three 3D LiDARs, differential RTK GNSS receiver with heading estimation, the IMU and FMCW radar. Data from all sensors are precisely timestamped for future offline interpretation and data fusion. The most significant gain of the dataset is the focus on the winter conditions in snow-covered environments.
Continue Reading