Advanced driver assistance systems (ADAS) applications are currently an area of focus in the automotive industry. Modern vehicles are equipped with different ADAS functions, increasing the driver’s comfort and safety. However, the complexity of ADAS functions is also increasing rapidly, and validation of such systems is becoming challenging. The validation of such a complex system in the real world is expensive and time-consuming. Therefore, the automotive industry has started considering type approval based on virtual tests. The virtual environment and environmental perception sensors exhibit the complexity and behavior of real-world scenarios, and sensors are essential elements and enablers in all these activities. However, the state-of-the-art virtual scenarios and environmental perception sensors provided by simulation tool vendors are insufficient to perform such tasks. Therefore, this scientific work will focus on developing and validating high-fidelity automotive LiDAR and RADAR sensor models. The developed sensor model will be considering the complete signal processing toolchain and sensor-specific effects of real LiDAR and RADAR sensors to generate a realistic output. Moreover, they will also include the environmental condition effects such as rain, fog and sunlight (only for LiDAR). The LiDAR sensor model will output the time-domain and point cloud data. The RADAR sensor model will output the raw data (range map, range Doppler map, CFAR), and object detection list. Lab tests, proving ground and real-world test drives will be conducted to obtain real sensors data to validate the virtual sensors. A tool chain will be also developed to replicate the real-world scenarios into the simulation with high-fidelity. The sensor models will be developed by using a standardized open simulation interface (OSI) and functional mock-up Interface (FMI). So, the sensor model can be tool-independent, and users can integrate them into the best-suited simulation tools with ease and intellectual property (IP) infringement.
Haider, A.; Pigniczki, M.; Köhler, M.H.; Fink, M.; Schardt, M.; Cichy, Y.; Zeh, T.; Haas, L.; Poguntke, T.; Jakobi, M.; Koch, A.W. Development of High-Fidelity Automotive LiDAR Sensor Model with Standardized Interfaces. Sensors 2022, 22, 7556. https://doi.org/10.3390/s22197556
A. Haider, A. Eryildirim, M. Thumann, T. Zeh and S. -A. Schneider, "Influence of RF Group Delay on the Performance of FMCW Automotive Radar Sensor," 2021 IEEE 21st Annual Wireless and Microwave Technology Conference (WAMICON), Sand Key, FL, USA, 2021, pp. 1-6, doi: 10.1109/WAMICON47156.2021.9443606.
A. Haider et al., "Integration of Phase Noise into a Virtual Test Driving Software to Investigate the Impact on the Radar Performance," 2020 IEEE 21st International Radar Symposium (IRS), Warsaw, Poland, 2020, pp. 328-333, doi: 10.23919/IRS48640.2020.9253801.
Haider, Arsalan, Jihoon Kim, Marcel Sachse, Thomas Zeh, Stefan Alexander Schneider, Matthias Thumann, Abdulkadir Eryildirim, and Tim Ebeling. "Automotive Radar Sensor Behavioral Models for Closed Loop Simulations." In Proceedings of the 5th International Symposium on Future Active Safety Technology toward Zero Accidents (FAST-zero’19). 2019.