Validated Innovative Multi-Sensor System for Cooperative Automated Driving

Publications

VALISENS publications:

  • Klöppel-Gersdorf, M., & Otto, T. (2024). Using V2X-Information for Trajectory Prediction at Urban Intersections. In International Conference on Vehicle Technology and Intelligent Transport Systems 2024.
  • Mirlach, J., Wan, L., Wiedholz, A., Keen, H. E., & Eich, A. (2025). R-LiViT: A LiDAR-Visual-Thermal Dataset Enabling Vulnerable Road User Focused Roadside Perception. In Proceedings of the IEEE/CVF International Conference on Computer Vision 2025, pp. 28375-28384.
  • Wan, L., Zhao, J., Wiedholz, A., Bied, M., de Lucena, M. M., Jagtap, A. D., Festag, A., Fröhlich, A. A., Keen, H. E., & Vinel, A. (2025). A Systematic Literature Review on Vehicular Collaborative Perception—A Computer Vision Perspective. In IEEE Transactions on Intelligent Transportation Systems, vol. 27, no. 1, pp. 81-118, Jan. 2026, doi: 10.1109/TITS.2025.3631141.
  • Klöppel-Gersdorf, M., Bellanger, A., Partzsch, I. & Otto, T (2024). Enhancing Trajectories from Collective Perception Messages for Performance Measurements at signalized Intersections
  • Partzsch, I., Klöppel-Gersdorf, M., Holfeld, J., Schubert, K., Trauzettel, F., Bellanger, A. & Otto, T. (2026). On the Usefulness of Collective Perception for Active and Inclusive Mobility. In Conference on Sustainable Urban Mobility (CSUM) 2026
  • Wan, L., Gupta, P., Eich, A., Kettelgerdes, M., Keen, H. E., Klöppel-Gersdorf, M., & Vinel, A. (2025). VALISENS: A Validated Innovative Multi-Sensor System for Cooperative Automated Driving. arXiv preprint arXiv:2505.06980.

 

VALISENS related publications:

  • Wan, L., Keen, H. E., & Vinel, A. (2025). The Components of Collaborative Joint Perception and Prediction - A Conceptual Framework. In International Conference on Vehicle Technology and Intelligent Transport Systems 2025.
  • Kettelgerdes, M., Pandey, A., Unruh, D., Erdogan, H., Wunderle, B., & Elger, G. (2023, November). Automotive lidar based precipitation state estimation using physics informed spatio-temporal 3d convolutional neural networks (pist-cnn). In 2023 29th International Conference on Mechatronics and Machine Vision in Practice (M2VIP) (pp. 1-6). IEEE.
  • Kettelgerdes, M., Sarmiento, N., Erdogan, H., Wunderle, B., & Elger, G. (2024). Precise Adverse Weather Characterization by Deep-Learning-Based Noise Processing in Automotive LiDAR Sensors. Remote Sensing, 16(13), 2407.
  • Kettelgerdes, M., Hillmann, T., Hirmer, T., Erdogan, H., Wunderle, B., & Gordon, E. (2024). Accelerated real-life testing of automotive lidar sensors as enabler for in-field condition monitoring. In Tagungsband 4. Symposium Elektronik und Systemintegration ESI (pp. 96-107).
  • Kettelgerdes, M., Hillmann, T., Hirmer, T., Erdogan, H., Wunderle, B., & Elger, G. (2023). Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models. arXiv preprint arXiv:2312.04229.