The potential of automotive perception sensors for local snow avalanche monitoring
- 1University of Graz, Heinrichstraße 36, 8010 Graz, Austria
- 2Virtual Vehicle Research GmbH, Inffeldgasse 21a, 8010 Graz, Austria
- 3Snow Scan GmbH, Stadlauerstraße 31, 1220 Wien, Austria
Monitoring of local snow avalanche releases are indispensable for many use cases. Existing lidar and radar technologies for monitoring local avalanche activity are costly and require closed source commercial software. These systems are often inflexible for exploring new use cases and too expensive for large scale applications, e.g., 100-1000 slopes. Therefore, developing reliable and inexpensive measurement and monitoring techniques with cutting- edge lidar and radar technology are highly required. Today, the automotive industry is a leading technology driver for lidar and radar sensors, because the largest challenge for achieving the next level of vehicle automation is to improve the reliability of its perception system. Automotive lidar sensors record high-resolution point clouds with very high acquisition frequencies of 10-20Hz and a range of up to 400m. High costs of mechanically spinning lidars (5-20kEUR) are still a limiting factor, but prices have already dropped significantly during the last decade and are expected to drop by another order of magnitude in the upcoming years. Modern automotive radar sensors operate at 24GHz and 77GHz, have a range of up to 300m, and provide raw data formats that allow the development of algorithms for detecting changes in the backscatter caused by avalanches. To exploit the potential of these newly emerging, cost- effective technologies for geoscientific applications, a stand-alone, modular sensor system called MOLISENS (MObile LIdar SENsor System) was developed in a cooperation between Virtual Vehicle Research Center and University of Graz. MOLISENS allows the modular incorporation of cutting-edge radar and lidar sensors. The open-source python package ‘pointcloudset’ was developed for handling, analyzing, and visualizing large datasets that consist of multiple point clouds recorded over time. This python package is designed to enable the development of new point cloud algorithms, and it is planned to extend the functionality to radar cluster data. Based on MOLISENS and pointcloudset, a strategy for their operational use in local avalanche monitoring is being developed.
How to cite: Muckenhuber, S., Goelles, T., Schlager, B., Kapper, K. L., Prokop, A., and Schöner, W.: The potential of automotive perception sensors for local snow avalanche monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4989, https://doi.org/10.5194/egusphere-egu23-4989, 2023.