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About

Josef Steinbaeck is a researcher specializing in automotive and robotic perception systems, with a particular focus on multi-sensor fusion, occupancy grid mapping, and environment perception for automated driving. His work centers on integrating heterogeneous sensor modalities — most notably radar and time-of-flight (ToF) sensors — to create robust, reliable perception pipelines capable of meeting the stringent safety demands of autonomous systems. Steinbaeck's most influential contribution is his open hardware and software framework for low-level radar and ToF sensor fusion, which has garnered 15 citations and provided the research community with a reproducible platform for developing and benchmarking fusion strategies. Building on this foundation, his 2019 work on occupancy grid fusion demonstrated the advantages of processing raw sensor data prior to compression, preserving critical environmental information with 11 citations to date. His 2017 exploration of mixed-criticality 3D ToF systems further underscores his commitment to safety-critical design principles in automated driving. More recent contributions address practical challenges such as precise multi-sensor timestamping and context-aware sensor adaptation, reflecting a maturing research agenda aimed at real-world deployment. Across his body of work, Steinbaeck has established himself as a methodical and practically oriented contributor to the autonomous vehicle perception community.