Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setup used for data acquisition. This bias can seriously impair the perception models’ transferability to new sensor setups, which continuously occur due to the market's competitive nature. We envision sensor data abstraction as an interface between sensor data and machine learning applications for highly automated vehicles (HAD). For this purpose, we review the primary sensor modalities, camera, lidar, and radar, published in autonomous-driving related datasets, examine single sensor abstraction and abstraction of sensor setups, and identify critical paths towards an abstraction of sensor data from multiple perception configurations.
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setup used for data acquisition. This bias can seriously impair the perception models’ transferability to new sensor setups, which continuously occur due to the market's competitive nature. We envision sensor data abstraction as an interface between sensor data and machine learning applications for highly automated vehicles (HAD). For this purpose, we review the primary sensor modalities, camera, lidar, and radar, published in autonomous-driving related datasets, examine single sensor abstraction and abstraction of sensor setups, and identify critical paths towards an abstraction of sensor data from multiple perception configurations.
Towards Sensor Data Abstraction of Autonomous Vehicle Perception Systems
Reichert, Hannes (Autor:in) / Lang, Lukas (Autor:in) / Rosch, Kevin (Autor:in) / Bogdoll, Daniel (Autor:in) / Doll, Konrad (Autor:in) / Sick, Bernhard (Autor:in) / Rellss, Hans-Christian (Autor:in) / Stiller, Christoph (Autor:in) / Zollner, J. Marius (Autor:in)
07.09.2021
3207521 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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