State-of-the-Art Sensor Models for Virtual Testing of Advanced Driver Assistance Systems/Autonomous Driving Functions

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Authors Abstract
Content
Sensor models are essential for virtual testing of Advanced Driver Assistance Systems/Autonomous Driving (ADAS/AD) functions. This article gives an overview of the state-of-the-art of ADAS/AD sensor models. The considered sensors are radar, lidar, and camera. To get a common understanding and a common language in sensor model research, a new classification method into low-, medium-, and high-fidelity sensor models is introduced. Low-fidelity sensor models are based on geometrical aspects like the Field Of View (FOV) of the sensor and object positions in the virtual environment. Object lists are used as input and output data formats. Medium-fidelity sensor models consider the detection probability and physical aspects in addition to geometrical aspects of the sensor. They have object lists as input and object lists or raw data as output. High-fidelity sensor models are based on rendering techniques. They have the virtual three-dimensional (3D) environment provided by the environment simulation as an input and sensor raw data as an output. The classification is useful for virtual testing of ADAS/AD functions since the classes can be correlated to the phases of the Systems Development Process (SDP) of ADAS/AD.
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DOI
https://doi.org/10.4271/12-03-03-0018
Pages
55
Citation
Schlager, B., Muckenhuber, S., Schmidt, S., Holzer, H. et al., "State-of-the-Art Sensor Models for Virtual Testing of Advanced Driver Assistance Systems/Autonomous Driving Functions," SAE Int. J. CAV. 3(3):233-261, 2020, https://doi.org/10.4271/12-03-03-0018.
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Publisher
Published
Oct 29, 2020
Product Code
12-03-03-0018
Content Type
Journal Article
Language
English