Intelligent vehicles use surround sensors to perceive their local environment. However, already small errors in sensor data measurement and interpretation can lead to severe accidents, thus, testing the reliability of sensors and safety systems before market introduction is of high importance. The downside of real-world testing is that the increasing complexity of safety systems results in increased test effort and, as a consequence, longer development time. To counteract, simulation have to play a central role in future development and test of automated driving functions. It is common knowledge that adverse weather conditions such as rain degrade a sensor's performance due to absorption and scattering by water particles in the air. As a result, the effects of rain on sensor raw data can lead to incorrect environmental perception. This behavior is highly random but still needs to be replicated in the simulation environment to enable virtual tests with a high degree of realism. This work gives an overview on the related work, rain physics, and basic sensor theory. Further, we present a novel approach for simulating the effects of rain on data from camera, Iidar, and radar sensors.


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    Titel :

    A Model-Based Approach to Simulate Rain Effects on Automotive Surround Sensor Data


    Beteiligte:


    Erscheinungsdatum :

    01.11.2018


    Format / Umfang :

    527118 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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