Abstract
Intelligent vehicles use surround sensors which perceive their environment and therefore enable automatic vehicle control. As already small errors in sensor data measurement and interpretation could lead to severe accidents, future object detection algorithms must function safely and reliably. However, adverse weather conditions, illustrated here using the example of rain, attenuate the sensor signals and thus limit sensor performance. The indoor rain simulation facility at CARISSMA enables reproducible measurements of predefined scenarios under varying conditions of rain. This simulator is used to systematically investigate the effects of rain on camera, lidar, and radar sensor data. This paper aims at (1) comparing the performance of simple object detection algorithms under clear weather conditions, (2) visualizing/discussing the direct negative effects of the same algorithms under adverse weather conditions, and (3) summarizing the identified challenges and pointing out future work.
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Acknowledgment
We applied the SDC approach for the sequence of authors. The authors would like to thank the master students Al-Bahr Ayad Ameen Sadeq, Altinbas Selim, Intriz Ercan, Ladva Ronak Madhavji, Malaviya Ujval Jaysukhbhai, and Nguyen Huu Anh Huy for implementing the detection algorithms and analyzing the influences on object-level. This work is supported under the FH-Impuls program of the German Federal Ministry of Education and Research (BMBF) under Grant No. 13FH7I01IA.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Hasirlioglu, S., Riener, A. (2019). Challenges in Object Detection Under Rainy Weather Conditions. In: Ferreira, J., Martins, A., Monteiro, V. (eds) Intelligent Transport Systems, From Research and Development to the Market Uptake. INTSYS 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 267. Springer, Cham. https://doi.org/10.1007/978-3-030-14757-0_5
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DOI: https://doi.org/10.1007/978-3-030-14757-0_5
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