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    • Signal processing pipeline for an autonomous electrical race car

    Martina Scheffler, Ole Kettern, Oliver Zbaranski, Finn Schäfer, Kevin Schmidt, Bjarne Eberhardt, Stefan Werling

    Kapitel/Beitrag aus dem Buch: Längle T. & Heizmann M. 2022. Forum Bildverarbeitung 2022.

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    This work presents a signal processing pipeline for an autonomous race car in the context of Formula Student. The software used for each step from the detection of objects in camera images or lidar point clouds to the calculation of control outputs for the actuators is described in detail. The sensors and actuators are covered and the system output is visualized. The computational times of the pipeline are analyzed and it is derived that the complex algorithms used for motion planning and SLAM take up the most of the computation times, leaving the most room for improvements.

    Fächer:

    Electrical engineering 

    Schlagwörter:

    MPC SLAM object detection YOLO Formula Student signal processing Autonomous driving 

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    Empfohlene Zitierweise für das Kapitel/den Beitrag
    Scheffler, M et al. 2022. Signal processing pipeline for an autonomous electrical race car. In: Längle T. & Heizmann M (eds.), Forum Bildverarbeitung 2022. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.58895/ksp/1000150865-15
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    This chapter distributed under the terms of the Creative Commons Attribution + ShareAlike 4.0 license. Copyright is retained by the author(s)

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    Veröffentlicht am 25. November 2022

    DOI
    https://doi.org/10.58895/ksp/1000150865-15