The safety of autonomous driving systems (ADS) is of paramount importance and heavily relies on precise sensors and robust perception algorithms. However, these are often prone to adverse weather conditions such as rain, fog and snow, which significantly affect the sensor signals and, consequently, the perception module's understanding of the environment. Compared to clear weather data, collecting driving data in harsh weather such as rainy and foggy days is costly and challenging. This paper proposes a novel method for synthetic Time-of-Flight (ToF) Light Detection and Ranging (LiDAR) point cloud generation under rainy and foggy weathers using a Monte Carlo-based signal-environment interaction simulation. By establishing a physical model of laser interaction with particles in adverse environment and conducting multiple random simulations, synthetic point clouds are generated from the existing clear weather dataset accordingly. Experimental results demonstrate promising effectiveness and application prospects in the field point cloud data augmentation in uncommon weather, which fosters the tailored capability to improve the robustness of perception algorithms through training thereby enhancing the safety of autonomous driving systems.


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    Titel :

    Synthetic Point Clouds Generation for ToF LiDAR Under Rainy and Foggy Weathers Using Monte Carlo Based Signal-Environment Interaction Simulation


    Beteiligte:
    Zeng, Zifan (Autor:in) / Wang, Peng (Autor:in) / Zhang, Qunli (Autor:in) / Liu, Shiming (Autor:in) / Liu, Feng (Autor:in) / Feng, Bin (Autor:in)


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    2523543 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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