As a critical sensor for high-level autonomous vehicles, LiDAR’s limitations in adverse weather (e.g. rain, fog, snow, etc.) impede the deployment of self-driving cars in all weather conditions. However, studies in literature on LiDAR’s performance in harsh conditions are insufficient. In this paper, based on a dataset collected with a popular Near-InfraRed (NIR) ToF LiDAR in a well-controlled artificial fog chamber, we statistically model the LiDAR ranging process in fog conditions through a data-driven approach. Specifically, giving an object at a known distance, our model is able to predict LiDAR measures (range and intensity) under various fog conditions. For a transmitted laser under fog, we first model and predict the minimum visibility required to detect its true range or not. Then, the noisy range and intensity measures are sampled from the probabilistic measurement distributions inferred from the dataset. The performance of the proposed method has been quantitatively and qualitatively evaluated. Experimental results show that our approach can provide a promising performance prediction of the utilized NIR ToF LiDAR under fog, which opens a new gate to the quantitative assessment of adverse weather and contributes to the specification of relevant Operational Domain Designs (ODDs). The developed ROS package is available at:https://github.com/cavayangtao/lanoising.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Performance Modeling a Near-Infrared ToF LiDAR Under Fog: A Data-Driven Approach


    Contributors:
    Yang, Tao (author) / Li, You (author) / Ruichek, Yassine (author) / Yan, Zhi (author)


    Publication date :

    2022-08-01


    Size :

    2270494 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Polarized Lidar Reflectance Measurements of Vegetation at Near-Infrared and Green Wavelengths

    Tan, S. / Narayanan, R. M. / Shetty, S. K. | British Library Online Contents | 2005


    A lidar-based approach for near range lane detection

    von Reyher, A. / Joos, A. / Winner, H. | IEEE | 2005


    A Lidar-Based Approach for Near Range Lane Detection

    Von Reyher, A. / Joos, A. / Winner, H. et al. | British Library Conference Proceedings | 2005


    Picosecond mid-infrared LIDAR system

    Geiser, P. / Wilier, U. / Schade, W. | IEEE | 2005


    Data-Driven Modeling Approach for Mistuned Cyclic Structures

    Kelly, Sean T. / Lupini, Andrea / Epureanu, Bogdan I. | AIAA | 2021