The main role of monocular vision based 3D object detection in intelligent vehicles is to extract obstacle information from the environment, including the category of object, location and orientation. Utilizing monocular cameras for this purpose has several advantages, such as being cost-effective and easily accessible compared to other types of sensors. The realization of 3D object detection through monocular vision alone has considerable potential for both commercial and research purposes. In the context of intelligent vehicles, perception systems play a key role by accurately assessing the surrounding environmental conditions. These systems generate reliable observations, which are essential for tasks such as prediction and planning. Within this framework, 3D object detection is a key feature that allows systems to predict the location, size and class of important 3D objects in the vicinity of intelligent vehicles. This chapter of the book is dedicated to this task. In order to address the different aspects of accuracy and robustness, two approaches, FANet and AMNet, are introduced.


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    Title :

    Robust Environmental Perception of Monocular 3D Object Detection


    Additional title:

    Recent Advancements in Connected Autonomous Vehicle Technologies


    Contributors:
    Pan, Huihui (author) / Wang, Jue (author) / Yu, Xinghu (author) / Sun, Weichao (author) / Gao, Huijun (author)


    Publication date :

    2023-11-26


    Size :

    41 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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