3D object detection is the task of locating and classifying objects in a 3D space. The task of 3d object detection is accompanied by the problem that objects are often detected while they are reversing, introducing a directional error of 180 degrees. This has a negative impact on following tasks of tracking and motion forecasting. One approach to solving this problem is by using a high definition (HD) map based on the information that cars drive along lanes; however, in its current form, this method does not fully utilize the given lane information and remains incapable of solving the problem of objects reversing. We propose a 3D object detection framework (”LaneFusion”) employing LiDAR and HD map fusion, using a vector map. LaneFusion overcomes the problem that the vector map format is difficult to input into current mainstream convolutional neural networks (CNNs), through a two-step rasterization process that incorporates vector map features into existing LiDAR-based detection methods. Our experiments confirmed that the proposed method increased the 3D average precision (AP) and average orientation similarity (AOS) of the vehicle class by up to 6.56 and 10.65 points, respectively. In addition, we analyzed the performance degradation caused by map input errors due to self-localization estimation and deviations from real road conditions. The proposed method was found to be more sensitive to orientation errors than to translation errors in self-localization, yet robust to the unavailability of map information by dropout during training.


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    Title :

    LaneFusion: 3D Object Detection with Rasterized Lane Map


    Contributors:


    Publication date :

    2022-06-05


    Size :

    2752023 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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