Efficient 3D Object Detection Models and Evaluation Method for Autonomous Driving

Recently, due to the increasing interest in autonomous driving, many researchers have been focused on Open Source Software (OSS) such as the Autoware platform, which enables full-stack autonomous driving. As a research group dedicated to the development of autonomous driving software platforms, we are particularly focused on the development of 3D object detection models that take into account real-time performance as well as accuracy. In this paper, we propose a VariFocal-based CenterPoint model and an adaptive shape estimation method combined with Apollo segmentation to improve the performance of the 3D object detection module included in the Autoware platform. Additionally, we present a Bird's Eye View (BEV) metric-based quantitative evaluation method to compare the performance of different models according to the dataset. By means of the evaluation method, we demonstrate the experimental results of the performance between the 3D object detection modules of the Autoware platform and our models on both our own dataset and a public dataset. Furthermore, since these models will be used for autonomous driving platforms where real-time processing is a crucial factor, we also perform experimental results on the processing time by converting the models to TensorRT. As shown in the experimental results, the improved models trained on a large amount of self-constructed datasets resolve issues such as missed detection of large vehicles and small objects like motorcycles and pedestrians compared to the previous models. As a result, VF-CenterPoint performed 13.76 mAP better than the previous model, and A-Shape Estimation method achieved 9.7 AP higher performance than the previous method.