The need for simulated data in autonomous driving applications has become increasingly important, both for validation of pretrained models and for training new models. In order for these models to generalize to real-world applications, it is critical that the underlying dataset contains a variety of driving scenarios and that simulated sensor readings closely mimics real-world sensors. We present the Carla Automated Dataset Extraction Tool (CADET), a novel tool for generating training data from the CARLA simulator to be used in autonomous driving research. The tool is able to export high-quality, synchronized LIDAR and camera data with object annotations, and offers configuration to accurately reflect a real-life sensor array. Furthermore, we use this tool to generate a dataset consisting of 10 000 samples and use this dataset in order to train the 3D object detection network AVOD-FPN, with finetuning on the KITTI dataset in order to evaluate the potential for effective pretraining. We also present two novel LIDAR feature map configurations in Bird's Eye View for use with AVOD-FPN that can be easily modified. These configurations are tested on the KITTI and CADET datasets in order to evaluate their performance as well as the usability of the simulated dataset for pretraining. Although insufficient to fully replace the use of real world data, and generally not able to exceed the performance of systems fully trained on real data, our results indicate that simulated data can considerably reduce the amount of training on real data required to achieve satisfactory levels of accuracy.
翻译:在自主驾驶应用方面,对模拟数据的需求已变得越来越重要,这既是为了验证预先培训的模型,也是为了培训新模型。为了使这些模型能够向现实世界应用推广,至关重要的是,基础数据集必须包含各种驱动情景,模拟传感器读数密切模仿现实世界传感器。我们介绍了卡拉自动数据集提取工具(CADET),这是从CARLA模拟器生成培训数据的新工具,用于自主驾驶研究。该工具能够输出高质量、同步的LIDAR和带有目标说明的相机数据,并提供配置以准确反映真实生活传感器阵列。此外,我们利用这一工具生成由10 000个样本组成的数据集,并使用该数据集来训练3D天天天体探测网络AVOD-FPN,同时对KITTI数据集进行微调,以评价有效预先培训的潜力。我们还在Bird Eye VEVOD-FN 中提出了两种新型LIDAR特征地图配置,供使用,可以很容易地反映真实生活传感器阵列的准确性数据。这些配置虽然在经过充分测试后,无法充分进行数据升级,因此无法充分使用。