In real-world autonomous driving, deep learning models can experience performance degradation due to distributional shifts between the training data and the driving conditions encountered. As is typical in machine learning, it is difficult to acquire a large and potentially representative labeled test set to validate models in preparation for deployment in the wild. In this work, we introduce complementary learning, where we use learned characteristics from different training paradigms to detect model errors. We demonstrate our approach by learning semantic and predictive motion labels in point clouds in a supervised and self-supervised manner and detect and classify model discrepancies subsequently. We perform a large-scale qualitative analysis and present LidarCODA, the first dataset with labeled anomalies in lidar point clouds, for an extensive quantitative analysis.

翻译：在现实世界的自动驾驶中，深度学习模型可能因训练数据与所遇驾驶条件之间的分布偏移而出现性能下降。正如机器学习中的典型情况，很难获取一个大规模且具有潜在代表性的标注测试集来验证模型，以准备在真实场景中部署。在本工作中，我们提出互补学习方法，利用从不同训练范式中学习到的特征来检测模型错误。我们通过以监督和自监督方式学习点云中的语义和预测运动标签来展示该方法，并随后检测和分类模型差异。我们进行了大规模定性分析，并提出了LidarCODA——首个包含激光雷达点云标注异常的数据集，用于广泛的定量分析。