Artificial intelligence for autonomous driving must meet strict requirements on safety and robustness. We propose to validate machine learning models for self-driving vehicles not only with given ground truth labels, but also with additional a-priori knowledge. In particular, we suggest to validate the drivable area in semantic segmentation masks using given street map data. We present first results, which indicate that prediction errors can be uncovered by map-based validation.
翻译:自主驾驶的人工智能必须满足安全和稳健的严格要求。我们提议验证自驾驶车辆的机器学习模式,不仅使用给定的地面真实标签,而且还使用额外的优先知识。特别是,我们建议使用给定的街道地图数据验证语义分割面罩中的可操作区域。我们提出了初步结果,表明预测错误可以通过基于地图的验证来发现。