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High definition (HD) maps can fail by becoming outdated. To still use them safely for automated driving, they need to be verified or updated, both requiring methods for change detection. We propose two significant improvements for HD map change detection that do not require a highly accurate localization prior as localization quickly fails or cannot be trusted in an outdated map. Given a very coarse localization prior, we group stored or measured map features in spatially and semantically separable feature groups. These feature groups are not only intuitive, like the sequence of leftmost dashed lane markings, but changes are also highly correlated within them. The first contribution improves the way internal consistency of each feature group is assured by using boosted classification trees. Additionally, a mutual evaluation scheme is added for all seemingly unchanged feature groups. Always one feature group is used for localization by feature alignment while each other group's alignment is checked for compatibility. Two voting schemes are presented that allow a more or less sensitive change detection on the level of proposed groups. In contrast to almost all other approaches, our approach allows to use still valid parts of the map for automated driving and to update the changed parts. We evaluate our approach on a previously published map verification dataset [1], showing that the number of undetected map changes can be reduced by up to 31 % compared to state of the art using boosted classification trees, at the same time reducing false positive rates by up to 50 %. The additional mutual evaluation step is able to uncover a whole category of previously undetectable changes and reduces undetected changes by an extra 15 %. |
