Sensor Fusion-based Online Map Validation for Autonomous Driving

High-Definition (HD) Maps are indispensable components of an autonomous vehicle software stack, containing a precise representation of the static surroundings. Prediction, motion planning and vehicle behavior heavily rely on the accuracy of the HD Map. However, a key problem in the mapping process is that the environment itself changes over time, leading to inconsistencies between the real world and the outdated knowledge in the HD Map. Therefore, validating the HD Map features becomes a deciding factor in the accuracy and safety of a self-driving vehicle. Intuitively, the validation can be done by correlating the map information with the acquired sensor measurements. Although, individual sensors are subject to errors, integrating the measurements from various sensor creates a more accurate representation of the vehicle surroundings which subsequently can be used for a more reliable map validation mechanism. In this paper, we propose such a real-time method to validate the HD map by using as input more accurate estimations provided by sensor fusion. The proposed solution is decomposing the high dimensional map validation problem into multiple independent one-dimensional estimators that are able to provide validity probabilities for every independent map feature, i.e., lines, curbs and lane markings.