Towards autonomous self-assessment of digital maps

Digital maps are becoming increasingly important for driver assistance systems: providing optimal lighting conditions in night scenarios, presenting the road geometry to the driver, or for usage in autonomous driving tasks. However, recorded digital maps own one drawback: due to road changes and inaccurate recordings, discrepancies between the map and the real world exist. Because these discrepancies can lead to severe application level failures, detection of map errors is essential to ensure overall system integrity. This work proposes a new approach to online verification of digital maps for automotive usage. In contrast to previous work, the described system is able to detect errors in front of the vehicle. On the basis of a large database of map geometry and sensor information, a neural network is trained to classify the digital map integrity by optimally fusing different information sources depending on their strength and reliability. Although generally applicable, it is shown that a combination of orthogonal measurement principles is greatly beneficial for this decision task. A radar sensor, infra-red imagery and road geometry information estimated from visible light images are employed as input for the neural fusion. Experiments on real-world data verify the proposed concepts.