Traffic Sign Localization and Orientation Classification for Automated Map Updating

High Definition (HD) maps, containing detailed road information, are essential for autonomous driving and many geo-related tasks. Recent developments in computer vision make it possible to automate the labor-intensive HD map maintenance work, such as localizing traffic signs within a road network. However, updating traffic signs to HD maps is non-trivial, as it not only requires precise geo-location but also requires confirming whether a sign belongs to a specific road. In our work, we develop an end-to-end automated traffic sign update system, termed AutoTS, which is capable of using an image sequence collected during vehicle operation to extract the geo-location of a traffic sign and determine whether it belongs to the road driven on, from its orientation. In AutoTS, we design a noise and sparsity adaptive localization module, which can filter noisy location points and derive a geo-location from sparse location points. To identify the orientation of traffic signs, we devise a position-aware orientation classification module, which uses the ROI feature and the position-aware SIFT feature to explore the orientation characteristic and understand the road context. To facilitate the evaluation of the proposed method, we construct a traffic sign localization and orientation classification benchmark, KITTI-TS. Our AutoTS achieves an MAE of 2.38 meters in traffic sign localization, while the accuracy in orientation classification reaches 88.89%.