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A temporal filter approach for real-time detection and reconstruction of curbs and road surfaces from 3D point clouds is presented. Instead of local thresholding, as used in many other approaches, a 3D curb model is extracted from the point cloud. The 3D points are classified to different parts of the model (i.e. road and sidewalk) using a temporally integrated Conditional Random Field (CRF). The parameters of curb and road surface are then estimated from the respectively assigned points, providing a temporal connection via a Kalman filter.In this contribution, we employ dense stereo vision for data acquisition. Other sensors capturing point cloud data, e.g. lidar, would also be suitable.The system was tested on real-world scenarios, showing the advantages over a temporally unfiltered version, due to robustness, accuracy and computation time. Further, the lateral accuracy of the system is evaluated. The experiments show the system to yield highly accurate results, for curved and straightline curbs, up to distances of 20 meters from the camera.

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This article presents a practical solution for fast and precise localization of a vehicle's position and orientation with respect to stop sign controlled intersections based on video sequences and mapped data. It consists of two steps. First, an intersection map is generated offline based on street-level imagery and GPS data, collected by a vehicle driving through an intersection from different directions. The map contains both landmarks for localization and information about stop line positions. This information is used in the second step to precisely and efficiently derive a vehicle's pose in real-time when approaching a mapped intersection. At this point, we only need coarse GPS information to be able to load the proper map data.The experimental results show this approach is able to successfully localize a vehicle at stop intersections with decimeter accuracy under various weather conditions and after several months between the map data collection and the test drive.The proposed method enables a variety of functions for future driver assistance systems, such as stop line violation warning, advanced Adaptive Cruise Control/ Traffic Jam Assist functions in cities, or autonomous driving.

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