High Definition (HD) maps are becoming key elements of the autonomous driving because they can provide information about the surrounding environment of the autonomous car without being affected by the real-time perception limit. To provide the most recent environmental information to the autonomous driving system, the HD map must maintain up-to-date data by updating changes in the real world. This paper presents a simultaneous localization and map change update (SLAMCU) algorithm to detect and update the HD map changes. A Dempster–Shafer evidence theory is applied to infer the HD map changes based on the evaluation of the HD map feature existence. A Rao–Blackwellized particle filter (RBPF) approach is used to concurrently estimate the vehicle position and update the new map state. The detected and updated map changes by the SLAMCU are reported to the HD map database in order to reflect the changes to the HD map and share the changing information with the other autonomous cars. The SLAMCU was evaluated through experiments using the HD map of traffic signs in the real traffic conditions.

A High-Definition map (HD map) is a precise and detailed map composed of various landmark feature layers. The HD map is a core technology that facilitates the essential functions of intelligent vehicles. Recently, it has come to be required for the HD map to continuously add new feature layers in order to increase the performances of intelligent vehicles in more complicated environments. However, it is difficult to generate a new feature layer for the HD map, because the conventional method of generating the HD map based on several professional mapping cars has high costs in terms of time and money due to the need to re-drive on all of the public roads. In order to reduce these costs, we propose a crowd-sourced mapping process of the new feature layer for the HD map. This process is composed of two steps. First, new features in the environments are acquired from multiple intelligent vehicles. The acquired new features build each new feature layer in each intelligent vehicle using the HD map-based GraphSLAM approach, and these new feature layers are conveyed to a map cloud through a mobile network system. Next, the crowd-sourced new feature layers are integrated into a new feature layer in a map cloud. In the simulation, the performance of the crowd-sourced process is then analyzed and evaluated. Experiments in real driving environments confirm the results of the simulation.

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This paper presents a construction process of the three-dimensional roadway geometry map for an autonomous driving system. The presented process focuses on the post-processing and the three-dimensional roadway geometry modelling algorithms. The post-processing algorithm refines the raw Global Positioning System position data by combining the novel three-dimensional roadway geometry model which originated from the construction engineering of roadways and vehicle motion data from onboard sensors using a Rauch–Tung–Striebel smoother. The three-dimensional geometry modelling algorithm approximates the road geometry information described in three-dimensional point clouds into a mathematical curve model based on the B-spline. An adaptive curve refinement method using dominant points was applied to the road modelling algorithm. This dominant-point-based refinement method can reduce the number of knots and the number of control points of the B-spline road model while maintaining the desired accuracy of the roadway map. Also, since the dominant-point refinement method considers a road shape factor, such as the curvature and the arc length, for the road modelling, it is more efficient than the previous B-spline road modelling algorithms. The proposed map generation algorithm was verified and evaluated through experiments in various test conditions. The experimental results show that the presented construction process of the three-dimensional roadway geometry map can provide sufficient accuracy, reliability and efficiency for applications of autonomous driving systems in comparison with those of other roadway map construction processes.