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Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position(More)
This paper presents a precise localization method for autonomous driving systems by correcting the GPS bias error. Since GPS errors have systematic noise properties that change slowly with time, a stand-alone GPS cannot be used for localization of an autonomous vehicle. To compensate for this systematic bias error, several types of additional sources of(More)
A vehicle localization system can be extremely useful for intelligent transformation systems (ITS) such as advanced driver assistance systems (ADASs), emergency vehicle notification systems, and collision avoidance systems. To optimize the performance of vehicle localization systems, localization algorithms that analyze multi-sensor data processed using a(More)
This paper proposes a map generation algorithm for a precise roadway map designed for autonomous cars. The roadway map generation algorithm is composed of three steps, namely, data acquisition, data processing, and road modeling. In the data acquisition step, raw trajectory and motion data for map generation are acquired through exploration using a probe(More)
This paper proposes a distributed vehicle state estimation system to improve the performance of vehicle positioning using Global Positioning System (GPS) and in-vehicle sensor components. The distributed architecture of the estimation system can reduce the computational complexity of high-order estimation by dividing it into several small-order estimation(More)
This paper presents a novel curvilinear-coordinate-based approach to improve object and situation assessment performance for highly automated vehicles under various curved road conditions. The approach integrates object information from radars and lane information from a camera with three steps: track-to-track fusion, curvilinear coordinate conversion, and(More)
This paper proposes a road-slope estimation algorithm to improve the performance and efficiency of intelligent vehicles. The algorithm integrates three types of road-slope measurements from a GPS receiver, automotive onboard sensors, and a longitudinal vehicle model. The measurement integration is achieved through a probabilistic data association filter(More)
This paper proposes a road slope aided position estimation algorithm based on the fusion of GPS data with information from automotive onboard sensors. Many previous studies for position estimation did not consider the effect of road slope, although many sloped roads are existing. In order to analyze the influence of road slope on position estimation,(More)