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Cloud-aided SDR solution for lane-specific vehicle positioning via Local Interference Compensation

The steadily increasing traffic density is causing enormous negative effects such as jams, accidents or CO2 emissions, especially in urban areas. In Europe more than 12% of the traffic network is daily congested. Hereby a reliable and dynamic congestion forecast is the key to avoid and/or compensate such locale bottleneck situations. Hence, this contribution focuses on a cloud-aided, lane-specific position determination of vehicles using a so-called Local Interference Compensation to enable a more detailed and lane-accurate traffic prediction (e.g. detecting short-dated roadworks or car breakdowns), and by that traffic-flow manipulation in the future. Thereby, the scientific challenge is to predict, quantify and compensate the inevitable local impacts to the positioning accuracy when using ordinary GNSS receivers. Beneath the detailed explanation of the LOCATe system itself, this contribution will also provide quantitative experimental validation and performance evaluation tests using an Advanced Software-Defined GNSS Receiver solution on geo-referenced points, which fit the definition of the introduced urban canyons.