Prototype design and implementation for urban area in-car navigation system

Xu, Aigong; Yang, Dongyuan; Cao, Feng; Xiao, W.D.; Law, Choi Look; Ling, Keck Voon; Chua, Huey Eng

doi:10.1109/itsc.2002.1041271

Cited by 3 publications

(1 citation statement)

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“…Greenfeld (2002) also suggests that additional research is required to verify the accurate performance of the algorithm and to make an accurate position determination on a given road segment. Xu et al (2002) proposed a new MM method based on intersection information stored in a map database. The method identified whether the vehicle is near intersections or between intersections or whether a turn is detected using DR sensors.…”

Section: Literature Reviewmentioning

confidence: 99%

Map-Matching in Complex Urban Road Networks

Ochieng¹,

Quddus²,

Noland³

2009

Rev. Bras. Cartogr.

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Global Navigation Satellite Systems (GNSS) such as GPS and digital road maps can be used for land vehicle navigation systems. However, GPS requires a level of augmentation with other navigation sensors and systems such as Dead Reckoning (DR) devices, in order to achieve the required navigation performance (RNP) in some areas such as urban canyons, streets with dense tree cover, and tunnels. One of the common solutions is to integrate GPS with DR by employing a Kalman Filter (Zhao et al., 2003). The integrated navigation systems usually rely on various types of sensors. Even with very good sensor calibration and sensor fusion technologies, inaccuracies in the positioning sensors are often inevitable. There are also errors associated with spatial road network data. This paper develops an improved probabilistic Map Matching (MM) algorithm to reconcile inaccurate locational data with inaccurate digital road network data. The basic characteristics of the algorithm take into account the error sources associated with the positioning sensors, the historical trajectory of the vehicle, topological information on the road network (e.g., connectivity and orientation of links), and the heading and speed information of the vehicle. This then enables a precise identification of the correct link on which the vehicle is travelling. An optimal estimation technique to determine the vehicle position on the link has also been developed and is described. Positioning data was obtained from a comprehensive field test carried out in Central London. The algorithm was tested on a complex urban road network with a high resolution digital road map. The performance of the algorithm was found to be very good for different traffic maneuvers and a significant improvement over using just an integrated GPS/DR solution.

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Section: Literature Reviewmentioning

confidence: 99%