Map Matching in Road Crossings of Urban Canyons Based on Road Traverses and Linear Heading-Change Model

The objective of this paper is to find a robust and sustainable positioning approach in Hong Kong, where the Global Positioning System (GPS) signal is frequently blocked by high buildings, traffic tunnels, etc. After evaluating several kinds of complementary positioning means, the GPS/dead-reckoning (DR)/map-matching (MM) integrated positioning technology is selected. During navigation, if the GPS signal is good enough, the GPS/DR/MM mode will be used; otherwise, the DR/MM mode will be used, and the MM algorithm will correct the DR system in a timely fashion. Generally, if the vehicle is running in a straight road, heading-related parameters in the DR system will be corrected, and if the vehicle makes a turn, both distance- and heading-related parameters will be corrected. Unfortunately, existing MM algorithms show poor performance near a road crossing or in a dense road network. To resolve this problem, based on road traverses and a linear heading-change model, an MM algorithm that considers historical information is proposed. First, a unified point-based MM calculating framework is presented, and the disadvantages of several kinds of existing MM algorithms are also given. After that, a single-line-single-direction road network model is presented, and the MM algorithm is always executed on road traverses instead of a single road so that historical information can be easily kept. By adopting the linear heading-change model, the problem of no analytical solution in the MM that considers heading information is obviated, and the MM ambiguities in road crossings can be greatly removed. Finally, an onsite MM test in Hong Kong is presented, and the test proves that the proposed MM algorithm provides long-time robust positioning performance.

[1]  Tae-Kyung Sung,et al.  Development of a map matching method using the multiple hypothesis technique , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[2]  M. Barth,et al.  Lane-level positioning for in-vehicle navigation and automated vehicle location (AVL) systems , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[3]  H. Sahli,et al.  Multiple model estimation scheme for map-matching , 2002, Proceedings. The IEEE 5th International Conference on Intelligent Transportation Systems.

[4]  Miki Haseyama,et al.  A Map Matching Method with the Innovation of the Kalman Filtering (Special Section of Letters Selected from the 1996 IEICE General Conference) , 1996 .

[5]  M. Fu,et al.  A hybrid map matching algorithm based on fuzzy comprehensive judgment , 2004, Proceedings. The 7th International IEEE Conference on Intelligent Transportation Systems (IEEE Cat. No.04TH8749).

[6]  Qing Wang,et al.  The relationship among vehicle positioning performance, map quality, and sensitivities and feasibilities of map-matching algorithms , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[7]  Gyu-In Jee,et al.  Efficient use of digital road map in various positioning for ITS , 2000, IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062).

[8]  Li Zhihua,et al.  A new approach to map-matching and parameter correcting for vehicle navigation system in the area of shadow of GPS signal , 2005, Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005..

[9]  Yilin Zhao,et al.  Standardization of mobile phone positioning for 3G systems , 2002, IEEE Commun. Mag..

[10]  N.S.T. Lee.  Single line street network: The foundation of Mobile GIS , 1993, Proceedings of VNIS '93 - Vehicle Navigation and Information Systems Conference.

[11]  David Bernstein,et al.  Some map matching algorithms for personal navigation assistants , 2000 .

[12]  Geoffrey Blewitt,et al.  Mapping Dilution of Precision (MDOP) and map-matched GPS , 2002, Int. J. Geogr. Inf. Sci..

[13]  Jong-Hwan Kim,et al.  Q-factor map matching method using adaptive fuzzy network , 1999, FUZZ-IEEE'99. 1999 IEEE International Fuzzy Systems. Conference Proceedings (Cat. No.99CH36315).

[14]  S. Syed,et al.  Fuzzy Logic Based-Map Matching Algorithm for Vehicle Navigation System in Urban Canyons , 2004 .

[15]  Yan Lei,et al.  GpsOne: A new solution to vehicle navigation , 2004, PLANS 2004. Position Location and Navigation Symposium (IEEE Cat. No.04CH37556).

[16]  Yilin Zhao,et al.  Vehicle Location And Navigation Systems , 1997 .

[17]  Ca Scott.  Improved positioning of motor vehicles through secondary information sources , 1996 .

[18]  Maan El Badaoui El Najjar,et al.  A Road-Matching Method for Precise Vehicle Localization Using Belief Theory and Kalman Filtering , 2005, Auton. Robots.

[19]  Yilin Zhao,et al.  Mobile phone location determination and its impact on intelligent transportation systems , 2000, IEEE Trans. Intell. Transp. Syst..