Data processing: vehicles – navigation – and relative location – Navigation – Employing position determining equipment
Reexamination Certificate
2000-08-18
2002-03-26
Cuchlinski, Jr., William A. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Navigation
Employing position determining equipment
C340S988000, C342S357490
Reexamination Certificate
active
06363320
ABSTRACT:
BACKGROUND
The present invention relates to computer-based locating and tracking of objects such as vehicles, vessels, aircraft, bicycles, animals, and containers.
Automatic Vehicle Location System (AVL) has been a known technology since the completion of the NAVSTAR Global Positioning System (GPS) by the US Department of Defense. A typical AVL consists of (1) one or more mobile units, (2) one or more vehicle monitoring stations, and (3) a wireless communication network. A mobile unit is a piece of hardware with a GPS receiver and a wireless transmitter installed in a vehicle. A vehicle monitoring station has the computer equipment to process GPS data and monitor vehicle locations. The wireless communication network is used to send vehicle GPS data from a mobile unit to a monitoring station.
Location information of any object can be obtained from a GPS receiver or other terrestrial location-detection device. The location information is typically represented by either latitude/longitude (denoted as lat/long in the GIS and mapping industry), or a pair of x and y coordinates using any local referencing system (denoted as x-y). In addition to lat/long or x-y, additional information about any object can include speed, direction of movement if the object is moving, and elevation. Furthermore, each location record can be associated with the time and date the object location was recorded.
The location information obtained from the object is typically transmitted through a wireless communication network to a centralized location for data processing. Such a location is usually called the AVL Host, Data Processing Center, or Data Processing Station, where AVL stands for Automatic Vehicle Location system. A Host and one or more Mobile Units are equipped with the location-detection device. The usual systems of the prior art handle location information by displaying the location of the Mobile Unit on a digital map in the same coordinate system (lat/long or x-y) as a dot. An operator interprets the location by referencing surrounding features on the map, such as streets, landmarks, buildings, parks, etc. Translation of the point location of the object into a more useful expression of map location is thus processed manually by the operator.
Major limitations of the prior art include:
(1) The Host computer is installed with mapping software to display both the object location and the reference map.
(2) An operator reads the map and translates the location information into other descriptive forms.
(3) If a user of the service desires to know the location of the object being tracked, the operator may verbally describe the location information to the user, or send the user a text file describing the location, or send the user a map in either digital form or hard copy map.
(4) If the user intends to retrieve the location information with a thin client receiver, the choices are quite limited. First, if the user has an analog cellular phone, then the operator must describe the location to the user by voice communication. In this case, sending a text file or a digital map is not an option. A hard copy map is out of question. Second, if the user has a computer of any kind with a modem or Internet connection, then the operator can either send a text file describing the location, or send a digital map showing the location of the object against the referencing map. Third, if the user has a hand-held computer with a modem or other form of wireless communication, then the receiving method could be either a text file or a digital map. Fourth, if the user has a hand-held computer, or a mobile data terminal, then the only option is a text file describing the location of the object. A cellular phone with limited image display capability can also receive a text file as in the fourth case.
The current method thus has the following major constraints:
(1) The process requires manual operations at the Host or the Data Processing Center.
(2) Manually interpreting location information is time consuming and error-prone.
(3) Due to the required manual processing, tracking of objects is not done in real-time.
(4) Because of the above constraints, currently it is not practical to receive location information through thin-clients such as cellular phones or hand-held computers.
Geocoding, also known as address matching, is the process of translating a street address into a set of map coordinates. For example, the input of “1234 Main Street, Columbus,” is translated into a pair of Latitude and Longitude readings, or X and Y coordinates, so that the location of that address can be displayed on a map. Reverse Geocoding is opposite to geocoding. It takes a pair of Latitude and Longitude, or X and Y coordinates, and converts the input coordinates into a street address. There are two known methods of reverse geocoding, the Polygon method and the Centroid method. The Centroid method is a revised and enhanced version of the Polygon method.
The Polygon method is based on polygons of a parcel map, i.e., each land record is digitized into a polygon representing the parcel, and the record is associated with a street address. Conceptually, any point location denoted by a pair of Lat/Long or X-Y can be plotted on the parcel map and then the parcel in which the point location falls is identified and the address determined. There are two major problems with the Polygon method. First, parcel maps showing polygon features tend to be excessively large in size even for just a small city. Data processing is extremely difficult due to the file size. As a consequence, the Polygon method is just a concept and is not used practically. Empirical application of this method is limited to small geographic areas. Second, digital parcel maps are expensive to build and are available only in very few cities or counties. Using this method for reverse geocoding is thus feasible in only a very few cases.
The Centroid method is basically a revision of the Polygon method and the enhancement is meant to reduce the file size substantially. Instead of using polygons to represent parcels, the Centroid method organizes land records by the centroid location of each parcel and the centroid point becomes the graphical object in the database. As each polygon is reduced to a point location, the file size is minimized. The search method can be much improved with the Centroid method. In the Polygon method, the program must compare the target point location with polygons. The Centroid method only needs to find the closest centroid point from the target point. The Centroid method still requires a parcel map, and must convert the parcel map into a point coverage of centroid locations, which requires one additional procedure in data preparation. The main problem remains the same, i.e., polygon maps are available only in very few areas. An additional problem is that an incorrect parcel can be associated with the target point location, particularly when the parcel sizes are not uniform.
Both the Polygon method and the Centroid method face major limitations. In practical terms, the required parcel maps are not available in most cities or counties. In terms of methodology, the major limitation is that the information that can be generated from either method is limited to the street address of the best-fit location and is not related to the street network. For instance, the result of one match could be “1234 Main Street, Santa Ana, Calif.”. In reality, the point location obtained from GPS satellites is not accurate. At present, after the US government removed the introduced error called Selective Availability (SA) on May 1, 2000, the point is within 10 meters accuracy, meaning that the location could be 10 meters off the actual location. In addition, if the digital map has a 20-meter accuracy, then the worst case position discrepancy could be 30 meters.
Due to the above limitations, the existing methods cannot generate information about street networks. Either method can generate a best-fit address, but it cannot tell us whether the object is between a pair of intersections. Furt
Cuchlinski Jr. William A.
GeoSpatial Technologies Inc.
Nguyen Thu
Sheldon & Mak
LandOfFree
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