Position determining program and method

A method of determining a position of an object relative to a road network formed by road segments represented by a geographic database, the method comprising the steps of:

• determining an intermediate position density along a plurality of road segments in the road network by shifting a prior position density which had been determined along said plurality of road segments by a distance traveled by the object since the prior position density had been determined;
• modifying said intermediate position density to take into account sensor data received since the prior position density had been determined to form a second position density;
• computing a certainty measure of the second position density;
• and
• if the certainty measure is less than a predetermined threshold, determining that the object is not located on said plurality of road segments.

A method of determining a position of an object relative to a road network formed by road segments represented by a geographic database, the method comprising the steps of:

• determining an intermediate position density along a plurality of road segments in the road network by shifting a prior position density which had been determined along said plurality of road segments by a distance traveled by the object since the prior position density had been determined;
• modifying said intermediate position density to take into account sensor data received since the prior position density had been determined to form a second position density;
• computing a certainty measure of the second position density;
• and
• if the certainty measure is greater than a predetermined threshold, determining the position of the object to be that location along the plurality of road segments at which the second position density is greatest.

A program that determines a position of an object relative to data that represent navigable roads in a geographic region, the program comprising:

• a first routine that forms an intermediate position density by shifting a prior-determined position density associated with a plurality of said navigable roads represented by said data by a distance traveled since said prior-determined position density was determined,
• wherein said prior-determined position density represents a probability that the object was located along said plurality of navigable roads at a time to which said prior-determined position density related;
• a second routine that modifies said intermediate position density using sensor data obtained since said prior-determined position density was determined to form a new position density;
• and
• a third routine that determines the position of the object to be that location along said plurality of roads at which said new position density is greatest.

A program that determines a position of an object relative to data that represent navigable roads in a geographic region, the program comprising:

• a first routine that forms an intermediate position density by shifting a prior-determined position density associated with a plurality of said navigable roads represented by said data by a distance traveled since said prior-determined position density was determined,
• wherein said prior-determined position density represents a probability that the object was located along said plurality of navigable roads at a time to which said prior-determined position density related;
• a second routine that modifies said intermediate position density using sensor data obtained since said prior-determined position density was determined to form a new position density;
• and
• a third routine that computes a certainty measure of the new position density and, if the certainty measure is less than a predetermined threshold, determines that the object is not located on said plurality of road segments.

A program that determines a position of an object relative to data that represent navigable roads in a geographic region, the program comprising:

• a first routine that forms an intermediate position density by shifting a prior-determined position density associated with a plurality of said navigable roads represented by said data by a distance traveled since said prior-determined position density was determined,
• wherein said prior-determined position density represents a probability that the object was located along said plurality of navigable roads at a time to which said prior-determined position density related;
• a second routine that modifies said intermediate position density using sensor data obtained since said prior-determined position density was determined to form a new position density;
• and
• a third routine that computes a certainty measure of the new position density and, if the certainty measure is greater than a predetermined threshold, determines the position of the object to be that location along the plurality of road segments at which the second position density is greatest.

A vehicle positioning program that determines a position of an object relative to data that represent navigable roads in a geographic region, the program comprising:

• a device independent layer that receives outputs from a plurality of sensors that sense conditions indicative of a position of said object and provides normalized outputs indicative thereof;
• a fusion layer that combines the outputs from said device independent layer to provide a dead-reckoned position and variance data associated with the sensor outputs;
• and
• a positioning layer that includes:
• a first routine that determines an intermediate position density by shifting a prior-determined position density associated with a plurality of said navigable roads represented by said data by a distance traveled since said prior-determined position density was determined,
• wherein said prior-determined position density represents a probability that the object was located along said plurality of navigable roads at a time to which said prior-determined position density related;
• a second routine that modifies said intermediate position density using said outputs of said fusion layer obtained since said prior-determined position density was determined to form a new position density;
• and
• a third routine that determines the position of the object to be that location along said plurality of roads at which said new position density is greatest.

A method of determining an estimated position of vehicle on a road network comprising the steps of:

• determining a first position density along a plurality of road segments in the road network;
• shifting the first position density along said plurality of road segments by a distance traveled;
• modifying said shifted first position density to take into account recently received sensor data to form a second position density;
• and
• determining the estimated position of the vehicle to be that location along the plurality of road segments that has at the second position density is greatest.

A navigation system comprising:

• sensors that sense conditions indicative of an object's position;
• a geographic database that includes data that represent features in a geographic region;
• programming that uses said geographic database and outputs from said sensors to determine the object's position relative to said data, wherein said programming includes
• a first routine that forms an intermediate position density by shifting a prior-determined position density associated with a plurality of roads represented by said data by a distance traveled since said prior-determined position density was determined,
• wherein said prior-determined position density represents a probability that the object was located along said plurality of navigable roads at a time to which said prior-determined position density related;
• a second routine that modifies said intermediate position density using said outputs from said sensors obtained since said prior-determined position density was determined to form a new position density;
• and
• a third routine that determines the position of the object to be that location along said plurality of roads at which said new position density is greatest.