1030 results about "Map matching" patented technology

A system and method for locating, tracking, and/or monitoring the status of personnel and/or assets (collectively “trackees”), both indoors and outdoors, is provided. Tracking data obtained from any number of sources utilizing any number of tracking methods (e.g., inertial navigation and signal-based methods) may be provided as input to a mapping application. The mapping application may generate position estimates for trackees using a suite of mapping tools to make corrections to the tracking data. The mapping application may further use information from building data, when available, to enhance position estimates. Indoor tracking methods including, for example, sensor fusion methods, map matching methods, and map building methods may be implemented to take tracking data from one or more trackees and compute a more accurate tracking estimate for each trackee. Outdoor tracking methods may be implemented to enhance outdoor tracking data by combining tracking estimates such as inertial tracks with magnetic and/or compass data if and when available, and with GPS, if and when available.

When information regarding position of a facility, such as a parking lot, is requested from a terminal (22), an information center (10) supplies position information to the terminal (22) as a relative position using a landmark, such as a neighboring road. The terminal (22) computes a shift from a position of a landmark in a local map database (24) and a position of a received landmark in a map matching processor (34), and corrects the facility position. If the landmark has not been stored in the map database (24) at the terminal (22), relative data based on another road is resent.

A map matching method and apparatus for a navigation system estimates a location of the navigation system on a correct road segment when a GPS signal is invalid. The map matching method creates a database of pairs of locations at which the navigation system encountered GPS signal loss and recovery in the first time. The navigation system conducts a map matching processing when the GPS signal is lost in the second time at the recorded location by incorporating various additional factors to match the current position with a correct road segment. The various additional factors, in addition to the measured data by a dead reckoning process, include road class, road accessibility, road angle, proximity to candidate road, etc.

Systems, methods, and devices are described for implementing map matching techniques relating to measured location data. Probabilistic models, including temporal Bayesian network models and Hidden Markov Models, may be used for combining multiple classes of evidence relating to potential locations of points traversed on routes over time. Multiple route segments and overall routes may be maintained under relative uncertainty as candidates. The candidate route segments and overall routes may then be reduced into a smaller number of candidates or a single most likely route as a trip progresses. As the trip progresses, route segments in proximity to each location point are identified and candidate matches are determined. A probability of an entity traversing a candidate match at a given time and a probability of an entity traversing between a first candidate match at a first time and a second candidate match at a second time are determined based on a plurality of factors. Different modalities may be used to measure and transmit the location data.

A vehicular swept path processor and a map matching processor calculate a current position of a subject vehicle. A gyro sensor and a steering sensor output detection signals. A POI recognition processor makes a determination as to whether the vehicle stopped off at a POI or not based on the calculated current position and the outputted detection signals. When the determination is affirmatively made, i.e., the vehicle is determined to have stopped off at the POI, the POI recognition processor calculates a recognition confidence degree relative to an accuracy in the determination. A vehicle control ECU controls an in-vehicle device such that the in-vehicle device performs a predetermined operation corresponding to the POI in a mode according to the calculated recognition confidence degree.

The invention discloses a prediction method and visualization method of traffic jam. The methods comprise the following steps: GPS data sent back by a taxicab is utilized for being associated to roads in an electronic map through the map matching method; the road section speed calculated in terms of the matched data is utilized for judging the traffic state of road sections; the evolution rule of the traffic jam, including formation and dissipation of the traffic jam, is extracted with the utilization of the historical data; the traffic jam prediction is performed with the utilization of the sliding time window scheme after a real-time traffic information database is associated; the congestion intensity and the influence range of congested road sections can be presented by a thermodynamic chart after the congestion intensity of the congested road sections is calculated based on the prediction result. The prediction method provided by the invention can realize highly accurate traffic jam prediction, and the visualization method of the thermodynamic chart enables the traffic jam to be visualized and understandable, so that the location and the influence range of the traffic jam can be discerned conveniently.

Methods for road safety enhancement use mobile communication device (MCD) onboard vehicle to share traveling data through inter-vehicle communication broadcasting, perform road hazard warning, enhance road navigation, and provide autonomous road assistance. The methods have a variety of vehicle status data, such as moving data, steering data, or indicator data, obtained through GPS or image capturing and recognition of instrument cluster of vehicle. The image capturing and recognition allows to get speed data from indication of speedometer, indication of left or right turn signal, steering action data corresponding to steering wheel rotation, and light-on indication of system status indicators. To facilitate that, the MCD may be placed in front of steering wheel, and, if applicable, also in coupling with movement of steering wheel. The MCD may perform relative positioning map-matching lane correlation and GPS-update-interval speed positioning to improve data quality regarding vehicle moving status.

An Adaptive Positioning System provides a method for directing and tracking position, motion and orientation of mobile vehicles, people and other entities using multiple complementary positioning components to provide seamless positioning and behavior across a spectrum of indoor and outdoor environments. The Adaptive Positioning System (APS) provides for complementary use of peer to peer ranging together with map matching to alleviate the need for active tags throughout an environment. Moreover, the APS evaluates the validity and improves the effective accuracy of each sensor by comparing each sensor to a collaborative model of the positional environment. The APS is applicable for use with multiple sensors on a single entity (i.e. a single robot) or across multiple entities (i.e. multiple robots) and even types of entities (i.e. robots, humans, cell phones, cars, trucks, drones, etc.).

A method and apparatus of vehicle positioning uses map matching as feedback for an integrated navigation system where a map and navigation system is coupled with an inertial navigation system (INS) using low-precision vehicle sensors when the vehicle passes through a tunnel or other area suffering from GPS signal loss. The method and apparatus operates to detect whether the vehicle has reached an entry point of a tunnel, and if so, immediately starts a map matching operation to match the current vehicle position with a road link of the tunnel. The current position determined by the map matching operation is feedbacked to an integration Kalman filter thereby correcting errors caused by the vehicle sensors. The method and apparatus resumes the normal navigation operation including GPS navigation as soon as it detects that the vehicle is out of the tunnel.

The invention provides an automatic guided vehicle based on map matching and a guide method of the automatic guided vehicle. The automatic guided vehicle comprises a map creation module, a location module, a human-machine interaction module, a route planning module, an autonomous navigation module and a dispatching system module, wherein the map creation module is used for creating an environmental map; the automatic guided vehicle utilizes the carried location module to match the observed local environmental information with a preliminarily created global map to obtain pose information under the global situation; the human-machine interaction module is used for displaying the information and working state of the automatic guided vehicle in the map in real time; the route planning module is used for planning a feasible optimum route in the global map; the automatic guided vehicle is autonomously navigated by virtue of the autonomous navigation module according to the planned route; the dispatching system module is used for dispatching the automatic guided vehicle closest to a calling site to go to work. By adopting the automatic guided vehicle, no other auxiliary location facility is needed, no alteration is made for the environment, the capability of the automatic guided vehicle is completely depended, the automatic guided vehicle is utterly ignorant to the environment, and no priori information is provided.

Disclosed is a navigation system that converts route guidance information into network data and determines the deviation of a mobile object from a route using a map-matching probability that shows a degree of match between a current location and network data.

The invention discloses an inter-city road network index and a matching method. The invention belongs to the field of intelligent transportation. The method comprises the steps that: an inter-city road network is partitioned; a large-grid index of the partitioned inter-city road network is established, such that road section sets contained in the large-grids of the regions after partitioning can be acquired; GPS information is received from a GPS receiving system; the grid in which the GPS information exists is positioned, and a candidate matching set of road sections is acquired; an exclusion positioning method is operated in the grid; and the candidate matching set obtained after exclusion is subjected to map matching with the GPS information, such that a matching road section is found. According to the invention, the inter-city road network is partitioned, the large-grid index is established, and an algorithm is operated in the large-grid, such that inter-city road network index and matching problems are solved with high efficiency.

The invention discloses a method and a device for speculating routes, relating to the technical field of intelligent traffic systems. The embodiment of the invention provides a route speculating method which comprises the following steps: establishing a relevant road network data structure and a relevant road network topology structure according to road information date of urban navigation electronic maps, wherein the road network data structure comprises nodes, road chains and mutual construction relations, and the road topology structure comprises communication relations among the road chains and all routes that each road chain can reach within a set time; receiving GPS positioning data of a floating car, wherein the GPS positioning data comprise time information, position information of a GPS positioned vehicle, vehicle running direction and vehicle running speed; carrying out map matching according to the position information of the GPS positioned vehicle to acquire alternative road chains of the GPS positioning data of the floating car; and acquiring a running route of the floating car according to the alternative road chains and the time information.

Detecting at least one object in the vicinity of a vehicle by a vehicle sensor and estimating characteristics about the object, the sensor being calibrated to the position of the vehicle by e.g. GPS, estimating a location of the sensed object from position and orientation estimates of the vehicle and of the measurements of the sensor; querying a map or image database by vehicle position or estimated sensed object location, the database allowing information to be retrieved for the objects, to extract the objects of the database for that position, comparing the sensed object with the extracted object using a comparison logic, and if such comparison is successful effecting either an adjustment of the GPS position of the vehicle, an adjustment of the position information for the extracted object of the database, or display the extracted, database-depicted object as a graphical image on a display of a navigation unit.

The invention discloses a method and system for map matching of transportation vehicle GPS (Global Position System) data. In the invention, a map matching process of the transportation vehicle GPS data is completed by structuring high-efficiency map matching grid indexes and using a simplified and accurate matching calculation, so that the operation efficiency of matching of the transportation vehicle GPS data is greatly improved, and the processing efficiency is thousands times higher than that of the traditional map matching method; and the matching positioning of a vehicle in a complex road network is assisted and judged by tracking the historical matching road state of the vehicle and using the topological connectivity of the road network, so that the accuracy of the map matching of the transportation vehicle GPS data can be greatly improved.

The invention relates to a vehicle GPS data map matching method based on a hidden markov model. The method comprises the following steps: acquiring route data from a shpefile electronic map; extracting original vehicle trajectory data, and preprocessing vehicle GPS data; taking a road section with a certain distance of each GPS observation point as a candidate road section; calculating the observation probability of each GPS point and transition probability of an adjacent candidate road section on the basis of the hidden markov model; and calculating an optimal matching trajectory by using a viterbi algorithm. The vehicle GPS data map matching method is based on the hidden markov model, by consideration of the positions of the GPS points, speed and direction, topology of a road network and the associated information between trajectory points and the road network, new observation probability and new transition probability are raised, and therefore, map matching accuracy is improved further.

The invention relates to a method for acquiring dynamic traffic information based on a middleware, which overcomes the shortcomings of data loss, data noise, and particularly the multi-source isomerism of data, reduces redundant data, ensures the accuracy of the data and improves the accuracy and the reliability of the data. The method comprises the following steps of: 1) transmitting the traffic information by adopting a serial interface communication mode and/or a network communication mode; 2) customizing an information acquisition port which can be matched with different inspection devices of the traffic information by using an interface definition language IDL in the CORBA middleware technique, identifying and normalizing the data from different inspection devices, thus acquiring the real-time dynamic traffic information, such as road traffic flow, vehicle velocity, road occupancy rate and the like; 3) performing preprocessing on the all data acquired by the method, and performing map matching on a floating automobile by using a road matching algorithm based on a network topology relationship; and 4) fusing and saving the multi-source isomerous real-time dynamic traffic data which is preprocessed by the method in a database by using an immune clustering neural network.

The invention relates to systems and methods for precise sub-lane vehicle positioning. A vehicle having an on-board computer, vehicle sensors, a satellite-positioning unit, a database storing a lane-level map performs a method to determine a new pose of the vehicle using map matching. The method includes the on-board computer of the vehicle receiving new data from at least one of the vehicle sensors and collecting measurements from the vehicle sensors. The method also includes the on-board computer of the vehicle computing propagation of vehicle pose with respect to consecutive time instances and performing a curve-fitting process. The method further includes the on-board computer of the vehicle performing a sub-routine of updating at least one observation model based on results of the curve-fitting process, performing a tracking sub-routine including using a probability distribution to update the vehicle pose in terms of data particles, and performing a particle filtering sub-routine based on the data particles to compute the new vehicle pose.

A method for correcting position error in a navigation system enables one to more accurately match a position measurement of a moving object on a digital map. Particularly, the method comprises the steps of: receiving a current position measurement of a moving object from GPS/DR (Dead Reckoning)-based information; correcting the current location measurement using a displacement-corrected value; performing map matching using the corrected current position measurement; calculating variation of correction angle by extracting a current correction angle out of the map matching result; converting and correcting a previous displacement-corrected value to the current correction angle; and compensating the displacement-corrected value by applying a predetermined constant to the converted displacement-corrected value, and storing the compensated displacement-corrected value.

An embodiment of the invention provides a map matching method and device. The map matching method comprises steps as follows: multiple track points are acquired, and at least one candidate road segment corresponding to each track point on an electronic map is acquired; observation probability between a first track point and any one candidate road segment corresponding to the first track point in the multiple track points is calculated, state transition probability between each candidate road segment corresponding to the first track point and each candidate road segment corresponding to a second track point is calculated, and the second track point and the first track point are adjacent track points; a road segment sequence matched with the multiple track points is determined according to the observation probability and the state transition probability. According to the map matching method and the device, the optimal road segment sequence matched with the track points is obtained under the condition that a limited quantity of the track points with low positioning precision are obtained, and the map matching robustness is improved.

A navigation system in a vehicle stores position history information including position information related to a detection position detected by a position detector and position information related to a map matching position. The map matching position is obtained by matching the detection position with a point of a road in road map data. When the vehicle runs on a not-stored road that is not stored in the road map data, the navigation system determines a trail of running of the vehicle from departure from a stored road that is stored in the road map data to comeback into a stored road that is stored in the road map data, based on the stored position history information.

The present invention relates to a method for estimating location of a moving object in a navigation system, which is capable of accurately estimating location of the object in a shadow area of GPS location data so that navigation service is provided. A method for estimating location of a moving object in a navigation system includes the steps of: (a) receiving GPS location data from a moving object; (b) determining GPS shadow area by using the received GPS location data; (c) calculating moving straight distance of the moving object with reference to a last GPS location data in visible regions when the moving object is in a GPS shadow area; (d) calculating virtual location data by using the calculated moving straight distance of the moving object; and (e) calculating estimated location on a digital numeric map positioned nearest from the virtual location data, and performing a map-matching to provide a navigation service.