CN108981729B - Vehicle positioning method and device - Google Patents
Vehicle positioning method and device Download PDFInfo
- Publication number
- CN108981729B CN108981729B CN201710407858.3A CN201710407858A CN108981729B CN 108981729 B CN108981729 B CN 108981729B CN 201710407858 A CN201710407858 A CN 201710407858A CN 108981729 B CN108981729 B CN 108981729B
- Authority
- CN
- China
- Prior art keywords
- lane
- target
- vehicle
- target vehicle
- positioning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 106
- 238000012360 testing method Methods 0.000 claims abstract description 79
- 230000001133 acceleration Effects 0.000 claims abstract description 51
- 230000008569 process Effects 0.000 claims abstract description 38
- 230000008859 change Effects 0.000 claims abstract description 14
- 238000004422 calculation algorithm Methods 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 12
- 238000004364 calculation method Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 6
- 230000004807 localization Effects 0.000 claims 1
- 230000007613 environmental effect Effects 0.000 abstract description 10
- 238000004590 computer program Methods 0.000 description 16
- 238000005516 engineering process Methods 0.000 description 16
- 230000001186 cumulative effect Effects 0.000 description 13
- 230000006870 function Effects 0.000 description 12
- 238000010586 diagram Methods 0.000 description 9
- 239000011159 matrix material Substances 0.000 description 9
- 230000009471 action Effects 0.000 description 4
- 238000011161 development Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 239000000284 extract Substances 0.000 description 3
- 238000010295 mobile communication Methods 0.000 description 3
- 230000006399 behavior Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000008447 perception Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000002864 sequence alignment Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3446—Details of route searching algorithms, e.g. Dijkstra, A*, arc-flags, using precalculated routes
Landscapes
- Engineering & Computer Science (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Automation & Control Theory (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Navigation (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention provides a vehicle positioning method and a vehicle positioning device, wherein the method comprises the following steps: acquiring a reference model of each lane, wherein the reference model is generated according to the change of the vertical acceleration of a reference vehicle in the direction vertical to the ground along with the time when the reference vehicle runs in the lane; measuring a vertical acceleration time-varying process of a target vehicle while the target vehicle is running to obtain a test sample; and determining the target lane where the target vehicle is located from the lanes according to the similarity between the test sample and the reference model of each lane. By the method, the influence of environmental factors on lane identification accuracy can be reduced, the accuracy and reliability of lane identification and positioning are improved, and the problem that the identification accuracy is easily interfered by environmental changes in the prior art is solved.
Description
Technical Field
The invention relates to the technical field of intelligent navigation, in particular to a vehicle positioning method and device.
Background
With the development of navigation technology, users have higher and higher requirements on navigation equipment, and it is expected that the navigation equipment can provide more accurate services such as road prompting, lane changing reminding, road diversion and the like, so that how to accurately position a current driving lane of a vehicle becomes a problem to be solved urgently.
In the related art, road recognition is usually performed based on technologies such as computer vision and image processing, an image sensor is used for monitoring a road in front of a vehicle in real time, collected road condition images are processed by using a related image processing algorithm, and finally, a current driving lane of the vehicle is recognized through landmark features of the lane, such as lane lines.
However, the existing road identification method based on computer vision and image processing is susceptible to interference of environment change in identification accuracy, and the quality of an acquired image is poor under the conditions of weak light, low visibility, traffic jam and the like, so that the accuracy of lane identification is reduced.
Disclosure of Invention
The present invention is directed to solving, at least to some extent, one of the technical problems in the related art.
Therefore, a first objective of the present invention is to provide a vehicle positioning method, so as to reduce the influence of environmental factors on lane recognition accuracy, improve lane recognition and positioning accuracy and reliability, and solve the problem in the prior art that the recognition accuracy is easily interfered by environmental changes.
A second object of the present invention is to provide a vehicle navigation method.
A third object of the present invention is to provide a vehicle positioning apparatus.
A fourth object of the present invention is to provide a car navigation device.
A fifth object of the invention is to propose a non-transitory computer-readable storage medium.
A sixth object of the invention is to propose another non-transitory computer-readable storage medium.
A seventh object of the invention is to propose a computer program product.
An eighth object of the invention is to propose another computer program product.
In order to achieve the above object, an embodiment of a first aspect of the present invention provides a vehicle positioning method, including:
acquiring a reference model of each lane, wherein the reference model is generated according to the change of the vertical acceleration of a reference vehicle in the direction vertical to the ground along with the time when the reference vehicle runs in the lane;
measuring a vertical acceleration time-varying process of a target vehicle while the target vehicle is running to obtain a test sample;
and determining the target lane where the target vehicle is located from the lanes according to the similarity between the test sample and the reference model of each lane.
According to the vehicle positioning method, the reference models of all lanes are obtained, when the target vehicle runs, the time-varying process of the vertical acceleration of the target vehicle is measured, so that the test sample is obtained, and the target lane where the target vehicle is located is determined from all lanes according to the similarity between the test sample and the reference models of all lanes. Therefore, the influence of environmental factors on lane recognition accuracy can be reduced, and the accuracy and reliability of lane recognition and positioning are improved.
In order to achieve the above object, a second aspect of the present invention provides a vehicle navigation method, including:
the vehicle positioning method is adopted to position a target vehicle so as to determine a target lane in a target road section where the target vehicle is located;
and navigating the target vehicle according to the target road section where the target vehicle is located and the target lane.
According to the vehicle navigation method, the target vehicle is positioned by adopting the vehicle positioning method, the target lane where the target vehicle is located is determined from the target road section where the target vehicle is located, and the target vehicle is navigated according to the target road section where the target vehicle is located and the target lane. Therefore, navigation can be performed based on the positioned lane, an optimal driving route is recommended for a user in real time, the navigation accuracy is improved, and the user experience is improved.
To achieve the above object, a third aspect of the present invention provides a vehicle positioning apparatus, including:
the system comprises an acquisition module, a processing module and a control module, wherein the acquisition module is used for acquiring a reference model of each lane, and the reference model is generated according to the change of the vertical acceleration of a reference vehicle in the direction vertical to the ground along with the time when the reference vehicle runs in the lane;
the measuring module is used for measuring the vertical acceleration change process of a target vehicle along with time when the target vehicle runs so as to obtain a test sample;
and the determining module is used for determining the target lane where the target vehicle is located from each lane according to the similarity between the test sample and the reference model of each lane.
According to the vehicle positioning device provided by the embodiment of the invention, the reference model of each lane is obtained, the time-varying process of the vertical acceleration of the target vehicle is measured when the target vehicle runs, so as to obtain the test sample, and the target lane where the target vehicle is located is determined from each lane according to the similarity between the test sample and the reference model of each lane. Therefore, the influence of environmental factors on lane recognition accuracy can be reduced, and the accuracy and reliability of lane recognition and positioning are improved.
To achieve the above object, a fourth aspect of the present invention provides a vehicular navigation apparatus, including:
a positioning module, configured to position a target vehicle by using the vehicle positioning apparatus according to the third embodiment, so as to determine a target lane in a target road segment where the target vehicle is located;
and the navigation module is used for navigating the target vehicle according to the target road section where the target vehicle is located and the target lane.
The vehicle navigation device provided by the embodiment of the invention positions the target vehicle by adopting the vehicle positioning device so as to determine the target lane in the target road section where the target vehicle is positioned, and navigates the target vehicle according to the target road section where the target vehicle is positioned and the target lane. Therefore, navigation can be performed based on the positioned lane, an optimal driving route is recommended for a user in real time, the navigation accuracy is improved, and the user experience is improved.
To achieve the above object, a fifth embodiment of the present invention provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the vehicle positioning method according to the first embodiment.
To achieve the above object, a sixth aspect of the present invention provides another non-transitory computer-readable storage medium, on which a computer program is stored, the computer program, when being executed by a processor, implementing the vehicle navigation method according to the second aspect.
To achieve the above object, a seventh embodiment of the present invention provides a computer program product, wherein when the instructions of the computer program product are executed by a processor, the vehicle positioning method according to the first embodiment is performed.
To achieve the above object, an eighth aspect of the present invention provides another computer program product, wherein when the instructions of the computer program product are executed by a processor, the vehicle navigation method according to the second aspect is executed.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The foregoing and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a schematic flow chart of a vehicle positioning method according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a vehicle positioning method according to another embodiment of the present invention;
FIG. 3 is a schematic flow chart illustrating a vehicle positioning method according to still another embodiment of the present invention;
FIG. 4 is a schematic flow chart illustrating a vehicle positioning method according to still another embodiment of the present invention;
FIG. 5 is a schematic view of a target vehicle traveling process;
FIG. 6 is a schematic flow chart illustrating a vehicle positioning method according to another embodiment of the present invention;
FIG. 7 is a flowchart illustrating a vehicle navigation method according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a vehicle positioning device according to an embodiment of the present invention;
FIG. 9 is a schematic structural diagram of a vehicle positioning device according to another embodiment of the present invention; and
fig. 10 is a schematic structural diagram of a car navigation device according to an embodiment of the present invention;
FIG. 11 is a schematic structural diagram of a vehicle positioning system according to an embodiment of the present invention;
fig. 12 is a schematic diagram of data collection by the mobile terminal.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
A vehicle positioning method and apparatus of an embodiment of the present invention are described below with reference to the drawings.
In recent years, with the development of mobile terminals, related road recognition research is beginning to utilize sensors, such as an acceleration sensor, a direction sensor, and the like, built in the mobile terminals to acquire a driving state and a motion track of a vehicle during driving, and detect lane changing behaviors, turning, and other actions of the vehicle, so as to realize lane recognition.
However, the above method requires that the initial lane information of the vehicle is known and that each lane change behavior of the vehicle is correctly recognized, otherwise it is prone to cause accumulated errors. In addition, in a complicated traveling environment such as a frequent lane change environment, the recognition accuracy is low.
Therefore, in order to solve the above problems, embodiments of the present invention provide a vehicle positioning method to improve the accuracy and reliability of lane identification and positioning.
Fig. 1 is a schematic flow chart of a vehicle positioning method according to an embodiment of the present invention, which can be executed by a cloud server, wherein the cloud server has data sensing, collecting and analyzing functions.
As shown in fig. 1, the vehicle positioning method includes the steps of:
and S11, acquiring a reference model of each lane.
The reference model can be generated according to the change of the vertical acceleration of the reference vehicle in the direction vertical to the ground along with the time when the reference vehicle runs in the lane.
As time passes and the number of passing vehicles increases, the surface of the roadway begins to pothole more or less to different extents. When the vehicle is driven, the acceleration of the vehicle changes in the vertical direction due to the influence of the lane depressions, and the greater the depression area, the longer the duration of the vertical acceleration in the vertical direction, the deeper the depression depth, and the greater the magnitude of the vertical acceleration in the vertical direction. Thus, in the present embodiment, a reference model of the lane may be established in advance based on this feature.
When a reference model is established according to the vertical acceleration in the direction perpendicular to the ground, vehicles of different types can be selected as reference vehicles in a real driving environment, data information of the reference vehicles in the operation process is sensed by various sensors installed in a mobile terminal on the vehicles, and the data sensed by the sensors are acquired through related application programs installed in the mobile terminal and then uploaded to a cloud server. In addition, in order to improve the accuracy of the established reference model, a second-generation On-Board Diagnostics (OBD-II) can be installed in the reference vehicle to acquire the driving data of the reference vehicle, including the vehicle speed, the mileage and the like, so as to calibrate the data acquired by the relevant application program. The related application programs installed in the mobile terminal can communicate with the cloud server through mobile communication technologies such as a mobile data network and Wi-Fi.
In order to facilitate understanding of the process of collecting data sensed by a sensor in a mobile terminal by using a relevant application program, a description is given below with reference to some objects and functions involved in the collection process.
Taking a smart phone taking a mobile terminal in a reference vehicle as an Android system as an example, the Android system provides a sensor manager object, and by executing a getDefaultSensor () function of the sensor manager object, a sensor object can be specified, for example, the sensor is specified as an acceleration sensor, or a direction sensor, and the like, wherein the getDefaultSensor () function specifies the type of the sensor by transferring a specific parameter. After a sensor object is specified, a listening event may be added to the specified sensor using the regiostrinerr () function to collect the required data. Finally, the data information perceived by the specified sensor is acquired through the onsensor changed () function in the SensorEventListener object.
After the relevant application programs in the mobile terminal acquire the data information sensed by the sensors, the acquired data information can be uploaded to the cloud server based on the mobile communication technology so that the cloud server can analyze and process the data information, the processed data information is used as a training sample, and a reference model of a lane is obtained through training. Since the vehicle is easily affected by the external environment during the driving process, the data sensed by the sensor is not necessarily accurate, and therefore, in order to improve the data accuracy, in one possible implementation manner of the embodiment of the invention, before uploading the acquired data to the cloud server, a noise elimination technology, such as a low-pass filter, may be adopted to perform denoising processing on the data acquired by the relevant application program, and then, the processed data is uploaded to the cloud server.
After the cloud server receives the data information uploaded by the mobile terminal, the received data information can be used as a training sample to train and obtain a lane reference model. Optionally, in a possible implementation manner of the embodiment of the present invention, the cloud server may further process the received data information, for example, perform noise reduction processing, so as to improve the accuracy of the data.
After the cloud server receives the data information uploaded by the mobile terminal, the data information is analyzed and processed, the vertical acceleration in the data information is extracted, a vertical acceleration curve of a corresponding lane is fitted according to the vertical acceleration, the vertical acceleration curve of the corresponding lane is used as training data to be input, the corresponding lane is used as output, and a lane reference model can be established.
For example, assuming that a road includes left, middle and right lanes, which are respectively denoted as l, m and r, the cloud server obtains vertical acceleration curves of the lanes according to the received data information, which are respectively denoted as s (l), s (m) and s (r). And (3) taking S (l), S (m) and S (r) as the input of the model, and taking the corresponding left lane, middle lane and right lane as the output, thus obtaining the reference model of the lane.
In this embodiment, when lane positioning is performed on a currently running target vehicle, a reference model of each lane may be obtained first, and a specific way of obtaining the reference model will be given in the following content, which is not described in detail herein for avoiding redundancy.
S12, the vertical acceleration of the target vehicle is measured as a function of time while the target vehicle is running to obtain a test sample.
In this embodiment, when the target vehicle is in a driving process, the mobile terminal in the target vehicle can acquire data information sensed by a sensor built in the mobile terminal, and upload the acquired data information to the cloud server. After the cloud server receives the data information, the vertical acceleration of the target vehicle in the running process can be extracted from the data information.
Optionally, in a possible implementation manner of the embodiment of the present invention, after receiving the data information uploaded by the mobile terminal, the cloud server may select to process the received data information, such as denoising, and then extract the vertical acceleration of the target vehicle in the driving process from the processed data information.
In another possible implementation manner of the embodiment of the present invention, after the cloud server receives the data information uploaded by the mobile terminal, the cloud server may also extract the vertical acceleration of the target vehicle in the driving process directly from the received data information without processing the data information.
After the cloud server extracts the vertical acceleration of the target vehicle in the running process, the process that the vertical acceleration changes along with time can be further measured, and a vertical acceleration curve of the target vehicle is obtained and is used as a test sample and recorded as S (t).
And S13, determining the target lane where the target vehicle is located from the lanes according to the similarity between the test sample and the reference model of each lane.
In this embodiment, after the reference model of each lane and the test sample corresponding to the target vehicle are obtained, the cloud server may match the test sample with the reference model, and determine the lane where the target vehicle is located from each lane according to the similarity between the test sample and the reference model of each lane.
Specifically, the test samples may be respectively input into the reference models of the lanes, and the similarity between the test samples and the reference models is calculated, where the higher the similarity is, the more similar the lane corresponding to the reference model of the lane is to the lane where the target vehicle is located, and the lane may be used as the target lane.
According to the vehicle positioning method, the reference models of all lanes are obtained, when the target vehicle runs, the time-varying process of the vertical acceleration of the target vehicle is measured, so that the test sample is obtained, and the target lane where the target vehicle is located is determined from all lanes according to the similarity between the test sample and the reference models of all lanes. Therefore, the influence of environmental factors on lane recognition accuracy can be reduced, and the accuracy and reliability of lane recognition and positioning are improved.
In order to reduce the computational complexity when the similarity between the test sample and the reference model of each lane is calculated and improve the efficiency of lane positioning, in a possible implementation manner of the embodiment of the invention, an OBD-II system may be further installed in the target vehicle to record the driving data of the target vehicle in the driving process, and the driving data may be collected by a related application installed in the mobile terminal and uploaded to the cloud server. The cloud server may determine the characteristics of the road where the target vehicle is located according to the received driving data information, and further obtain the reference model of each lane similar to the characteristics of the road where the target vehicle is located from the reference model, so as shown in fig. 2, on the basis of the embodiment shown in fig. 1, step S11 may include the following steps:
and S21, locating the target road section where the target vehicle is located.
In this embodiment, the cloud server may receive driving data recorded in the OBD-II system collected by a relevant application installed in the mobile terminal, and locate the target road segment where the target vehicle is located according to the driving data.
For example, the cloud server may determine the target road segment where the target vehicle is located according to the deflection direction of the steering wheel recorded in the received driving data. When the steering wheel is steered to the left, the surface target vehicle performs the actions of turning left, turning round or changing the road to the left, and the target road section where the target vehicle is located can be determined to be a curve, a road section allowing turning round or a road section changing to the left. Further, the cloud server can judge the specific type of the target road section according to the degree of leftward deflection of the steering wheel in the driving data. When the deflection degree is small, the target vehicle may perform a lane-changing action to the left, and the target road segment may be determined as a changeable road segment; when the deflection degree is large, the target vehicle may turn, and the target road section can be determined to be a curve; when the degree of deflection is large, the target vehicle may have a u-turn action, and the target road segment may be determined as a u-turn-allowed road segment.
And S22, selecting a reference model of each lane in the target road section.
In this embodiment, after the cloud server determines the target road segment where the target vehicle is located, the reference model of each lane corresponding to the road segment with the same characteristics as the target road segment can be selected from the reference models, so as to be used for subsequent matching with the test sample.
According to the vehicle positioning method, the target road section where the target vehicle is located is positioned, and the reference model of each lane in the target road section is selected, so that the matching complexity can be reduced, and the lane positioning efficiency can be improved.
The embodiment of the invention provides two possible implementation modes for positioning the target road section where the target vehicle is located. As one possible implementation manner, the target road segment where the target vehicle is located may be located according to the positioning information of the mobile terminal in the target vehicle, so that, as shown in fig. 3, on the basis of the foregoing embodiment, step S21 may include the following steps:
and S31, acquiring the positioning information from the mobile terminal bound by the target vehicle.
With the development of mobile terminal technology, the positioning service almost becomes the standard configuration of the existing mobile terminal, and therefore, in this embodiment, the positioning information can be acquired from the mobile terminal bound to the target vehicle.
The Positioning information may be obtained through a Global Positioning System (GPS) or a base station Location Service (LBS) technology. The GPS positioning technology utilizes a GPS positioning module in the mobile terminal to position the mobile terminal and determine the positioning information of the mobile terminal; the LBS location technology, as a location-based service technology, mainly obtains location Information (such as Geographic coordinates, geodetic coordinates, etc.) of a Mobile terminal through a radio communication network (such as a Global System for Mobile Communications (GSM) network, a Code Division Multiple Access (CDMA) network, etc.) of an operator of telecommunications, Mobile, etc., and determines the location Information of the Mobile terminal with the support of a Geographic Information System (GIS) platform.
The mobile terminal is bound on the target vehicle, so that the position information of the mobile terminal can represent the position information of the target vehicle, and the cloud server acquires the positioning information from the mobile terminal bound by the target vehicle, namely the positioning information of the target vehicle is acquired.
And S32, determining the target road section where the target vehicle is located according to the positioning information.
In this embodiment, after the cloud server obtains the positioning information of the target vehicle, the target road section where the target vehicle is located may be determined according to the obtained positioning information, and then the reference model of each lane in the target road section is selected from the reference models.
For example, if the positioning information acquired by the cloud server is N39 ° 59 'in north latitude and E116 ° 23' in east longitude, and it is shown in the positioning information that the target vehicle is close to the national olympic sports center, the cloud server may determine, according to the positioning information, that the target road segment where the target vehicle is located is a road segment close to the national olympic sports center on the four-road-north circle. Because the north four-loop has 8 lanes, the cloud server selects the reference models of the 8 lanes in the road section close to the national olympic sports center on the north four-loop from the pre-established reference models.
According to the vehicle positioning method, the positioning information is obtained from the mobile terminal bound by the target vehicle, the target road section where the target vehicle is located is determined according to the positioning information, and the road section where the target vehicle is located can be roughly positioned, so that the lane positioning range is narrowed, and the matching complexity is reduced.
Because the method for acquiring the positioning information by adopting the GPS positioning technology or the LBS positioning technology is limited, it is difficult to acquire the positioning information in an area without base station coverage or network coverage, or in an area with network coverage but with network signals shielded by buildings and the like. Therefore, in another possible implementation manner of the embodiment of the present invention, the target road segment where the target vehicle is located may be located according to the driving data recorded by the OBD-II system installed in the target vehicle, so that, as shown in fig. 4, on the basis of the foregoing embodiment, step S21 may include the following steps:
and S41, acquiring the final positioning information sent by the mobile terminal before the signal sent by the mobile terminal disappears.
Because the mobile terminal can be used as a node in wireless communication to receive and transmit data in real time, and the mobile terminal can acquire the positioning information in real time based on the GPS positioning technology or the LBS positioning technology, and the mobile terminal cannot acquire the positioning information and cannot send data under the condition of no network coverage or weak network signals, the cloud server can acquire the final positioning information sent by the mobile terminal before the signals sent by the mobile terminal disappear.
S42, the travel distance is calculated based on the travel data of the target vehicle.
The OBD-II system installed in the target vehicle may record travel data of the target vehicle including, but not limited to, travel speed and travel time. In a possible implementation of the embodiment of the present invention, the driving data may further include a yaw direction of the steering wheel. After the cloud server acquires the driving data of the target vehicle, the driving distance can be calculated and obtained according to the driving speed and the driving time in the driving data.
It should be noted that the travel time of the target vehicle is not limited to be recorded by the OBD-II system, and may also be recorded by a mobile terminal bound to the target vehicle, which is not limited in the present invention.
And S43, determining the target road section where the target vehicle is located according to the driving distance and the final positioning information.
In this embodiment, after the cloud server calculates and obtains the travel distance, the target road segment where the target vehicle is located may be determined according to the obtained travel distance and the obtained final positioning information.
For example, if the target vehicle passes through a tunnel during driving and there is no network signal in the tunnel, the mobile terminal cannot acquire the position location information. Fig. 5 is a schematic view of a target vehicle running process. As shown in fig. 5, the road includes two branches, i.e., a route 1 and a route 2, wherein the route 1 includes a tunnel with a length of 200 meters. When the target vehicle runs to the point A, the mobile terminal in the target vehicle carries out the last positioning and then enters the tunnel, and the mobile terminal cannot acquire positioning information. The OBD-II system installed in the target vehicle records that the target vehicle has a travel time in the tunnel of 10 seconds, a travel speed of 72km/h, and a yaw direction of the steering wheel as a leftward yaw. When the target vehicle runs to the point B, the network signal is recovered, and the cloud server acquires the positioning information of the mobile terminal at the point A and the running data recorded by the OBD-II system. According to the acquired positioning information, the cloud server learns that the target vehicle passes through a turnout junction of a double-turnout road and a gas station is arranged nearby; from the acquired running data, it is known that the target vehicle has run for 10 seconds on the left-hand turnout road at a speed of 72 km/hour, and the running distance is calculated to be 200 m. According to the driving distance, the deflection direction of the steering wheel and the positioning information, the cloud server can determine that the target road section where the target vehicle is located is a road section outside the tunnel (namely behind the point B) on the line 1.
According to the vehicle positioning method, the final positioning information sent by the mobile terminal before the signal sent by the mobile terminal disappears is obtained, the driving distance is calculated according to the driving data of the target vehicle, the target road section where the target vehicle is located is determined according to the driving distance and the final positioning information, the road section where the target vehicle is located can be roughly positioned, the lane positioning range is narrowed, and the matching complexity is reduced.
In order to more clearly understand the process of determining the target lane where the target vehicle is located from the lanes, another vehicle positioning method is provided in the embodiment of the present invention, and fig. 6 is a flowchart illustrating the vehicle positioning method according to another embodiment of the present invention. As shown in fig. 6, on the basis of the embodiment shown in fig. 1, step S13 may include the following steps:
and S51, calculating the accumulated distance between the test sample and the reference model of each lane by adopting a dynamic programming algorithm to obtain the similarity between the test sample and the reference model of each lane.
The Dynamic Programming (DP) algorithm is also called Dynamic Time Warping (DTW) algorithm, and is generally used for solving an optimal solution problem. The DTW algorithm combines time warping and distance measurement calculation to match the test sample with the reference model, so as to measure the similarity of two time sequences with different lengths. The following describes a specific calculation process of the DTW algorithm:
assuming that the test sample and the reference model are respectively P and Q, the lengths of the test sample and the reference model are respectively m and n, and the characteristic value of the ith element of the test sample P is recorded as PiThe characteristic value of the jth element of the reference model Q is recorded as Qj. The calculation process can be divided into two cases according to whether the values of m and n are the same. The description is as follows:
in the first case: and m is n.
When m is equal to n, it indicates that the sequence lengths of the test sample P and the reference model Q are the same, and at this time, the euclidean distance between the corresponding elements may be directly calculated, and the calculation formula is as shown in formula (1). The smaller the calculated euclidean distance, the higher the degree of similarity between the test sample P and the reference model Q.
In the second case: m ≠ n.
When m ≠ n, it indicates that the sequence lengths of the test sample P and the reference model Q are different, in this case, the sequence alignment of the test sample P and the reference model Q is required before the calculation.
To align the test sample P with the reference model Q, the DTW algorithm constructs an m n matrix with the matrix elements (i, j) representing the eigenvalues PiAnd QjThe calculation formula of the Euclidean distance between the two is shown as formula (2).
After constructing the Euclidean distance matrix of the completed sequence, the DTW algorithm searches a path with the shortest cumulative distance (marked as D) from the constructed matrix, which is called a regular path, wherein the cumulative distance represents the similarity between the test sample P and the reference model Q, and the smaller the cumulative distance, the higher the similarity.
When a regular path is selected, the DTW algorithm needs to satisfy the constraints of continuity, monotonicity and boundary conditions, so that in the process of searching the regular path, searching can only be started from the lower left corner of a constructed matrix to the upper right corner of the matrix, and the searching process can only be monotonously performed along with the progressive time, and the middle cannot be searched across elements, so that for a matrix element (i, j), the next element to be determined can only be one of (i +1, j), (i +1, j +1) and (i, j + 1). Therefore, in the process of finding the regular path, starting from the lower left corner of the matrix, after each calculation, the element with the smallest cumulative distance D is selected as the starting point of the next calculation, and until the upper right corner of the matrix, the complete path with the smallest cumulative distance D is found, and the calculation formula is shown as formula (3).
D(i+1,j+1)＝d(i+1,j+1)+min{D(i,j+1),D(i,j),D(i+1,j)} (3)
In this embodiment, the cloud server obtains the reference model of each lane, and after obtaining the test sample according to the time variation process of the vertical acceleration in the driving process of the target vehicle, matches the test sample with the reference model of each lane based on the DTW algorithm, and can calculate and obtain the cumulative distance D between the test sample and the reference model of each lane by using the formula (3), the smaller the cumulative distance D, the higher the degree of similarity between the test sample and the reference model is, and further, the degree of similarity between the test sample and the reference model of each lane can be obtained according to the cumulative distance D.
In practical application, the situation that the reference model is longer and the test sample is shorter may occur, which may result in a decrease in matching accuracy, so that, in a possible implementation manner of the embodiment of the present invention, the cloud server may obtain, in addition to data information sensed by a sensor built in the mobile terminal from the mobile terminal, positioning information that is known by the mobile terminal based on GPS positioning technology or LBS positioning technology, so that, before the cloud server matches the test sample and the reference model by using the DTW algorithm, the cloud server may first determine a rough road section currently traveled by the target vehicle according to the obtained positioning information, segment the test sample and the reference model of each lane by an appropriate segmentation distance, further match the segmented test sample and the reference model based on the DTW algorithm, and obtain an accumulated distance D between the test sample and the reference model of each lane, and further the similarity degree of the test sample and the reference model is obtained. By segmenting the test sample and the reference model and then calculating the accumulated distance between the test sample and the reference model, the matching accuracy can be improved.
And S52, determining the lane with the highest similarity as the target lane.
In this embodiment, after the cumulative distance D between the test sample and each lane reference model is obtained through calculation by the DTW algorithm, the degree of similarity between the test sample and each lane reference model can be obtained according to the cumulative distance D, and the lane corresponding to the reference model with the highest degree of similarity to the test sample is selected as the target lane where the target vehicle is located.
For example, assume a three-lane road, in which the left (l), middle (m) and right (r) lanes correspond to the reference model, the vertical acceleration curves are represented as s (l), s (m) and s (r), respectively. And the cloud server analyzes and processes the data information acquired from the mobile terminal bound by the target vehicle to obtain a vertical acceleration curve of the target vehicle, wherein the curve is represented as S (t), and S (t) is a test sample. Based on the DTW algorithm, the cloud server respectively matches the test sample S (t) with the vertical acceleration curves S (l), S (m) and S (r) of the left lane, the middle lane and the right lane, and the calculated accumulated distances are respectively represented as D (l), D (m) and D (r). When the cumulative distance d (l) is the minimum of the three, it indicates that the test samples s (t) and s (l) are the most similar, that is, the degree of similarity between the test sample s (t) and the reference model of the left lane is the highest, and it can be determined that the left lane is the target lane, that is, the lane where the target vehicle is currently driving is the left lane. When the cumulative distance d (m) is the minimum, it indicates that the test samples s (t) and s (m) are the most similar, that is, the degree of similarity between the test sample s (t) and the reference model of the middle lane is the highest, and it may be determined that the middle lane is the target lane, that is, the lane currently traveled by the target vehicle is the middle lane. When the cumulative distance d (r) is the minimum of the three, it indicates that the test samples s (t) and s (r) are the most similar, i.e. the degree of similarity between the test sample s (t) and the reference model of the right lane is the highest, and it can be determined that the right lane is the target lane, i.e. the lane currently driven by the target vehicle is the right lane.
According to the vehicle positioning method, the accumulated distance between the test sample and the reference model of each lane is calculated by adopting a dynamic programming algorithm, the similarity between the test sample and the reference model of each lane is obtained, the lane with the highest similarity is determined as the target lane, the calculated amount can be reduced, and the matching accuracy is improved.
The purpose of vehicle positioning is to provide a technical basis for vehicle navigation, so that vehicle navigation software can implement optimal driving route recommendation and/or prompt services (such as lane change in advance, fast driving route prompt, and the like) based on lanes according to traffic conditions and planned routes, and thus, an embodiment of the present invention provides a vehicle navigation method, and fig. 7 is a flowchart of the vehicle navigation method provided by an embodiment of the present invention, where the method may be executed by navigation software installed in a mobile terminal, and the mobile terminal may include smart devices such as a smart phone and a tablet computer.
As shown in fig. 7, the vehicle navigation method may include the steps of:
and S61, positioning the target vehicle by adopting a vehicle positioning method so as to determine the target lane from the target road section where the target vehicle is located.
For a description of the adopted vehicle positioning method, reference may be made to the description of the vehicle positioning method embodiment in fig. 1 to 6 in the foregoing embodiment, and for avoiding redundancy, detailed description is not given here.
In this embodiment, by using the vehicle positioning method provided by the foregoing embodiment of the present invention, positioning of the target vehicle can be realized, and then the target lane where the target vehicle is located is determined from the target road segment where the target vehicle is located.
And S62, navigating the target vehicle according to the target road section and the target lane where the target vehicle is located.
In this embodiment, after the target road section where the target vehicle is located is determined from the target road section where the target vehicle is located by using the vehicle positioning method, the navigation software installed in the mobile terminal can navigate the target vehicle according to the target road section where the target vehicle is located and the target road section.
In the vehicle navigation method of the embodiment, the target vehicle is positioned by adopting the vehicle positioning method, so that the target lane where the target vehicle is located is determined from the target road section where the target vehicle is located, and the target vehicle is navigated according to the target road section where the target vehicle is located and the target lane. Therefore, navigation can be performed based on the positioned lane, an optimal driving route is recommended for a user in real time, the navigation accuracy is improved, and the user experience is improved.
In order to realize the embodiment, the invention further provides a vehicle positioning device.
Fig. 8 is a schematic structural diagram of a vehicle positioning device according to an embodiment of the present invention.
As shown in fig. 8, the vehicle positioning device 80 includes: an acquisition module 810, a measurement module 820, and a determination module 830. Wherein,
an obtaining module 810, configured to obtain a reference model of each lane.
The reference model can be generated according to the change of the vertical acceleration of the reference vehicle in the direction vertical to the ground along with the time when the reference vehicle runs in the lane.
And the measuring module 820 is used for measuring the vertical acceleration change process of the target vehicle along with time when the target vehicle runs so as to obtain a test sample.
And the determining module 830 is configured to determine a target lane where the target vehicle is located from the lanes according to the similarity between the test sample and the reference model of each lane.
Further, in a possible implementation manner of the embodiment of the present invention, as shown in fig. 9, on the basis of the embodiment shown in fig. 8, the obtaining module 810 may further include:
the positioning unit 811 is configured to position a target road segment where the target vehicle is located.
Optionally, in a possible implementation manner of the embodiment of the present invention, the positioning unit 811 is specifically configured to obtain positioning information from a mobile terminal bound to a target vehicle; and determining the target road section where the target vehicle is located according to the positioning information.
Optionally, in another possible implementation manner of the embodiment of the present invention, the positioning unit 811 is specifically configured to obtain final positioning information sent before a signal sent by the mobile terminal disappears; calculating a travel distance according to the travel data of the target vehicle; and determining the target road section where the target vehicle is located according to the driving distance and the final positioning information.
A selecting unit 812, configured to select a reference model of each lane in the target road segment.
Further, in a possible implementation manner of the embodiment of the present invention, as shown in fig. 9, on the basis of the embodiment shown in fig. 8, the determining module 830 may include:
and the calculating unit 831 is configured to calculate an accumulated distance between the test sample and the reference model of each lane by using a dynamic programming algorithm, so as to obtain a similarity between the test sample and the reference model of each lane.
And a determining unit 832, configured to determine the lane with the highest degree of similarity as the target lane.
It should be noted that the foregoing explanation of the embodiment of the vehicle positioning method is also applicable to the vehicle positioning device of the embodiment, and the implementation principle thereof is similar and will not be described herein again.
The vehicle positioning device of the embodiment obtains the reference models of the lanes, measures the time-varying process of the vertical acceleration of the target vehicle when the target vehicle runs to obtain the test sample, and determines the target lane where the target vehicle is located from the lanes according to the similarity between the test sample and the reference models of the lanes. Therefore, the influence of environmental factors on lane recognition accuracy can be reduced, and the accuracy and reliability of lane recognition and positioning are improved.
In order to implement the above embodiment, the present invention further provides a vehicle navigation device.
Fig. 10 is a schematic structural diagram of a car navigation device according to an embodiment of the present invention.
As shown in fig. 10, the car navigation device 100 includes: a location module 1010, and a navigation module 1020. Wherein,
the positioning module 1010 is configured to position the target vehicle by using the vehicle positioning apparatus 80 according to the foregoing embodiment, so as to determine the target lane in the target road section where the target vehicle is located.
The navigation module 1020 is configured to navigate the target vehicle according to a target road segment and a target lane where the target vehicle is located.
It should be noted that the foregoing explanation of the embodiment of the vehicle navigation method is also applicable to the vehicle navigation apparatus of the embodiment, and the implementation principle thereof is similar and will not be described herein again.
The vehicle navigation device of the embodiment locates the target vehicle by adopting the vehicle locating device to determine the target lane in the target road section where the target vehicle is located, and navigates the target vehicle according to the target road section where the target vehicle is located and the target lane. Therefore, navigation can be performed based on the positioned lane, an optimal driving route is recommended for a user in real time, the navigation accuracy is improved, and the user experience is improved.
In order to achieve the above embodiments, the present invention also proposes a non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the vehicle positioning method described in the foregoing embodiments.
In order to implement the above-mentioned embodiments, the present invention also proposes another non-transitory computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the vehicle navigation method described in the foregoing embodiments.
In order to implement the above embodiments, the present invention further provides a computer program product, wherein when the instructions in the computer program product are executed by a processor, the vehicle positioning method described in the foregoing embodiments is executed.
In order to implement the above embodiments, the present invention also proposes another computer program product, wherein when the instructions of the computer program product are executed by a processor, the vehicle navigation method described in the foregoing embodiments is executed.
In order to clearly illustrate the vehicle positioning device provided in the foregoing embodiment, an embodiment of the present invention further provides a vehicle positioning system, fig. 11 is a schematic structural diagram of the vehicle positioning system according to an embodiment of the present invention, as shown in fig. 11, as a possible implementation manner, the vehicle positioning system includes: mobile terminal and high in the clouds server. Wherein,
and the mobile terminal is used for acquiring the perception data, the positioning information and the driving data, generating the relevant information of the vehicle according to the acquired perception data and the driving data, and providing the relevant information of the vehicle to the cloud server. Specifically, fig. 12 is a schematic diagram of data collected by the mobile terminal, and as shown in fig. 12, the sensing data is data obtained by sensing by using a sensor built in the mobile terminal when the target vehicle runs, so that the cloud server can measure a time-varying process of the vertical acceleration of the target vehicle according to the sensing data to generate a test sample. The source of the driving data is the vehicle's OBD-II system and the positioning information is obtained by GPS.
And the cloud server can interact with the mobile terminal, so that the relevant information of the vehicle is obtained through the mobile terminal, and the positioned target lane is fed back to the mobile terminal. The cloud server stores a reference model. The reference model is generated according to the change of the vertical acceleration of the reference vehicle in the direction vertical to the ground along with the time when the reference vehicle runs in the lane.
Specifically, after the mobile terminal collects the sensing data, the positioning information and the driving data, the sensing data, the positioning information and the driving data are provided for the cloud server. And then the cloud server generates a test sample according to the sensing data, and the test sample indicates the process of the vertical acceleration of the target vehicle changing along with time when the target vehicle runs. And then, the cloud server estimates the road section where the target vehicle is located according to the positioning information and the driving data, acquires the reference model of each lane in the corresponding road section, and determines the target lane where the target vehicle is located from each lane according to the similarity between the test sample and the reference model of each lane.
Through the system, the influence of environmental factors on the lane identification accuracy can be reduced, the lane identification and positioning accuracy and reliability are improved, and the problem that the identification accuracy is easily interfered by environmental changes in the prior art is solved.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A vehicle positioning method, characterized by comprising the steps of:
acquiring a reference model of each lane, wherein the reference model is generated according to the change of the vertical acceleration of a reference vehicle in the direction vertical to the ground along with the time when the reference vehicle runs in the lane, and before the reference model of the lane is acquired, the reference model of the lane is established, wherein the establishing of the reference model of the lane specifically comprises the following steps: analyzing and processing data information uploaded by a mobile terminal, extracting vertical acceleration in the data information, fitting a vertical acceleration curve of a corresponding lane according to the vertical acceleration, inputting the vertical acceleration curve of the corresponding lane as training data, outputting the corresponding lane as output, and establishing a reference model of the lane;
measuring a time-varying process of a vertical acceleration of a target vehicle determined by data information sensed by a sensor built in a mobile terminal in the target vehicle to obtain a test sample while the target vehicle is running;
determining a target lane where the target vehicle is located from each lane according to the similarity between the test sample and the reference model of each lane;
wherein the obtaining of the reference model of each lane comprises:
positioning a target road section where the target vehicle is located according to the driving data of the target vehicle or the positioning information of the mobile terminal on the target vehicle;
and selecting a reference model of each lane in the target road section.
2. The vehicle positioning method according to claim 1, wherein the positioning the target road segment where the target vehicle is located comprises:
acquiring positioning information from the mobile terminal bound by the target vehicle;
and determining the target road section where the target vehicle is located according to the positioning information.
3. The vehicle positioning method according to claim 1, wherein the positioning the target road segment where the target vehicle is located comprises:
acquiring final positioning information sent by a mobile terminal before a signal sent by the mobile terminal disappears;
calculating a driving distance according to the driving data of the target vehicle;
and determining the target road section where the target vehicle is located according to the driving distance and the final positioning information.
4. The vehicle positioning method according to any one of claims 1 to 3, wherein the determining a target lane in which the target vehicle is located from the lanes according to the degree of similarity between the test sample and the reference model of each lane comprises:
calculating the accumulated distance between the test sample and the reference model of each lane by adopting a dynamic programming algorithm to obtain the similarity between the test sample and the reference model of each lane;
and determining the lane with the highest similarity as the target lane.
5. A vehicle navigation method, characterized by comprising the steps of:
positioning a target vehicle by adopting the vehicle positioning method according to any one of claims 1-4 to determine a target lane from a target road section where the target vehicle is located;
and navigating the target vehicle according to the target road section where the target vehicle is located and the target lane.
6. A vehicle positioning device, comprising:
the method comprises the steps of obtaining a reference model of each lane, wherein the reference model is generated according to the change of the vertical acceleration of a reference vehicle in the direction vertical to the ground along with time when the reference vehicle runs in the lane, and before obtaining the reference model of the lane, the method further comprises the step of establishing the reference model of the lane, wherein the step of establishing the reference model of the lane specifically comprises the following steps: analyzing and processing data information uploaded by a mobile terminal, extracting vertical acceleration in the data information, fitting a vertical acceleration curve of a corresponding lane according to the vertical acceleration, inputting the vertical acceleration curve of the corresponding lane as training data, outputting the corresponding lane as output, and establishing a reference model of the lane;
the device comprises a measuring module, a processing module and a processing module, wherein the measuring module is used for measuring the vertical acceleration of a target vehicle along with time when the target vehicle runs so as to obtain a test sample, and the vertical acceleration of the target vehicle is determined by data information sensed by a sensor built in a mobile terminal in the target vehicle;
the determining module is used for determining a target lane where the target vehicle is located from all lanes according to the similarity between the test sample and the reference model of each lane;
the acquisition module includes:
the positioning unit is used for positioning a target road section where the target vehicle is located according to the running data of the target vehicle or the positioning information of the mobile terminal on the target vehicle;
and the selection unit is used for selecting the reference model of each lane in the target road section.
7. The vehicle positioning apparatus of claim 6, wherein the positioning unit is specifically configured to:
acquiring positioning information from the mobile terminal bound by the target vehicle;
and determining the target road section where the target vehicle is located according to the positioning information.
8. The vehicle positioning apparatus of claim 6, wherein the positioning unit is specifically configured to:
acquiring final positioning information sent by a mobile terminal before a signal sent by the mobile terminal disappears;
calculating a driving distance according to the driving data of the target vehicle;
and determining the target road section where the target vehicle is located according to the driving distance and the final positioning information.
9. The vehicle localization apparatus of any one of claims 6-8, wherein the determination module comprises:
the calculation unit is used for calculating the accumulated distance between the test sample and the reference model of each lane by adopting a dynamic programming algorithm to obtain the similarity between the test sample and the reference model of each lane;
and the determining unit is used for determining the lane with the highest similarity degree as the target lane.
10. A vehicular navigation apparatus, characterized by comprising:
a positioning module, configured to position a target vehicle by using the vehicle positioning apparatus according to any one of claims 6 to 9, so as to determine a target lane located in a target road segment where the target vehicle is located;
and the navigation module is used for navigating the target vehicle according to the target road section where the target vehicle is located and the target lane.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710407858.3A CN108981729B (en) | 2017-06-02 | 2017-06-02 | Vehicle positioning method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710407858.3A CN108981729B (en) | 2017-06-02 | 2017-06-02 | Vehicle positioning method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108981729A CN108981729A (en) | 2018-12-11 |
| CN108981729B true CN108981729B (en) | 2021-11-19 |
Family
ID=64501133
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710407858.3A Active CN108981729B (en) | 2017-06-02 | 2017-06-02 | Vehicle positioning method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108981729B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113924460B (en) * | 2019-06-06 | 2024-04-26 | 北京嘀嘀无限科技发展有限公司 | System and method for determining recommendation information for service request |
| CN110400490B (en) * | 2019-08-08 | 2022-02-25 | 腾讯科技（深圳）有限公司 | Trajectory prediction method and apparatus |
| CN113538894B (en) * | 2020-10-13 | 2024-02-09 | 腾讯科技（深圳）有限公司 | Vehicle positioning data processing method, device, computer equipment and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007198961A (en) * | 2006-01-27 | 2007-08-09 | Toyota Motor Corp | Driving support apparatus |
| CN104422462A (en) * | 2013-09-06 | 2015-03-18 | 上海博泰悦臻电子设备制造有限公司 | Vehicle navigation method and vehicle navigation device |
| CN104697536A (en) * | 2015-02-12 | 2015-06-10 | 奇瑞汽车股份有限公司 | Vehicle positioning method and apparatus |
| CN105894843A (en) * | 2015-02-17 | 2016-08-24 | 赫克斯冈技术中心 | Method and system for determining a road condition |
| CN106123906A (en) * | 2016-08-17 | 2016-11-16 | 深圳市金立通信设备有限公司 | A kind of auxiliary navigation method and terminal |
| CN106373417A (en) * | 2015-07-20 | 2017-02-01 | 德韧营运有限责任公司 | System and method for generating and communicating lane information from a host vehicle to a vehicle-to-vehicle network |
| CN106441319A (en) * | 2016-09-23 | 2017-02-22 | 中国科学院合肥物质科学研究院 | System and method for generating lane-level navigation map of unmanned vehicle |
| CN106662628A (en) * | 2014-05-01 | 2017-05-10 | Fzc哥络普斯 | Positioning arrangement, method, mobile device and computer program |
-
2017
- 2017-06-02 CN CN201710407858.3A patent/CN108981729B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007198961A (en) * | 2006-01-27 | 2007-08-09 | Toyota Motor Corp | Driving support apparatus |
| CN104422462A (en) * | 2013-09-06 | 2015-03-18 | 上海博泰悦臻电子设备制造有限公司 | Vehicle navigation method and vehicle navigation device |
| CN106662628A (en) * | 2014-05-01 | 2017-05-10 | Fzc哥络普斯 | Positioning arrangement, method, mobile device and computer program |
| CN104697536A (en) * | 2015-02-12 | 2015-06-10 | 奇瑞汽车股份有限公司 | Vehicle positioning method and apparatus |
| CN105894843A (en) * | 2015-02-17 | 2016-08-24 | 赫克斯冈技术中心 | Method and system for determining a road condition |
| CN106373417A (en) * | 2015-07-20 | 2017-02-01 | 德韧营运有限责任公司 | System and method for generating and communicating lane information from a host vehicle to a vehicle-to-vehicle network |
| CN106123906A (en) * | 2016-08-17 | 2016-11-16 | 深圳市金立通信设备有限公司 | A kind of auxiliary navigation method and terminal |
| CN106441319A (en) * | 2016-09-23 | 2017-02-22 | 中国科学院合肥物质科学研究院 | System and method for generating lane-level navigation map of unmanned vehicle |
Non-Patent Citations (2)
| Title |
|---|
| 基于多源信息融合的试验场道路识别系统研究;孙觉非;《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》;20170315(第3期);第C035-638页 * |
| 孙觉非.基于多源信息融合的试验场道路识别系统研究.《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》.2017,(第3期), * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108981729A (en) | 2018-12-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101919366B1 (en) | Apparatus and method for recognizing vehicle location using in-vehicle network and image sensor | |
| US20210311490A1 (en) | Crowdsourcing a sparse map for autonomous vehicle navigation | |
| US20220011130A1 (en) | Selective retrieval of navigational information from a host vehicle | |
| US10248124B2 (en) | Localizing vehicle navigation using lane measurements | |
| CN107145578B (en) | Map construction method, device, equipment and system | |
| AU2017300097B2 (en) | Crowdsourcing and distributing a sparse map, and lane measurements for autonomous vehicle navigation | |
| EP3358303B1 (en) | An apparatus and associated methods for use in updating map data | |
| CN113710988B (en) | Method for detecting the functional capability of an environmental sensor, control unit and vehicle | |
| CN106352867B (en) | Method and device for determining the position of a vehicle | |
| US20220082403A1 (en) | Lane mapping and navigation | |
| CN103109160B (en) | For determining the method and apparatus of the position of vehicle on road surface and there is the automobile of this equipment | |
| CN110333524A (en) | Vehicle positioning method, device and equipment | |
| JP2016180980A (en) | Information processing device, program, and map data updating system | |
| CN106494393B (en) | Method for planning a supported parking maneuver | |
| WO2019092149A1 (en) | Automatic occlusion detection in road network data | |
| WO2014115531A1 (en) | Curve modeling device, curve modeling method, and automotive navigation device | |
| CN102037317A (en) | Improvements relating to navigation apparatus used in-vehicle | |
| CN104677361B (en) | A kind of method of comprehensive location | |
| CN108291814A (en) | For putting the method that motor vehicle is precisely located, equipment, management map device and system in the environment | |
| CN108981729B (en) | Vehicle positioning method and device | |
| CN114383620A (en) | Vehicle accurate position obtaining method and system, readable storage medium and vehicle | |
| US20210048819A1 (en) | Apparatus and method for determining junction | |
| US8024117B2 (en) | Map display apparatus for vehicle | |
| CN113720341B (en) | Vehicle travel route generation method, system, computer device, and storage medium | |
| CN113566834A (en) | Positioning method, positioning device, vehicle, and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |