A method and device for yaw identification and navigation route planning

Abstract

The present invention discloses a method and device for yaw identification and navigation route planning. The method comprises: obtaining positioning information and a planned navigation route of a navigated object; determining a weight coefficient using preset rules based on positioning signal speed parameters, positioning signal quality parameters, the location information of the navigated object, and the planned navigation route included in the positioning information; determining a scene weight based on the road attributes of the navigated object's current location and the weight coefficient; determining a yaw weight based on the scene weight, and determining whether the navigated object is in a yaw state based on the yaw weight. The method can accurately identify whether the vehicle navigation is in an erroneous yaw state, perform timely route calculation adjustments, and effectively suppress yaw errors.

Description

Technical Field

[0001] The present invention relates to the field of navigation technology, and in particular to a method and device for yaw identification and navigation route planning. Background Art

[0002] When travel applications (such as map navigation applications) provide navigation services to users, if the terminal device's location is found to be not on the planned navigation route, the application will generally remind the user of the deviation from the navigation route through voice prompts, etc., and guide the user back to the navigation route or recalculate the navigation route for the user.

[0003] The inventors of this application discovered that due to the accuracy of the terminal device's positioning, yaw errors can occur (the user is mistakenly judged to have yawed even though they did not). This error, on the one hand, can cause voice broadcasts and user guidance to be inconsistent with the actual situation, causing trouble for the user, and on the other hand, it can increase the number of yaw status reports and navigation route recalculations, resulting in wasted traffic and increased resource overhead for navigation services. Therefore, how to accurately determine yaw is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the Invention

[0004] In view of the above problems, the present invention is proposed to provide a method and device for yaw identification and navigation route planning that overcomes the above problems or at least partially solves the above problems.

[0005] An embodiment of the present invention provides a method for yaw identification, comprising:

[0006] Obtain the location information of the navigated object and the navigation planning route;

[0007] Determining a weight coefficient using a preset rule according to a positioning signal speed parameter, a positioning signal quality parameter, the location information of the navigated object, and a navigation planned route included in the positioning information;

[0008] Determining a scene weight according to the road attribute of the road where the navigated object is currently located and the weight coefficient;

[0009] A yaw weight is determined based on the scene weight, and whether the navigated object is in a yaw state is determined according to the yaw weight.

[0010] In some optional embodiments, a weight coefficient is determined using a preset rule based on the positioning signal speed parameter, the positioning signal quality parameter, the location information of the navigated object, and the navigation planned route included in the positioning information, including:

[0011] Determining a speed factor representing a change in the moving speed of the navigated object according to the positioning signal speed parameter included in the positioning information;

[0012] Determining a signal quality factor representing the quality of the positioning signal according to the positioning signal quality parameter included in the positioning information;

[0013] Determining a matching distance factor and an angle deviation factor based on the location information of the navigated object and the navigation planned route included in the positioning information; the matching distance factor represents a change in the distance from the positioning trajectory point of the navigated object to its matching point on the navigation planned route, and the angle deviation factor represents a difference between a cumulative change angle of the positioning trajectory of the navigated object and a cumulative change angle of the navigation planned route;

[0014] The weight coefficient is determined using a preset determination rule according to the speed factor, the signal quality factor, the matching distance factor and the angle deviation factor.

[0015] In some optional embodiments, determining a speed factor representing a change in vehicle speed based on a positioning signal speed parameter included in the positioning information includes:

[0016] Determining the moving speed of the navigated object based on the positioning signal speed parameter included in the positioning information;

[0017] The speed factor at the current moment is determined according to the moving speed of the navigated object and the speed factor determined at the previous moment.

[0018] In some optional embodiments, determining a signal quality factor representing the quality of the positioning signal according to the positioning signal quality parameter included in the positioning information includes:

[0019] According to at least one of the single point credibility, joint credibility, DQ value and maximum signal accuracy included in the positioning information, a signal quality positive factor for current signal quality judgment and a signal quality reverse factor for signal mutation processing are determined.

[0020] In some optional embodiments, determining a matching distance factor according to the location information of the navigated object and the navigation planning route included in the positioning information includes:

[0021] Determine, based on the location information of the navigated object included in the positioning information, the distance from the location point included in the location information to its matching point on the navigation planning route;

[0022] The matching distance factor at the current moment is determined according to the determined distance.

[0023] In some optional embodiments, determining the angle deviation factor according to the position information of the navigated object and the navigation planned route included in the positioning information includes:

[0024] Determine the positioning trajectory of the navigated object according to the position information of the navigated object at different times included in the user's positioning information, and obtain the signal cumulative change angle of the positioning trajectory of the navigated object at the current time in an accumulation manner;

[0025] Obtain the cumulative change angle of the road corresponding to the positioning trajectory of the navigated object in the navigation planning route;

[0026] The difference between the signal cumulative change angle and the road cumulative change angle is determined.

[0027] In some optional embodiments, determining the weight coefficient using a preset determination rule according to the speed factor, the signal quality factor, the matching distance factor, and the angle deviation factor includes:

[0028] According to the speed factor, signal quality factor, matching distance factor and angle deviation factor, a weighted average method is used to obtain the accumulated value of the weight coefficient at the current moment;

[0029] The accumulation method of the actual weight coefficient is selected according to the cumulative fitting time of the positioning trajectory of the navigated object and the navigation planning route, and the weight coefficient at the current moment is determined according to the actual weight coefficient at the previous moment, the accumulated value of the weight coefficient at the current moment and the selected accumulation method.

[0030] In some optional embodiments, a weighted average is used to obtain the accumulated value of the weight coefficient at the current moment based on the speed factor, the signal quality factor, the matching distance factor, and the angle deviation factor, including:

[0031] When it is determined that the GPS signal state is good according to the speed factor and the signal quality factor, and it is determined that the GPS track deviates from the navigation planned route according to the matching distance factor and the angle deviation factor, a weighted average of the matching distance factor and the angle deviation factor is performed to obtain an accumulated value of the weight coefficient at the current moment;

[0032] Otherwise, a weighted average is performed on the speed factor, the signal quality factor, the matching distance factor, and the angle deviation factor to obtain the accumulated value of the weight coefficient at the current moment.

[0033] In some optional embodiments, the cumulative alignment time of the navigation object's positioning trajectory and the navigation planning route is determined in the following manner:

[0034] Determine the positioning trajectory of the navigated object according to the position information of the navigated object at different times contained in the positioning information;

[0035] Determine whether the length of the fit between the positioning trajectory of the navigated object and the navigation planned route is less than a set threshold; if so, increase the value of the cumulative fit duration according to the set duration accumulation rule; if not, reduce the value of the cumulative fit duration according to the set duration accumulation rule.

[0036] In some optional embodiments, determining a scene weight according to the road attributes of the road currently located by the navigated object and the weight coefficient, determining a yaw weight based on the scene weight, and determining whether the vehicle is in a yaw state according to the yaw weight includes:

[0037] According to the road attributes of the road currently located by the navigated object, the set weight of the road scene currently located by the user is obtained, and the scene weight is calculated by multiplying the set weight and the weight coefficient;

[0038] A yaw weight is determined according to the scene weight, and when the yaw weight meets a set condition, it is determined that the navigated object is in a yaw state.

[0039] An embodiment of the present invention further provides a navigation route planning method, comprising:

[0040] When the above-mentioned yaw identification method is used to determine that the navigated object is in a yaw state, a navigation route calculation request is initiated;

[0041] A navigation planned route is received in response to the navigation route calculation request.

[0042] An embodiment of the present invention further provides a yaw identification device, comprising:

[0043] The acquisition module is used to obtain the location information of the navigated object and the navigation planning route;

[0044] A weight coefficient determination module is used to determine the weight coefficient using a preset rule based on the positioning signal speed parameter, the positioning signal quality parameter, the position information of the navigated object and the navigation planned route included in the positioning information;

[0045] A scene weight determination module, configured to determine a scene weight according to a road attribute of a road where the navigated object is currently located and the weight coefficient;

[0046] a yaw weight determination module, configured to determine a yaw weight based on the scene weight;

[0047] The yaw judgment module is used to judge whether the navigated object is in a yaw state according to the yaw weight.

[0048] An embodiment of the present invention further provides a navigation route planning device, comprising:

[0049] The above-mentioned yaw identification device;

[0050] a request module, configured to initiate a navigation route calculation request when the deviation identification device determines that the navigated object is in a deviation state;

[0051] The receiving module is used to receive the navigation planning route in response to the navigation route calculation request.

[0052] An embodiment of the present invention further provides a terminal device, comprising: the above-mentioned deviation identification device or the above-mentioned navigation route planning device.

[0053] An embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned deviation identification method or the above-mentioned navigation route planning method is implemented.

[0054] The beneficial effects of the above technical solutions provided by the embodiments of the present invention include at least:

[0055] The weight coefficient is determined based on the positioning signal speed parameters, positioning signal quality parameters, position information of the navigated object and the navigation planned route included in the positioning information of the navigated object, and the scene weight is determined based on the weight coefficient. The yaw weight is further determined based on the scene weight, and whether the navigated object is in a yaw state is judged based on the yaw weight; when determining the yaw state, multiple factors such as the speed and quality of the positioning signal and the position of the located object are taken into consideration to avoid erroneous judgments caused by the positioning accuracy of the terminal device, improve the accuracy of yaw recognition, avoid the inconvenience caused to users by the inconsistency between voice broadcast and actual situation, and greatly reduce the number of times the route is recalculated due to yaw, thereby reducing traffic waste and navigation service resource consumption.

[0056] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purposes and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0059] Figure 1 This is a flow chart of the yaw identification method in Example 1 of the present invention;

[0060] Figure 2 This is a flow chart of the yaw identification method in the second embodiment of the present invention;

[0061] Figure 3 This is a flowchart of a specific implementation of the yaw identification method in the third embodiment of the present invention;

[0062] Figure 4This is an example diagram of a speed factor change curve in the third embodiment of the present invention;

[0063] Figure 5 This is an example diagram of a positive factor change curve of the signal quality factor in the third embodiment of the present invention;

[0064] Figure 6 This is an example diagram of a reverse factor curve of the signal quality factor in the third embodiment of the present invention;

[0065] Figure 7 This is an example diagram of the cumulative lamination time change curve in Example 3 of the present invention;

[0066] Figure 8 This is an example diagram of a matching distance factor change curve in Example 3 of the present invention;

[0067] Figure 9 This is an example diagram of the angle deviation factor change curve in Example 3 of the present invention;

[0068] Figure 10 This is an example diagram of a closed road scene in Example 3 of the present invention;

[0069] Figure 11 This is a schematic diagram of a closed road scene discrimination principle in the third embodiment of the present invention;

[0070] Figure 12 This is an example of the yaw identification optimization effect in the third embodiment of the present invention. Figure 1 ;

[0071] Figure 13 This is an example of the yaw recognition optimization effect in the third embodiment of the present invention. Figure 2 ;

[0072] Figure 14 This is an example of the yaw recognition optimization effect in the third embodiment of the present invention. Figure 3 ;

[0073] Figure 15 Schematic diagram of the structure of the yaw identification device in the fifth embodiment of the present invention;

[0074] Figure 16 Schematic diagram of the structure of the navigation route planning device in the fifth embodiment of the present invention. DETAILED DESCRIPTION

[0075] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0076] In closed road scenarios, abnormal signal drift can lead to erratic navigation and incorrect route tracking, negatively impacting users. This can waste traffic and resources by requiring multiple route calculation requests. When a route is incorrectly tracked, voice announcements and user guidance often mismatch the user's actual situation, leading to user confusion and incorrect route guidance. Closed roads generally refer to roads within a certain radius without multiple exits.

[0077] In order to solve the problems of inaccurate yaw identification and low false yaw state recognition rate in the prior art, an embodiment of the present invention provides a yaw identification method that can accurately identify the yaw state and improve the false yaw suppression rate.

[0078] Example 1

[0079] The first embodiment of the present invention provides a method for identifying a yaw error, the process of which is as follows: Figure 1 As shown, the following steps are included:

[0080] Step S101: Acquire the positioning information of the navigated object and the navigation planning route.

[0081] Determining the misalignment status of a guided object (such as a vehicle) in navigation requires obtaining the vehicle's positioning information and the planned navigation route. The vehicle's positioning information includes at least one of the following: positioning signal speed parameters, positioning signal quality parameters, and the location information of the guided object. It may also include a reliability parameter. The guided object's positioning information may be GPS information.

[0082] Step S102: Determine a weight coefficient using a preset rule according to the positioning signal speed parameter, the positioning signal quality parameter, the position information of the navigated object and the navigation planned route included in the positioning information of the navigated object.

[0083] After obtaining the positioning information of the navigated object, the navigation system determines the moving speed of the navigated object, the signal quality, and the degree of match between the positioning information and the navigation plan route based on the parameters in the positioning information, and determines a weight coefficient to be used to weight the scenario weight of the deviation judgment. The process of determining the weight coefficient includes:

[0084] Determining a speed factor representing a change in the moving speed of the navigated object according to a positioning signal speed parameter included in the positioning information of the navigated object;

[0085] Determining a signal quality factor representing the quality of the positioning signal according to a positioning signal quality parameter included in the positioning information of the navigated object;

[0086] Determine a matching distance factor and an angle deviation factor based on the location information of the navigated object and the navigation planned route included in the positioning information of the navigated object; wherein the matching distance factor represents the change in distance from the positioning trajectory point of the navigated object to its matching point on the navigation planned route, and the angle deviation factor represents the difference between the cumulative change angle of the positioning trajectory of the navigated object and the cumulative change angle of the navigation planned route;

[0087] The weight coefficient is determined using a preset determination rule according to the speed factor, the signal quality factor, the matching distance factor and the angle deviation factor.

[0088] Optionally, when determining the weight coefficient, historical factors can be taken into account, that is, the weight coefficient of the previous moment is taken into account to comprehensively determine the weight coefficient of the current moment, that is, the weight coefficient of the current moment is the weighted sum of the weight coefficient of the previous moment and the weight coefficient that can be accumulated at the current moment. The weight coefficient that can be accumulated at the current moment can be called the accumulated value of the weight coefficient at the current moment. The accumulated value of the weight coefficient at the current moment can be determined by weighted averaging of the speed factor, signal quality factor, matching distance factor and angle deviation factor; or the signal status and the trajectory status of the navigated object can be judged based on the speed factor, signal quality factor, matching distance factor and angle deviation factor. When the signal status is good and the trajectory of the navigated object is in a deviated state, it is determined by weighted averaging of the matching distance factor and the angle deviation factor.

[0089] Step S103: determining a scene weight according to the road attributes and weight coefficient of the road where the navigated object is currently located.

[0090] Obtain the road scene of the navigation object, such as mountain roads, ramps, and highways. Different roads have different road attributes in different road scenes. Road attributes may include, but are not limited to, the following: road type, road grade, road width, and road direction. You can obtain one or more road attributes as needed.

[0091] The scene weight can be determined based on the road attributes and the weight coefficient determined above.

[0092] Step S104: determining a yaw weight based on the scene weight.

[0093] The scene weight is part of the yaw weight. Once the scene weight is determined, the yaw weight can be determined accordingly.

[0094] For example, an optional way to determine the navigation weight is to determine it through the matching weight (onRouteW) and the offRoute weight (offRouteW). The above-determined scenario weight is part of the offRoute weight (offRouteW), for example:

[0095] Navigation weight = onRouteW – offRouteW;

[0096] offRouteW = scene weight + other basic weights;

[0097] Therefore, yaw weight = onRouteW – offRouteW = onRouteW – scene weight – other basic weights.

[0098] If the scene weight changes, the yaw weight will also change accordingly.

[0099] Step S105: judging whether the navigated object is in a yaw state according to the yaw weight.

[0100] When the yaw weight meets certain conditions, the object is considered to be in a yaw state. Optionally, when the yaw weight is greater than a set threshold, the object is determined to be in a yaw state. For example, if the yaw weight is greater than 0, the object is considered to be in a yaw state, and subsequent yaw processing can be selected. Otherwise, it is considered not in a yaw state, that is, the object can match the navigation plan route.

[0101] The above-mentioned method of this embodiment determines a weight coefficient based on the positioning signal speed parameter, positioning signal quality parameter, location information of the navigated object, and the planned navigation route, included in the positioning information of the navigated object. The weight coefficient is then used to determine a scenario weight. A yaw weight is further determined based on the scenario weight. The yaw weight is then used to determine whether the navigated object is in a yaw state. When determining the yaw state, multiple factors such as the positioning signal speed and quality, and the location of the navigated object are considered to avoid misjudgments caused by the terminal device's positioning accuracy, improve the accuracy of yaw identification, avoid user confusion caused by inconsistencies between voice broadcasts and actual conditions, and significantly reduce the number of recalculations due to yaw, thereby reducing traffic waste and navigation service resource consumption. This method can be used to identify yaw states on closed roads, as well as in other road scenarios.

[0102] Example 2

[0103] The second embodiment of the present invention provides a closed road mis-course identification method, the process of which is as follows: Figure 2 As shown, the following steps are included:

[0104] Step S201: Acquire the positioning information of the navigated object and the navigation planning route.

[0105] In this embodiment, a vehicle is used as the navigation target, and the positioning information is GPS information. Therefore, it is necessary to obtain the vehicle's GPS information and plan a navigation route. The vehicle's GPS information includes at least one of GPS signal speed parameters, reliability parameters, signal quality parameters, and GPS location information.

[0106] Step S202: determining a speed factor according to a positioning signal speed parameter included in the positioning information of the navigated object.

[0107] The determined speed factor can be used to characterize the current speed change of the navigated object, and the speed factor can be determined by accumulation to prevent sudden changes caused by speed anomalies. Specifically, the moving speed of the navigated object can be determined based on the positioning signal speed parameter included in the positioning information; the speed factor at the current moment can be determined based on the moving speed of the navigated object and the speed factor determined at the previous moment. For example: on the basis of the speed factor determined at the previous moment, the product of the moving speed of the navigated object at the current moment and a weighting coefficient is accumulated to obtain the speed factor at the current moment. For another example: a speed ratio can be determined based on the moving speed of the navigated object at the current moment, and on the basis of the speed factor determined at the previous moment, the product of the speed ratio determined at the current moment and a weighting coefficient is accumulated to obtain the speed factor at the current moment.

[0108] Step S203: determining a signal quality factor according to the positioning signal quality parameter included in the positioning information of the navigated object.

[0109] The determined signal quality factor can be used to characterize the positioning signal quality. Specifically, the signal quality positive factor used for current signal quality judgment and the signal quality reverse factor used for signal mutation processing can be determined based on at least one of the single-point credibility, joint credibility, DQ value and maximum signal accuracy included in the positioning information.

[0110] Step S204: determining the cumulative fitting time of the navigation object positioning trajectory and the navigation planning route according to the position information of the navigation object included in the navigation object positioning information and the navigation planning route.

[0111] The cumulative fit duration can be used to characterize the match between the navigation object's location trajectory and the planned navigation route. The longer the cumulative fit duration, the greater the likelihood that the navigation object actually followed the planned navigation route. The cumulative fit duration can be determined using the following methods: determining the navigation object's location trajectory based on the navigation object's position information at different times contained in the positioning information; determining whether the length of fit between the navigation object's location trajectory and the planned navigation route is less than a set threshold; if so, increasing the cumulative fit duration according to the set duration accumulation rule; if not, decreasing the cumulative fit duration according to the set duration accumulation rule.

[0112] Step S205: determining a matching distance factor according to the location information of the navigated object and the navigation planning route.

[0113] The matching distance factor can be used to characterize the change in distance between the location trajectory of the navigated object and its matching point on the planned navigation route. To determine the matching distance factor, the distance between the location information of the navigated object included in the positioning information and its matching point on the planned navigation route can be determined based on the location information of the navigated object. The current matching distance factor is then determined based on this determined distance.

[0114] Optionally, when determining the matching distance factor, the cumulative fitting duration may also be considered, that is, a weighted calculation is performed on the matching distance factor determined at the current moment according to the determined cumulative fitting duration.

[0115] Step S206: determining an angle deviation factor according to the position information of the navigated object included in the positioning information of the navigated object and the navigation planning route.

[0116] The angle deviation factor can be used to characterize the difference between the cumulative angle change of the navigation object's positioning trajectory and the cumulative angle change of the navigation-planned route. To determine the angle deviation factor, the navigation object's positioning trajectory can be determined based on the navigation object's position information at different times, as included in the user's positioning information. The cumulative angle change of the signal of the navigation object's positioning trajectory at the current moment is obtained through accumulation. The cumulative angle change of the road corresponding to the navigation object's positioning trajectory in the navigation-planned route is then obtained. The difference between the cumulative angle change of the signal and the cumulative angle change of the road is then determined to yield the angle deviation factor.

[0117] Optionally, when determining the angle deviation factor, the cumulative fitting duration may also be considered, that is, a weighted calculation is performed on the angle deviation factor determined at the current moment according to the determined cumulative fitting duration.

[0118] Step S207: Determine a weight coefficient using a preset determination rule according to the speed factor, the signal quality factor, the matching distance factor, and the angle deviation factor.

[0119] In this step, the weight coefficient at the current moment is determined. The weight coefficient at the current moment can be obtained by weighted accumulation of the weight coefficients at the previous moment. Specifically:

[0120] According to the speed factor, signal quality factor, matching distance factor and angle deviation factor, the weighted average method is adopted to obtain the cumulative value of the weight coefficient at the current moment; the accumulation method of the actual weight coefficient is selected according to the cumulative fitting time of the positioning trajectory of the navigated object and the navigation planned route, and the weight coefficient at the current moment is determined according to the actual weight coefficient at the previous moment, the cumulative value of the weight coefficient at the current moment and the selected accumulation method.

[0121] Among them, according to the speed factor, signal quality factor, matching distance factor and angle deviation factor, a weighted average method is used to obtain the accumulated value of the weight coefficient at the current moment, including:

[0122] When the GPS signal status is determined to be good based on the speed factor and the signal quality factor, and the GPS track is determined to deviate from the navigation planned route based on the matching distance factor and the angle deviation factor, the matching distance factor and the angle deviation factor are weighted averaged to obtain the current weight coefficient; otherwise, the speed factor, signal quality factor, matching distance factor, and angle deviation factor are weighted averaged to obtain the current weight coefficient.

[0123] When determining the current weight coefficient, the cumulative alignment duration is taken into account. If the cumulative alignment duration, as determined based on the accumulated alignment duration, meets the set conditions, different weight coefficients can be used to accumulate the actual weight coefficient at the previous moment and the accumulated weight coefficient at the current moment. This allows for the appropriate weight coefficient accumulation method to be selected based on the alignment between the navigated object and the planned navigation route, allowing for more accurate determination of whether the navigated object is yawed, further reducing erroneous yaw identification and the false yaw identification rate.

[0124] Step S208: determining a scene weight according to the road attribute of the road where the navigated object is currently located and the weight coefficient.

[0125] Get the set weight of the road scene the user is currently in, and calculate the scene weight by multiplying the set weight and the weight coefficient.

[0126] Different road scenarios require different weights. For example, closed road scenarios also have different characteristics, such as mountain roads and ramps. The road characteristics of closed roads vary across different scenarios. For example, if the weight of a closed road scenario is set to 60, the scenario weight is equal to 60 * weight coefficient.

[0127] Step S209: Determine the yaw weight according to the scene weight.

[0128] Step S210: determining whether the navigated object is in a yaw state according to the yaw weight.

[0129] Example 3

[0130] The third embodiment of the present invention provides a specific implementation process of the above-mentioned deviation identification method, and takes the positioning information of the navigated object as the GPS information of the vehicle as an example for explanation. The process is as follows: Figure 3 As shown, the following steps are included:

[0131] Step S301: Obtain the vehicle's GPS information and navigation planning route.

[0132] Step S302: Determine a speed factor representing a current vehicle speed change according to the GPS signal speed parameter included in the GPS information.

[0133] speedRatio is the current vehicle speed ratio, such as Figure 4 is the proportional value change curve. When the vehicle speed exceeds a certain value, the proportional value can become a constant, for example Figure 4 As shown, when the vehicle speed exceeds 50km / h, the ratio is 1. Figure 4 Where R_spd is the speed factor variable.

[0134] The cumulative form can be used to prevent abnormal speed changes, that is:

[0135] mRatioSpeed = C1 * mRatioSpeed + C2 * speedRatio; where mRatioSpeed is the accumulated speed factor, and C1 and C2 are configurable constants. It is calculated by weightedly adding the accumulated speed factor from the previous moment to the current vehicle speed ratio. In the above formula, mRatioSpeed on the left side of the equal sign represents the current accumulated result, while the right side corresponds to the previous accumulated result.

[0136] Step S303: Determine a signal quality factor representing the quality of the GPS signal according to the GPS signal quality parameters included in the GPS information.

[0137] The signal quality factor can take the maximum value of single-point credibility, joint credibility, DQ value, and signal accuracy to prevent signal jumps and untimely DQ response.

[0138] like Figure 5 The figure shows the positive factor of the signal quality factor, which is used to determine the current signal quality. R_dq is the quality factor variable. The better the signal quality, the smaller the positive factor. It starts from a set value (for example, 1) and approaches 0.

[0139] like Figure 6 The figure shows the inverse factor of the signal quality factor, which is used to handle signal mutations. R_dq is the quality factor variable. The better the signal quality, the larger the inverse factor, starting from 0 and approaching a set value (e.g., 1).

[0140] Step 304: Determine the cumulative fitting time of the vehicle GPS trajectory and the navigation planned route based on the vehicle's GPS location information and the navigation planned route included in the GPS information.

[0141] The cumulative alignment duration represents the duration of alignment between the GPS signal trajectory and the actual road, and is a factor in the duration of the signal alignment. The corresponding matching point on the road for each GPS trajectory point is determined based on the location information included in the GPS information, including the coordinates and direction of the location point. Whether the cumulative alignment duration is accumulated is determined based on whether the distance between the trajectory point and the matching point meets preset conditions.

[0142] For example: the moving distance of the matching point is greater than 1m and the moving distance of the GPS signal is within 2 times the moving distance of the matching point.

[0143] For example, the accumulation method can be: if the matching distance is less than or equal to 25m, the accumulation time is increased to a maximum of 30s; if the matching distance is greater than 25m, the time is reduced to a maximum of 0s (to prevent the long guidance time from accumulating too long and the subsequent decay is slow).

[0144] like Figure 7 This is the cumulative fitting time (R_delateTime) change curve, which lasts for 20 seconds or more, with a maximum value of 2. The longer the time, the more reliable it is:

[0145] When R_delateTime is greater than the set value (for example, 1) (about 15s) and the signal quality is good (for example, R_dq is greater than 0.5), since the position and angle difference factors are small (for example, less than 0.5), the speed and angle difference factors are magnified by R_delateTime times.

[0146] Step S305: Determine a matching distance factor representing a change in the distance from the vehicle's GPS trajectory point to its matching point on the navigation planning route based on the vehicle's GPS location information and the navigation planning route included in the GPS information.

[0147] The matching distance refers to the distance from the GPS track point to the corresponding matching point on the road, and distRatio is the current distance ratio, such as Figure 8 is the ratio value change curve. When the matching distance exceeds a certain value, the ratio value can approach 0. Figure 8 R_dist is the matching distance factor variable.

[0148] The cumulative form can be used to prevent abnormal distance mutations, that is:

[0149] mRatioDist = D1 * mRatioDist + D2 * distRati; where mRatioDist is the cumulative result of the matching distance factor, and D1 and D2 are configurable constants. It is calculated by weightedly accumulating the cumulative result of the matching distance factor at the previous moment and the distance ratio at the current moment. In the above formula, mRatioDist on the left side of the equal sign represents the cumulative result at the current moment, while the right side corresponds to the cumulative result at the previous moment.

[0150] Step S306: Determine an angle deviation factor representing the difference between the cumulative change angle of the vehicle's GPS trajectory and the cumulative change angle of the navigation planned route based on the vehicle's GPS position information and the navigation planned route included in the GPS information.

[0151] The angle deviation factor can take the maximum of the following two angle deviation values:

[0152] 1) Difference between the cumulative change angle of the road and the cumulative change angle of the signal;

[0153] 2) Minimum difference between signal angle, movement angle and road angle;

[0154] diffDeltaAziRatio is the current angle deviation ratio, such as Figure 9 The following is a curve showing the change of the proportional value. When the angle deviation exceeds a certain value, the proportional value can approach 0. Figure 9 Where R_difftAzi is the angle deviation factor variable.

[0155] The cumulative form can be used to prevent abnormal angle changes, that is:

[0156] mRatioDiffDeltaAzi = E1*mRatioDiffDeltaAzi + E2*diffDeltaAziRatio; where mRatioDiffDeltaAzi is the cumulative result of the angle deviation factor, and E1 and E2 are configurable constants. This is calculated by weightedly accumulating the cumulative result of the angle deviation factor at the previous moment and the angle deviation ratio at the current moment. In the above formula, mRatioDiffDeltaAzi on the left side of the equal sign represents the current cumulative result, while the right side corresponds to the previous cumulative result.

[0157] Step S307: Obtain the accumulated value of the weight coefficient at the current moment by weighted averaging according to the speed factor, the signal quality factor, the matching distance factor and the angle deviation factor.

[0158] After the speed factor (which can be represented by the variable R_spd), the signal quality factor (which can be represented by the variable R_dq), the matching distance factor (which can be represented by the variable R_dist) and the angle deviation factor (which can be represented by the variable R_diffAzi), the accumulated value of the weight coefficient at the current moment (currRatio) can be calculated:

[0159] In general, the average value of R_spd, R_dq (reverse), R_dist, and R_diffAzi is taken as the accumulated value of the weight coefficient at the current moment;

[0160] In the following cases, the average of R_diffAzi and R_dist is used as the cumulative weight coefficient for the current moment: The signal condition is good and the track has deviated from the road. If ∑△R_dq (positive) is greater than a set value (e.g., 0.5) and ∑△R_spd is greater than a set value (e.g., 0.4), the signal condition is considered to be continuously good. If either ∑△R_diffAzi or ∑△R_dist is less than 0, the track is considered to have deviated from the route. In this case, the proportion of R_diffAzi and R_dist can be increased when calculating the current weight coefficient. Therefore, the average of R_diffAzi and R_dist is used as the cumulative weight coefficient for the current moment.

[0161] Among them, ∑△ represents the cumulative change, which can represent the continuous increasing (decreasing) state.

[0162] ∑△R_spd: The difference between the current R_spd and the previous R_spd is accumulated to determine whether the speed is continuously increasing (decreasing);

[0163] ∑△R_dist: The difference between the current moment R_dist and the previous moment R_dist is accumulated to determine whether the distance continues to increase (decrease);

[0164] ∑△R_diffAzi: The difference between the current R_diffAzi and the previous R_diffAzi is accumulated to determine whether the angle deviation continues to increase (decrease);

[0165] ∑△R_dq (forward): The difference between the current R_dq and the previous R_dq is accumulated to determine whether the signal quality is continuously increasing (decreasing);

[0166] For example, if the ∑△* is greater than 0, it is increasing; if the ∑△* is less than 0, it is decreasing. ∑△* represents one of ∑△R_spd, ∑△R_dist, ∑△R_diffAzi, and ∑△R_dq (positive direction).

[0167] Step S308: Select the accumulation method of the actual weight coefficient according to the cumulative fitting time of the vehicle positioning trajectory and the navigation planning route, and determine the weight coefficient at the current moment according to the actual weight coefficient at the previous moment, the accumulated value of the weight coefficient at the current moment and the selected accumulation method.

[0168] The current weight coefficient (R_w) is calculated based on the accumulated weight coefficient value and the accumulated fitting duration, which can also be called the actual weight coefficient at the current moment:

[0169] When the cumulative fitting time (R_deltaTime) is greater than the set value (e.g., 1), the current actual weight coefficient = R_deltaTime * the previous actual weight coefficient + K * the current accumulated weight coefficient / R_deltaTime. In this case, the weight coefficient R_w needs to be decayed faster; K is the set value.

[0170] When the cumulative fitting time (R_deltaTime) is not greater than the set value (for example: 1), the actual weight coefficient at the current moment = M * the actual weight coefficient at the previous moment + N * the accumulated value of the weight coefficient at the current moment; M and N are the set values.

[0171] The actual weight coefficient (R_w) at the current moment is obtained above. If R_deltaTime is greater than the set value, such as 0.5, it is considered that the signal has a sudden change. Then the actual weight coefficient (R_w) at the current moment is added to R_dq (reverse) by a maximum of the specified value, that is, the weight coefficient range of R_dq (reverse) is [0, specified value].

[0172] When the signal movement distance is greater than a certain value of the speed conversion distance, or when the current angle difference factor and distance factor are less than the actual angle difference factor and distance factor and the ratio reaches a certain value, it can be regarded as a signal position or angle jump, and the signal is considered to have a mutation.

[0173] Step S309: Determine the scene weight according to the road attributes and weight coefficient of the road where the vehicle is currently located.

[0174] The scene weight can be obtained by multiplying the weight coefficient by the set weight of different road scenes. Different set weights can be set according to different scenes and road properties.

[0175] Taking a closed road as an example, the road attribute characteristics [targetScene] in different situations are also different:

[0176] For example, the attributes of a ramp may include:

[0177] Road type linktype = ordinary road;

[0178] Road class = highway | main street, urban expressway | main road | provincial road;

[0179] Road form formway = elevated JCT | ramp | ramp.

[0180] For example, the road attribute characteristics of a mountain road may include:

[0181] Road type linktype = ordinary road;

[0182] Road class = provincial road | urban secondary road;

[0183] Road form formway = ordinary road;

[0184] Traffic direction mLineDir = two-way traffic;

[0185] The number of lanes mLaneNum≤2;

[0186] Road width mRoadWidth≤6m;

[0187] When the above road attribute characteristics are met, the scene is considered to be a closed road if the following conditions are also met:

[0188] The angle of the shape point is large (you can set a judgment condition, for example, if the total length of the link is greater than 1.5 times the distance between the start and end points, it is considered that the shape point angle is large)

[0189] Out-degree restriction [getHasForkInrange]: The links within 20m before and after the matching point have an out-degree of 1, that is, there is no fork.

[0190] For various possible road scenarios such as ramps, mountain roads, and tunnels, after obtaining the set weight of the road scenario the vehicle is currently in, the scenario weight can be obtained by calculating the product of the set weight and the weight coefficient.

[0191] Reference Figure 10 The ramps shown in the figure have no forks and belong to the closed road scene. The ramps with forks do not belong to the closed road scene and do not meet the out-degree constraint. When judging whether there is a fork, you can set a judgment length range, for example: the links within 20m before and after the matching point have no forks, such as Figure 11 As shown in the figure, if there is a fork within 20m, it does not meet the out-degree restriction of 1 and does not belong to the closed road scenario.

[0192] Step S310: Determine the yaw weight according to the scene weight.

[0193] Since the scene weight is part of the yaw weight, when the scene weight changes, the yaw weight will also change accordingly. After the scene weight is determined, the yaw weight can also be determined accordingly. The yaw weight can be equal to the matching weight + scene weight + other weights.

[0194] Step S311: Determine whether the vehicle is in a yaw state according to the yaw weight.

[0195] When the yaw weight is greater than a set threshold, it is determined that the vehicle is in a yaw state, such as the yaw weight.

[0196] For example, the constant set above can be expressed as 0.2f, where f is a form of expression for calculation data and 0.2f represents a floating-point value of 0.2.

[0197] The yaw identification methods provided in Examples 1, 2, and 3 above can more accurately identify whether the navigated object is in a yaw state, reducing the possibility of yaw identification errors, thereby effectively suppressing yaw and initiating a reroute calculation process when a yaw state occurs. Since erroneous yaw identifications are reduced, reroute calculations and erroneous yaw reports are also reduced.

[0198] The false yaw suppression rate refers to the ratio of incorrect yaw identification results that are successfully suppressed, that is, the probability that the yaw identification method of the present application can accurately identify a situation where the existing yaw identification method determines that the yaw state is not actually a yaw state. Figure 12 、 Figure 13 、 Figure 14 This is an example of the yaw recognition optimization effect. The black arrows (the darker arrows in the figure) represent the GPS position points, and the gray arrows (the lighter arrows in the figure) represent the matching points of the vehicle on the road. The left half is the recognition result after optimization, and the left half is the recognition result before optimization. Obviously, after optimization, the inconsistency between the GPS position points and the matching points of the vehicle on the road is greatly reduced. Figure 12 Some GPS positions starting from the position point in the left box are inconsistent with the matching points of the vehicle on the road, which means that the vehicle is not accurately positioned on the correct road. Figure 13 Some of the positions in the left box are not accurately located on the correct road. Figure 14 The yaw occurred at the ramp and was successfully suppressed after the corresponding optimization, so there is no need to recalculate the route and the vehicle can continue to follow the navigation route. Figure 12 、 Figure 13 、 Figure 14 The optimization results shown in the figure show that before the optimization, the signal fit was low, the movement trajectory changed greatly, and there were many incorrect yaw identifications. After the optimization, the signal fit was greatly improved and the incorrect yaw identifications were significantly reduced.

[0199] Example 4

[0200] Embodiment 4 of the present invention provides a navigation route planning method, which uses the yaw identification method provided in embodiment 1, embodiment 2, or embodiment 3 to determine whether the navigated object is in a yaw state. When the navigated object is in a yaw state, a navigation route calculation request is initiated; and a navigation planned route is received in response to the navigation route calculation request.

[0201] When sending the navigation route calculation request, the navigation terminal can send the navigation route calculation request to the route calculation module in the map server. The route calculation module triggers the re-route calculation process according to the navigation route calculation request and provides the navigation terminal with the navigation planned route obtained after the re-route calculation.

[0202] Example 5

[0203] Based on the same inventive concept, an embodiment of the present invention further provides a deviation identification device, which can be set in a terminal device, which can be a user's personal computer, PAD, mobile terminal, etc., or a vehicle-mounted terminal or vehicle-mounted navigation device, etc. The structure of the device is as follows Figure 15 Shown, including:

[0204] An acquisition module 11 is used to obtain the location information of the navigated object and the navigation planning route;

[0205] The weight coefficient determination module 12 is configured to determine the weight coefficient using a preset rule based on the positioning signal speed parameter, the positioning signal quality parameter, the position information of the navigated object, and the navigation planned route included in the positioning information;

[0206] A scene weight determination module 13 is configured to determine a scene weight according to a road attribute of a road where the navigated object is currently located and the weight coefficient;

[0207] a yaw weight determination module 14, configured to determine a yaw weight based on the scene weight;

[0208] The yaw judgment module 15 is used to judge whether the navigated object is in a yaw state according to the yaw weight.

[0209] In some optional embodiments, the weight coefficient determination module 12 is specifically configured to:

[0210] Determining a speed factor representing a change in the moving speed of the navigated object according to the positioning signal speed parameter included in the positioning information;

[0211] Determining a signal quality factor representing the quality of the positioning signal according to the positioning signal quality parameter included in the positioning information;

[0212] Determining a matching distance factor and an angle deviation factor based on the location information of the navigated object and the navigation planned route included in the positioning information; the matching distance factor represents a change in the distance from the positioning trajectory point of the navigated object to its matching point on the navigation planned route, and the angle deviation factor represents a difference between a cumulative change angle of the positioning trajectory of the navigated object and a cumulative change angle of the navigation planned route;

[0213] The weight coefficient is determined using a preset determination rule according to the speed factor, the signal quality factor, the matching distance factor and the angle deviation factor.

[0214] In some optional embodiments, the weight coefficient determination module 12 is specifically configured to:

[0215] Determining the moving speed of the navigated object based on the positioning signal speed parameter included in the positioning information;

[0216] The speed factor at the current moment is determined according to the moving speed of the navigated object and the speed factor determined at the previous moment.

[0217] In some optional embodiments, the weight coefficient determination module 12 is specifically configured to:

[0218] According to at least one of the single point credibility, joint credibility, DQ value and maximum signal accuracy included in the positioning information, a signal quality positive factor for current signal quality judgment and a signal quality reverse factor for signal mutation processing are determined.

[0219] In some optional embodiments, the weight coefficient determination module 12 is specifically configured to:

[0220] Determine, based on the location information of the navigated object included in the positioning information, the distance from the location point included in the location information to its matching point on the navigation planning route;

[0221] The matching distance factor at the current moment is determined according to the determined distance.

[0222] In some optional embodiments, the weight coefficient determination module 12 is specifically configured to: determine the positioning trajectory of the navigated object according to the position information of the navigated object at different times included in the user's positioning information, and obtain the signal cumulative change angle of the positioning trajectory of the navigated object at the current time in an accumulation manner;

[0223] Obtain the cumulative change angle of the road corresponding to the positioning trajectory of the navigated object in the navigation planning route;

[0224] The difference between the signal cumulative change angle and the road cumulative change angle is determined.

[0225] In some optional embodiments, the weight coefficient determination module 12 is specifically configured to:

[0226] According to the speed factor, signal quality factor, matching distance factor and angle deviation factor, a weighted average method is used to obtain the accumulated value of the weight coefficient at the current moment;

[0227] The accumulation method of the actual weight coefficient is selected according to the cumulative fitting time of the positioning trajectory of the navigated object and the navigation planning route, and the weight coefficient at the current moment is determined according to the actual weight coefficient at the previous moment, the accumulated value of the weight coefficient at the current moment and the selected accumulation method.

[0228] The weight coefficient determination module 12 is specifically configured to include:

[0229] When it is determined that the GPS signal state is good according to the speed factor and the signal quality factor, and it is determined that the GPS track deviates from the navigation planned route according to the matching distance factor and the angle deviation factor, a weighted average of the matching distance factor and the angle deviation factor is performed to obtain an accumulated value of the weight coefficient at the current moment;

[0230] Otherwise, a weighted average is performed on the speed factor, the signal quality factor, the matching distance factor, and the angle deviation factor to obtain the accumulated value of the weight coefficient at the current moment.

[0231] The weight coefficient determination module 12 is specifically used to determine the cumulative alignment time of the navigation object's positioning trajectory and the navigation planning route in the following manner:

[0232] Determine the positioning trajectory of the navigated object according to the position information of the navigated object at different times contained in the positioning information;

[0233] Determine whether the length of the fit between the positioning trajectory of the navigated object and the navigation planned route is less than a set threshold; if so, increase the value of the cumulative fit duration according to the set duration accumulation rule; if not, reduce the value of the cumulative fit duration according to the set duration accumulation rule.

[0234] In some optional embodiments, the scene weight determination module 13 is specifically used to obtain the set weight of the user's current road scene based on the road attributes of the road where the navigated object is currently located, and calculate the scene weight by multiplying the set weight and the weight coefficient.

[0235] Based on the same inventive concept, an embodiment of the present invention also provides a navigation route planning device, which can be set in a terminal device, which can be a user's personal computer, PAD, mobile terminal, etc., or a vehicle-mounted terminal or vehicle-mounted navigation device, etc. The structure of the device is as follows Figure 16 Shown, including:

[0236] The yaw identification device 10 is used to determine whether the navigated object is in a yaw state; Figure 15 Related description.

[0237] The request module 21 is configured to initiate a navigation route calculation request when the deviation identification device determines that the navigated object is in a deviation state;

[0238] The receiving module 22 is configured to receive a navigation planning route in response to the navigation route calculation request.

[0239] Regarding the closed road mis-course deviation identification device and closed road mis-course deviation suppression implementation system in the above-mentioned embodiments, the specific manner in which each module or device performs operations has been described in detail in the embodiments of the method and will not be elaborated here.

[0240] An embodiment of the present invention further provides a terminal device, comprising the above-mentioned deviation identification device or the above-mentioned navigation route planning device.

[0241] An embodiment of the present invention further provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned deviation identification method or the above-mentioned navigation route planning method is implemented.

[0242] The above-mentioned method and device of the embodiment of the present invention greatly improve the yaw suppression rate in various road scenarios. For example, for the ramp scenario of a closed road, the proportion of false yaw is usually as high as 13% or even higher. After adopting the above-mentioned method, the ramp false yaw suppression rate exceeds 50%. In other words, the false yaw identification is reduced by more than 50%, which greatly improves the accuracy of yaw identification and reduces the time and proportion of yaw occurrence.

[0243] Unless otherwise specifically stated, terms such as process, calculate, compute, determine, display, and the like may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, that manipulate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within the memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0244] It should be understood that the specific order or hierarchy of steps in the disclosed processes is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0245] In the foregoing detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention comprises less than all the features of any individual disclosed embodiment. The appended claims are therefore hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0246] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein may be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, the various illustrative components, blocks, modules, circuits, and steps described above are generally described around their functions. Whether such functions are implemented as hardware or software depends on the specific application and the design constraints imposed on the entire system. A skilled person may implement the described functions in an adaptable manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of this disclosure.

[0247] The steps of the methods or algorithms described in conjunction with the embodiments herein may be directly embodied as hardware, software modules executed by a processor, or a combination thereof. The software module may be located in a RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, register, hard disk, removable disk, CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and storage medium may also be present in a user terminal as discrete components.

[0248] For software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or external to the processor. In the latter case, it is communicatively coupled to the processor via various means, which are well known in the art.

[0249] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purposes of describing the above embodiments, but one of ordinary skill in the art will recognize that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to encompass all such changes, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent the term "comprising" is used in the specification or claims, the term is intended to be encompassed in a manner similar to the term "including," as explained in terms of "including," used as a transitional word in the claims. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or."

Claims

1. A yaw identification method, comprising: Obtain the location information of the navigated object and the navigation planning route; Determine a speed factor, a signal quality factor, a matching distance factor, and an angle deviation factor based on a positioning signal speed parameter, a positioning signal quality parameter, the location information of the navigated object, and a navigation planned route included in the positioning information; and determine a weight coefficient using a preset rule based on the speed factor, the signal quality factor, the matching distance factor, and the angle deviation factor. Determining a scene weight according to the road attribute of the road where the navigated object is currently located and the weight coefficient; A yaw weight is determined based on the scene weight, and whether the navigated object is in a yaw state is determined according to the yaw weight.

2. The method according to claim 1, wherein Determining a speed factor, a signal quality factor, a matching distance factor, and an angle deviation factor based on a positioning signal speed parameter, a positioning signal quality parameter, location information of a navigated object, and a navigation planning route included in the positioning information includes: Determining a speed factor representing a change in the moving speed of the navigated object according to the positioning signal speed parameter included in the positioning information; Determining a signal quality factor representing the quality of the positioning signal according to the positioning signal quality parameter included in the positioning information; Based on the position information of the navigated object and the navigation planned route included in the positioning information, a matching distance factor and an angle deviation factor are determined; the matching distance factor represents the change in distance from the positioning trajectory point of the navigated object to its matching point on the navigation planned route, and the angle deviation factor represents the difference between the cumulative change angle of the positioning trajectory of the navigated object and the cumulative change angle of the navigation planned route.

3. The method according to claim 2, wherein: The determining of a speed factor representing a change in vehicle speed based on a positioning signal speed parameter included in the positioning information includes: Determining the moving speed of the navigated object based on the positioning signal speed parameter included in the positioning information; The speed factor at the current moment is determined according to the moving speed of the navigated object and the speed factor determined at the previous moment.

4. The method according to claim 2, wherein: The determining, according to the positioning signal quality parameter included in the positioning information, a signal quality factor representing the quality of the positioning signal includes: According to at least one of the single point credibility, joint credibility, DQ value and maximum signal accuracy included in the positioning information, a signal quality positive factor for current signal quality judgment and a signal quality reverse factor for signal mutation processing are determined.

5. The method according to claim 2, wherein: Determining a matching distance factor based on the location information of the navigated object and the navigation planning route included in the positioning information includes: Determine, based on the location information of the navigated object included in the positioning information, the distance from the location point included in the location information to its matching point on the navigation planning route; The matching distance factor at the current moment is determined according to the determined distance.

6. The method of claim 2, wherein: Determining an angle deviation factor according to the position information of the navigated object and the navigation planning route included in the positioning information includes: Determine the positioning trajectory of the navigated object according to the position information of the navigated object at different times included in the user's positioning information, and obtain the signal cumulative change angle of the positioning trajectory of the navigated object at the current time in an accumulation manner; Obtain the cumulative change angle of the road corresponding to the positioning trajectory of the navigated object in the navigation planning route; The difference between the signal cumulative change angle and the road cumulative change angle is determined.

7. The method of claim 2, wherein: The weight coefficient is determined by using a preset determination rule according to the speed factor, the signal quality factor, the matching distance factor and the angle deviation factor, including: According to the speed factor, signal quality factor, matching distance factor and angle deviation factor, a weighted average method is used to obtain the accumulated value of the weight coefficient at the current moment; The accumulation method of the actual weight coefficient is selected according to the cumulative fitting time of the positioning trajectory of the navigated object and the navigation planning route, and the weight coefficient at the current moment is determined according to the actual weight coefficient at the previous moment, the accumulated value of the weight coefficient at the current moment and the selected accumulation method.

8. The method of claim 7, wherein: Based on the speed factor, signal quality factor, matching distance factor, and angle deviation factor, a weighted average method is used to obtain the accumulated value of the weight coefficient at the current moment, including: When it is determined that the GPS signal state is good according to the speed factor and the signal quality factor, and it is determined that the GPS track deviates from the navigation planned route according to the matching distance factor and the angle deviation factor, a weighted average of the matching distance factor and the angle deviation factor is performed to obtain an accumulated value of the weight coefficient at the current moment; Otherwise, a weighted average is performed on the speed factor, the signal quality factor, the matching distance factor, and the angle deviation factor to obtain the accumulated value of the weight coefficient at the current moment.

9. The method of claim 7, wherein: The cumulative alignment time between the navigation object's positioning trajectory and the navigation planning route is determined as follows: Determine the positioning trajectory of the navigated object according to the position information of the navigated object at different times contained in the positioning information; Determine whether the length of the fit between the positioning trajectory of the navigated object and the navigation planned route is less than a set threshold; if so, increase the value of the cumulative fit duration according to the set duration accumulation rule; if not, reduce the value of the cumulative fit duration according to the set duration accumulation rule.

10. The method according to any one of claims 1 to 8, wherein: Determining a scene weight according to a road attribute of a current road on which the navigated object is located and the weight coefficient, determining a yaw weight based on the scene weight, and determining whether the vehicle is in a yaw state according to the yaw weight, including: According to the road attributes of the road currently located by the navigated object, the set weight of the road scene currently located by the user is obtained, and the product of the set weight and the weight coefficient is calculated to obtain the scene weight; A yaw weight is determined according to the scene weight, and when the yaw weight meets a set condition, it is determined that the navigated object is in a yaw state.

11. A navigation route planning method, comprising: When the yaw identification method according to any one of claims 1 to 10 is used to determine that the navigated object is in a yaw state, a navigation route calculation request is initiated; A navigation planned route is received in response to the navigation route calculation request.

12. A yaw identification device, comprising: The acquisition module is used to obtain the location information of the navigated object and the navigation planning route; a weight coefficient determination module, configured to determine a speed factor, a signal quality factor, a matching distance factor, and an angle deviation factor based on a positioning signal speed parameter, a positioning signal quality parameter, the location information of the navigated object, and a navigation planned route included in the positioning information; and determine a weight coefficient using a preset rule based on the speed factor, the signal quality factor, the matching distance factor, and the angle deviation factor; A scene weight determination module, configured to determine a scene weight according to a road attribute of a road where the navigated object is currently located and the weight coefficient; a yaw weight determination module, configured to determine a yaw weight based on the scene weight; The yaw judgment module is used to judge whether the navigated object is in a yaw state according to the yaw weight.

13. A navigation route planning device, comprising: The yaw identification device according to claim 12; a request module, configured to initiate a navigation route calculation request when the deviation identification device determines that the navigated object is in a deviation state; The receiving module is used to receive the navigation planning route in response to the navigation route calculation request.

14. A terminal device comprising: The deviation identification device according to claim 12 or the navigation route planning device according to claim 13.

15. A computer storage medium storing computer executable instructions, wherein the computer executable instructions, when executed by a processor, implement the deviation identification method according to any one of claims 1 to 10 or the navigation route planning method according to claim 11.

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