Vehicle position determination method, device and equipment

Abstract

The invention provides a vehicle position determination method, device and equipment, and relates to the computer technology, in particular to automatic driving and intelligent transportation. According to the specific implementation scheme, when it is determined that a current road where a vehicle is located currently has an auxiliary road according to vehicle position coordinates, the number of auxiliary road lanes and the number of main road lanes of the current road in a high-precision map are obtained; determining the final position of the vehicle according to the side road lane number and the main road lane number; wherein the final position of the vehicle represents whether the vehicle is outside the geo-fence. Whether the vehicle is outside the geo-fence can be accurately determined.

Description

Technical Field

[0001] The present disclosure relates to automatic driving and intelligent transportation in computer technology, and in particular to a method, device and equipment for determining a vehicle position. Background Art

[0002] An assisted driving system is deployed in the vehicle, and control of the assisted driving system needs to be based on whether the vehicle is outside the geo-fence, that is, it needs to be determined whether it is outside the geo-fence.

[0003] Furthermore, there is an urgent need for a solution that can accurately determine whether a vehicle is outside a geographic fence. Summary of the invention

[0004] The present disclosure provides a method, apparatus and device for accurately determining whether a vehicle is outside a geographic fence.

[0005] According to a first aspect of the present disclosure, a method for determining a vehicle position is provided, comprising:

[0006] When it is determined according to the vehicle position coordinates that the current road where the vehicle is currently located has an auxiliary road, the number of auxiliary road lanes and the number of main road lanes of the current road in the high-precision map are obtained;

[0007] The final position of the vehicle is determined according to the number of lanes on the auxiliary road and the number of lanes on the main road; wherein the final position of the vehicle indicates whether the vehicle is outside the geographic fence.

[0008] According to a second aspect of the present disclosure, there is provided a device for determining a vehicle position, comprising:

[0009] an acquisition unit, configured to acquire the number of lanes of the auxiliary road and the number of lanes of the main road of the current road in the high-precision map when it is determined according to the vehicle position coordinates that the current road on which the vehicle is currently located has an auxiliary road;

[0010] The first determination unit is used to determine the final position of the vehicle according to the number of lanes of the auxiliary road and the number of lanes of the main road; wherein the final position of the vehicle represents whether the vehicle is outside the geographic fence.

[0011] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0012] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method described in the first aspect.

[0013] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method described in the first aspect.

[0014] According to a fifth aspect of the present disclosure, a computer program product is provided, comprising: a computer program, wherein the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program so that the electronic device executes the method described in the first aspect.

[0015] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure.

[0017] Figure 1 This is a scenario where the embodiments of the present disclosure can be implemented. Figure 1 ;

[0018] Figure 2 This is a scenario where the embodiments of the present disclosure can be implemented. Figure 2 ;

[0019] Figure 3 This is a scenario where the embodiments of the present disclosure can be implemented. Figure 3 ;

[0020] Figure 4 is a schematic diagram according to a first embodiment of the present disclosure;

[0021] Figure 5 is a schematic diagram according to a second embodiment of the present disclosure;

[0022] Figure 6 is a connection diagram of a control system provided by the present disclosure;

[0023] Figure 7 is a schematic diagram according to a third embodiment of the present disclosure;

[0024] Figure 8 is a schematic diagram according to a fourth embodiment of the present disclosure;

[0025] Fig. 9 is a schematic diagram according to a fifth embodiment of the present disclosure;

[0026] Fig.10 A schematic block diagram of an example electronic device 1000 is shown that can be used to implement embodiments of the present disclosure. DETAILED DESCRIPTION

[0027] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0028] An assisted driving system is deployed in the vehicle, and the control of the assisted driving system needs to be based on whether the vehicle is outside the geo-fence, that is, it needs to be determined whether it is outside the geo-fence. If the vehicle is inside the geo-fence, it is determined that the vehicle is on the main road; if the vehicle is outside the geo-fence, it is determined that the vehicle is on the secondary road.

[0029] The geographic fence may also be referred to as an electronic fence. The assisted driving system may be, for example, a high-speed assisted driving system.

[0030] Figure 1 This is a scenario where the embodiments of the present disclosure can be implemented. Figure 1 ,like Figure 1 As shown, in the parallel main and auxiliary road scenario, the road has a main road 101 and an auxiliary road 102, wherein the main road 101 and the auxiliary road 102 are parallel. If it is determined that the vehicle is within the geo-fence, it is determined that the vehicle is located on the main road 101; if it is determined that the vehicle is outside the geo-fence, it is determined that the vehicle is located on the auxiliary road 102.

[0031] Figure 2 This is a scenario where the embodiments of the present disclosure can be implemented. Figure 2 ,like Figure 2 As shown, in the viaduct scenario, the road has an viaduct 201 and an auxiliary road 202, wherein the auxiliary road 202 is located below the viaduct 201, and the auxiliary road 202 may also be referred to as an auxiliary road under the viaduct. If it is determined that the vehicle is within the geo-fence, it is determined that the vehicle is located on the viaduct 201; if it is determined that the vehicle is outside the geo-fence, it is determined that the vehicle is located on the auxiliary road 202 (i.e., the auxiliary road under the viaduct).

[0032] Figure 3 This is a scenario where the embodiments of the present disclosure can be implemented. Figure 3 ,like Figure 3 As shown, in the ramp scenario, the road has a main road 301 and a secondary road 302, wherein the secondary road 302 may also be referred to as a ramp. If it is determined that the vehicle is within the geo-fence, the vehicle is determined to be on the main road 301; if it is determined that the vehicle is outside the geo-fence, the vehicle is determined to be on the secondary road 302 (i.e., the ramp).

[0033] If the vehicle is actually outside the geofence (i.e., the vehicle is actually on the secondary road), but is mistakenly determined to be inside the geofence (i.e., the vehicle is determined to be on the main road), the assisted driving system will be activated, resulting in the unexpected activation of the assisted driving system.

[0034] If the vehicle is actually within the geofence (i.e., the vehicle is actually on the main road), but is mistakenly determined to be outside the geofence (i.e., the vehicle is determined to be on the secondary road), the assisted driving system will be shut down, resulting in an unexpected shutdown of the assisted driving system and an availability error of the assisted driving system.

[0035] Furthermore, if the assisted driving system is incorrectly controlled (for example, unexpectedly turned on or off), the assisted driving of the vehicle may be wrong, which may further affect the driving safety of the vehicle. Therefore, it is necessary to accurately determine whether the vehicle is outside the geographic fence.

[0036] The present disclosure provides a method, device and equipment for determining the position of a vehicle, which are applied to autonomous driving and intelligent transportation in computer technology to accurately determine whether the vehicle is outside a geographic fence.

[0037] In order to enable readers to more deeply understand the implementation principle of the present disclosure, the following Figure 5-Figure 10 right Figure 4 The illustrated embodiment is further refined.

[0038] Figure 4 is a schematic diagram according to the first embodiment of the present disclosure, such as Figure 4 As shown, the present disclosure provides a method for determining a vehicle position, the method comprising:

[0039] 401. When it is determined based on the vehicle position coordinates that there is an auxiliary road on the current road where the vehicle is currently located, the number of auxiliary road lanes and the number of main road lanes of the current road in the high-precision map are obtained.

[0040] For example, the execution subject of this embodiment may be a controller in a vehicle, or a processor in a vehicle, or a device or apparatus for determining the position of a vehicle, or other devices or apparatuses that can implement this embodiment, and there is no limitation to this.

[0041] First, the vehicle position coordinates need to be determined, wherein the vehicle position coordinates may adopt Global Positioning System (GPS) coordinate information.

[0042] The current road where the vehicle is currently located can be identified based on the vehicle position coordinates, and then whether there is an auxiliary road on the current road where the vehicle is currently located can be determined based on the high-precision map.

[0043] If the vehicle is identified as being in a parallel main and auxiliary road scenario based on the vehicle position coordinates, it is necessary to determine whether there is an auxiliary road on the current road where the vehicle is currently located.

[0044] If the vehicle is identified as being under an elevated bridge scene based on the vehicle position coordinates, it is necessary to determine whether there is an auxiliary road under the elevated bridge on the current road where the vehicle is currently located. In this case, the auxiliary road is the "auxiliary road under the elevated bridge".

[0045] If the vehicle is identified as being in a ramp scenario based on the vehicle position coordinates, it is necessary to determine whether there is a ramp on the current road where the vehicle is currently located. In this case, the auxiliary road is the "ramp".

[0046] Then, if it is determined that there is an auxiliary road on the current road where the vehicle is currently located, the number of auxiliary road lanes and the number of main road lanes of the current road need to be obtained from the high-precision map. When there is an auxiliary road on the road, the high-precision map includes the number of auxiliary road lanes and the number of main road lanes of the road; when there is no auxiliary road on the road, the high-precision map includes the number of main road lanes of the road.

[0047] 402. Determine a final position of the vehicle according to the number of lanes of the auxiliary road and the number of lanes of the main road; wherein the final position of the vehicle indicates whether the vehicle is outside the geographic fence.

[0048] Exemplarily, the final position of the vehicle is determined based on the number of auxiliary road lanes and the number of main road lanes of the current road, that is, it is determined whether the vehicle is outside the geographic fence.

[0049] If the vehicle is within the geographic fence, it is determined that the vehicle is located on the main road; if the vehicle is outside the geographic fence, it is determined that the vehicle is located on the secondary road.

[0050] In this embodiment, by obtaining the vehicle position coordinates, when it is determined that the current road where the vehicle is currently located has an auxiliary road according to the vehicle position coordinates, the number of auxiliary road lanes and the number of main road lanes of the current road are obtained from the high-precision map; then, based on the number of auxiliary road lanes and the number of main road lanes of the current road, it is determined whether the vehicle is outside the geographic fence, and then the final position of the vehicle is obtained. When it is determined that the current road where the vehicle is currently located has an auxiliary road, further verification is performed according to the number of auxiliary road lanes and the number of main road lanes of the current road in the high-precision map, and then it is determined whether the vehicle is outside the geographic fence; it can be accurately determined whether the vehicle is outside the geographic fence. In the process of determining the final position of the vehicle by a simple inspection method of the number of auxiliary road lanes and the number of main road lanes, there is no need to refer to new hardware and increase hardware costs; and through the above determination process, it can be known that the algorithm complexity of the verification process involved in step 505 is low and will not consume too many system resources of the vehicle.

[0051] Figure 5is a schematic diagram according to the second embodiment of the present disclosure, such as Figure 5 As shown, the present disclosure provides a method for determining a vehicle position, the method comprising:

[0052] 501. Obtain initial fence information of the vehicle; wherein the initial fence information indicates whether the initially identified vehicle is located outside the geographic fence.

[0053] In one example, step 501 includes the following process:

[0054] The first step of step 501 is to obtain vehicle information of the vehicle and object information of objects in the surrounding environment of the vehicle; wherein the vehicle information represents the vehicle position and vehicle posture.

[0055] The second step of step 501 is to determine the initial fence information based on the vehicle information, the object information and the road segment location information of the road segment in the high-precision map; wherein the road segment location information represents the location of the road segment on the road.

[0056] For example, the execution subject of this embodiment may be a controller in a vehicle, or a processor in a vehicle, or a device or apparatus for determining the position of a vehicle, or other devices or apparatuses that can implement this embodiment, and there is no limitation to this.

[0057] First, initial fence information of the vehicle is determined, wherein the initial fence information represents whether the initially identified vehicle is located outside the geographic fence.

[0058] Then, the vehicle position and the vehicle posture of the vehicle are obtained, wherein the vehicle position and the vehicle posture constitute vehicle information. Furthermore, object information of objects in the surrounding environment of the vehicle is obtained, wherein the object information represents the object position and the object posture.

[0059] In addition, the high-precision map stores the section location information of each road section.

[0060] Furthermore, based on the positioning module and the navigation module, the vehicle information, the object information and the section position information of the road section in the high-precision map are processed to determine the initial fence information.

[0061] Therefore, an initial fence information is initially determined based on the vehicle information, object information and the road section position information of the road section in the high-precision map.

[0062] In one example, the second step of step 501 includes:

[0063] Based on the vehicle information, object information and the road segment position information of the road segment in the high-precision map, the distance relationship information and the angle relationship information of the road segment are determined; wherein the distance relationship information represents the distance relationship between the vehicle and the object on the road segment, and the angle relationship information represents the angle relationship between the vehicle and the object on the road segment.

[0064] Determine matching information between a perception system of the vehicle and a positioning system of the vehicle; wherein the matching information represents a matching degree between the perception system and the positioning system.

[0065] Initial fence information is determined according to the distance relationship information of the road segments, the angle relationship information of the road segments, and the matching information.

[0066] In one example, “determining initial fence information according to distance relationship information of road segments, angle relationship information of road segments, and matching information” includes:

[0067] The weight information of the road segment is determined according to the distance relationship information of the road segment, the angle relationship information of the road segment, and the matching information; wherein the weight information represents the positional relationship between the vehicle and the road segment.

[0068] If it is determined that the value of the weight information of the road segment is greater than a preset threshold, the initial fence information is determined according to the value of the weight information of the road segment.

[0069] In one example, “determining weight information of a road segment according to distance relationship information of the road segment, angle relationship information of the road segment, and matching information” includes:

[0070] The distance weight of the road segment is determined according to the distance relationship information of the road segment and the first mapping relationship; the angle weight of the road segment is determined according to the angle relationship information of the road segment and the second mapping relationship; and the matching weight corresponding to the matching information is determined according to the matching information and the third mapping relationship; wherein the first mapping relationship is the corresponding relationship between the distance relationship information and the distance weight, the second mapping relationship is the corresponding relationship between the angle relationship information and the angle weight, and the third mapping relationship is the corresponding relationship between the matching information and the matching weight.

[0071] The distance weight of the road segment, the angle weight of the road segment, and the matching weight corresponding to the matching information are weighted and summed to obtain the weight information of the road segment.

[0072] For example, Figure 6 is a connection diagram of the control system provided by the present disclosure, such as Figure 6As shown, a control system 600 is deployed in the vehicle, and the control system 600 includes a global navigation satellite system (GNSS) module 601, a collection unit 602, a visual perception module 603, a positioning module 604, a navigation module 605, and an ODD (Operational Design Domain) verification module 606.

[0073] The acquisition unit 602 includes: a camera, a posture sensor, a laser radar, an ultrasonic sensor, etc. The posture sensor is, for example: a GNSS, an inertial measurement unit (IMU), a wheel speed sensor.

[0074] In the process of acquiring the initial fence information of the vehicle, the vehicle position of the vehicle is firstly acquired based on the GNSS module 601 in the vehicle.

[0075] Then, the vehicle posture of the vehicle is acquired based on the posture sensor in the vehicle, wherein the vehicle posture represents the posture information of the vehicle; wherein the vehicle position and the vehicle posture constitute the vehicle information.

[0076] Based on the camera, laser radar, or ultrasonic sensor in the vehicle, object information of objects in the surrounding environment of the vehicle is obtained, wherein the object information represents the object position and object posture.

[0077] In addition, the high-precision map stores the location information of each road segment.

[0078] Then, for each road segment in the high-precision map, the distance relationship information of each road segment is determined based on the vehicle position in the vehicle information and the object position of the object in the object information. For each road segment, there is at least one object on the road segment; for each road segment, the distance between the vehicle position and the object position of each object is calculated to obtain a distance value corresponding to each object, and the distance value represents the distance between the vehicle and the object; then, for each road segment, the distance values ​​of each object on the road segment are weighted averaged to obtain the value of the distance relationship information of the road segment. It can be seen that the distance relationship information represents the distance relationship between the vehicle and the object on the road segment.

[0079] For each road segment in the high-precision map, the angle relationship information of each road segment is determined based on the vehicle posture in the vehicle information and the object posture of the object in the object information. For each road segment, there is at least one object on the road segment; for each road segment, the distance between the vehicle posture and the object posture of each object is calculated to obtain the angle value corresponding to each object, and the angle value represents the angle between the vehicle and the object; then, for each road segment, the angle values ​​of each object on the road segment are weighted averaged to obtain the value of the angle relationship information of the road segment. It can be seen that the angle relationship information represents the angle relationship between the vehicle and the object on the road segment.

[0080] The vehicle has a perception system, and the perception information includes millimeter wave radar, camera, laser radar, etc. The vehicle has a positioning system, which is used to locate the vehicle; the positioning system reports the above-mentioned GPS, IMU, wheel speed sensor, etc. It is necessary to determine the degree of match between the perception system and the positioning system, and then obtain matching information; for example, the data collected by the perception system and the data collected by the positioning system can be matched and fused using a pre-trained artificial intelligence model to obtain matching information. It can be seen that the matching information represents the degree of match between the perception system and the positioning system.

[0081] Based on the positioning module 604, the initial fence information is determined by performing calculations according to the distance relationship information of each road segment, the angle relationship information of each road segment, and the matching information.

[0082] Exemplarily, “calculating according to the distance relationship information of each road segment, the angle relationship information of each road segment, and the matching information to determine the initial fence information” may be performed in the following manner.

[0083] The first method is to input the distance relationship information of each road segment, the angle relationship information of each road segment, and the matching information into a pre-trained artificial intelligence model for recognition, and output the initial fence information; wherein the initial fence information indicates whether the initially recognized vehicle is outside the geographic fence.

[0084] For example, the distance relationship information and the angle relationship information of each road segment are first input into a pre-trained artificial intelligence model for feature extraction to obtain a first feature, which indicates the above-mentioned distance relationship information and the above-mentioned angle relationship information of each road segment on the road; then, the matching information is input into the pre-trained artificial intelligence model, and the matching information is fused with the first feature to obtain a second feature, that is, the first feature is corrected using the matching information; the second feature is identified based on the pre-trained artificial intelligence model to obtain initial fence information.

[0085] The second method is to perform weighted calculation on the distance relationship information of each road segment, the angle relationship information of each road segment, and the matching information to obtain a weight value, and determine the initial fence information according to the value of the weight value.

[0086] For example, the distance relationship information of each road segment, the angle relationship information of each road segment, and the matching information are weighted together to obtain a weight value. Alternatively, the distance relationship information of each road segment and the angle relationship information of each road segment are weighted to obtain an initial weight value, and then the initial weight value is corrected according to the matching information (for example, the initial weight value is corrected according to the matching degree indicated by the matching information; wherein, if the value represented by the matching degree indicated by the matching information is low, the initial weight value is lowered; if the value represented by the matching degree indicated by the matching information is high, the initial weight value is increased) to obtain the corrected weight value. Then, according to the value of the weight value, the initial fence information is determined; for example, if the weight value is greater than a threshold value, it is determined that the initial fence information indicates that the initially identified vehicle is located within the geographic fence, and if the weight value is less than a threshold value, it is determined that the initial fence information indicates that the initially identified vehicle is located outside the geographic fence.

[0087] Since for each road segment, the distance relationship information of each road segment indicates the distance relationship between the vehicle and the object on the road segment, and the angle relationship information of each road segment indicates the angle relationship between the vehicle and the object on the road segment; then the road condition can be analyzed according to the distance relationship information of each road segment and the angle relationship information of each road segment, and then the detection of whether the road is a secondary road can be completed. Among them, matching information can also be added, and the matching information indicates the matching degree between the vehicle's perception system and the vehicle's positioning system; thus, based on the matching information, the distance relationship information of each road segment detected and calculated and the angle relationship information of each road segment can be calibrated to accurately complete the analysis of the road condition and accurately obtain the initial fence information.

[0088] The third method: A first mapping relationship, a second mapping relationship and a third mapping relationship are pre-stored, wherein the first mapping relationship is the correspondence relationship between distance relationship information and distance weight, the second mapping relationship is the correspondence relationship between angle relationship information and angle weight, and the third mapping relationship is the correspondence relationship between matching information and matching weight.

[0089] Therefore, the distance weight of each road segment can be determined according to the distance relationship information of each road segment and the first mapping relationship. The smaller the value of the distance relationship information, the closer the distance between the vehicle and the object is, and the larger the value of the distance weight is determined to be; the larger the value of the distance weight is, the greater the impact on the value of the weight information calculated for each road segment; that is, the closer the object is to the vehicle, the greater the impact on the weight information of the road segment, and the greater the impact on the initial fence information to be obtained.

[0090] According to the angle relationship information of each road segment, the angle weight of each road segment is determined according to the second mapping relationship. The larger the value of the angle relationship information, the larger the value of the distance weight is determined to be; the larger the value of the angle weight is, the greater the influence on the value of the weight information of each road segment calculated; that is, the greater the angle deviation between the object and the vehicle, the greater the influence on the weight information of the road segment, and the greater the influence on the initial fence information to be obtained.

[0091] According to the matching information, the matching weight w corresponding to the matching information is determined according to the third mapping relationship. vision (X).

[0092] Through the above method, we can get the rth j The distance weight w of each road segment d (X,r j ), get the rth j The angle weight w of the road segment θ (X,r j ), and get the matching weight w vision (X). j is a positive integer greater than or equal to 1. X represents the vehicle information corresponding to the vehicle used. For example, j The distance weight w of each road segment d (X,r j ), the vehicle information used is the vehicle position; get the rth j The angle weight w of the road segment θ (X,r j ), the vehicle information used is the vehicle posture; the matching weight w is obtained vision In the process of (X), the vehicle information used is the data collected by the perception system and the data collected by the positioning system.

[0093] Then, after weighted summing the distance weight of each road segment, the angle weight of each road segment, and the matching weight corresponding to the matching information, the weight information of each road segment is obtained based on the preset function. Among them, λ1, λ2, and λ3 are different pre-set weight parameters; s max is the judgment function.

[0094] Among them, in this solution, only the weight information of the current road segment that the vehicle is currently in can be calculated. Through the above mapping relationships, the distance weight of the current road segment, the angle weight of the current road segment, and the matching weight can be determined; then the determination function can be used to accurately obtain the value of the weight information of the road segment.

[0095] Furthermore, based on the positioning module 604, if it is determined that the value of the weight information of the current road segment where the vehicle is currently located is greater than a preset threshold, the initial fence information is determined according to the weight information of the current road segment where the vehicle is currently located. Wherein, if the weight information of the current road segment where the vehicle is currently located is a first value (for example, a value of 1), the initial fence information is determined to indicate that the initially identified vehicle is located within the geographic fence; if the weight information of the current road segment where the vehicle is currently located is a second value (for example, a value of -1), the initial fence information is determined to indicate that the initially identified vehicle is located outside the geographic fence.

[0096] By calculating and measuring the position of the vehicle and the position of the object on the current road segment where the vehicle is located, the distance relationship between the vehicle and the object on the current road segment where the vehicle is located is obtained; the vehicle posture and the object posture of the object on the current road segment where the vehicle is located are calculated and measured to obtain the angle relationship between the vehicle and the object on the current road segment where the vehicle is located; and it is necessary to determine the matching information between the vehicle's perception system and the vehicle's positioning system. Based on the above distance relationship, angle relationship and matching information, the initial recognition of the current road segment where the vehicle is located can be completed, and then it can be initially determined whether the current road segment where the vehicle is located is outside the geographic fence.

[0097] The initial fence information of the vehicle is obtained through step 501; wherein, the initial fence information indicates whether the initially identified vehicle is located outside the geographic fence. However, in the process of obtaining the initial fence information in step 501, the visual perception result of the vehicle's camera is required, but the visual perception result of the camera needs to use an artificial intelligence module, such as a CNN model. The artificial intelligence module may have problems of false detection and missed detection; and the output visual perception result will be affected by noise interference and environmental factors such as lighting changes, occlusion, and angle; which will result in the initial fence information obtained being not accurate enough, that is, it will not be able to accurately determine whether the vehicle is located outside the geographic fence at this time. In addition, the judgment function used needs to be adjusted according to the actual application scenario. Faced with a changing and complex traffic environment, the generalization performance of the function is limited; which further results in the initial fence information obtained being not accurate enough.

[0098] Furthermore, it is necessary to execute the following steps 502-505 to obtain the final position of the vehicle, and the final result of the vehicle represents whether the vehicle is outside the geographic fence.

[0099] 502. Obtain the vehicle position coordinates, and determine whether there is an auxiliary road on the current road where the vehicle is currently located according to the vehicle position coordinates.

[0100] In one example, step 502 includes: matching the vehicle position coordinates with the high-precision map to obtain the traffic scene in which the vehicle is currently located; wherein the traffic scene represents whether there is an auxiliary road on the current road where the vehicle is currently located.

[0101] Exemplarily, the vehicle position coordinates are obtained based on GPS technology, and the vehicle position coordinates are matched with a high-precision map to determine the traffic scenario in which the vehicle is currently located.

[0102] If the vehicle is identified as being in a parallel main and auxiliary road scenario based on the vehicle position coordinates, it is necessary to determine whether there is an auxiliary road on the current road where the vehicle is currently located.

[0103] If the vehicle is identified as being under an elevated bridge scene based on the vehicle position coordinates, it is necessary to determine whether there is an auxiliary road under the elevated bridge on the current road where the vehicle is currently located. In this case, the auxiliary road is the "auxiliary road under the elevated bridge".

[0104] If the vehicle is identified as being in a ramp scenario based on the vehicle position coordinates, it is necessary to determine whether there is a ramp on the current road where the vehicle is currently located. In this case, the auxiliary road is the "ramp".

[0105] By comparing the vehicle position coordinates collected by GPS technology with the high-precision map, it is possible to accurately determine whether there is an auxiliary road on the current road where the vehicle is currently located.

[0106] In one example, “matching the vehicle’s position coordinates with the high-precision map to obtain the current traffic scene of the vehicle” includes:

[0107] Determine the location area corresponding to the vehicle's location coordinates from the high-precision map.

[0108] If it is determined that there is a secondary road in the location area, then it is determined that the traffic scene represents that there is a secondary road on the current road where the vehicle is currently located.

[0109] If it is determined that there is no secondary road in the location area, it is determined that the traffic scene represents that there is no secondary road on the current road where the vehicle is currently located.

[0110] In one example, the location area is an area within a preset longitude and latitude range with the vehicle location coordinates as the center point; or, the location area is an area within a preset three-dimensional space range with the vehicle location coordinates as the center point.

[0111] Exemplarily, step 502 may be implemented in the following manner: The vehicle position coordinates are obtained based on GPS technology, and the vehicle position coordinates are matched with a high-precision map to determine a location area corresponding to the vehicle position coordinates from the high-precision map.

[0112] For example, with the vehicle position coordinates as the center point, within a preset latitude and longitude range, the location area is determined from the high-precision map. Then the above-mentioned parallel main and auxiliary road scene and the above-mentioned ramp scene can be determined. Or, with the vehicle position coordinates as the center point, within a preset three-dimensional space range, the location area is determined from the high-precision map. Then the above-mentioned viaduct scene can be determined.

[0113] Then, since the high-precision map records complete information on the road, it is possible to determine from the data in the obtained location area whether there is an auxiliary road in the location area. If there is an auxiliary road in the location area, it is determined that the traffic scene represents that there is an auxiliary road on the current road where the vehicle is currently located; if there is no auxiliary road in the location area, it is determined that there is no auxiliary road on the current road where the vehicle is currently located.

[0114] Therefore, since the location area corresponding to the vehicle position coordinates is matched from the high-precision map, matching can be performed at both two-dimensional and three-dimensional angles; and then the main and auxiliary road scenes, ramp scenes, and viaduct scenes can be used in parallel to complete the matching process to accurately determine whether there is an auxiliary road in the location area corresponding to the vehicle position coordinates.

[0115] 503. When it is determined based on the vehicle position coordinates that there is an auxiliary road on the current road where the vehicle is currently located, the number of auxiliary road lanes and the number of main road lanes of the current road in the high-precision map are obtained.

[0116] In one example, when the traffic scene currently located by the vehicle is an elevated bridge scene, the auxiliary road on the road currently located by the vehicle is the auxiliary road under the elevated bridge, and the number of lanes on the auxiliary road is the number of lanes on the lower auxiliary road.

[0117] When the traffic scene currently located by the vehicle is a parallel main and auxiliary road scene, the auxiliary road on the road currently located by the vehicle is an auxiliary road parallel to the main road, and the number of lanes of the auxiliary road is the number of lanes of the auxiliary road parallel to the main road.

[0118] When the traffic scene currently located by the vehicle is a ramp scene, the auxiliary road on the road currently located by the vehicle is a ramp, and the number of lanes on the auxiliary road is the number of ramps.

[0119] Exemplarily, after step 503, if it is determined based on the vehicle position coordinates that there is an auxiliary road on the current road where the vehicle is currently located, the number of auxiliary road lanes and the number of main road lanes of the current road are obtained from the high-precision map.

[0120] Among them, when it is determined that the current traffic scene of the vehicle is an viaduct scene, the main road is the viaduct, and the auxiliary road is the auxiliary road under the viaduct; the number of auxiliary road lanes that need to be obtained from the high-precision map is the number of lower auxiliary road lanes, and the number of main road lanes that need to be obtained from the high-precision map is the number of main road lanes on the viaduct.

[0121] When it is determined that the traffic scenario in which the vehicle is currently located is a parallel main and auxiliary road scenario, the auxiliary road is the auxiliary road parallel to the main road; then the number of auxiliary road lanes that need to be obtained from the high-precision map is the number of auxiliary road lanes parallel to the main road, and the number of main road lanes that need to be obtained from the high-precision map is the number of main road lanes.

[0122] When it is determined that the current traffic scene of the vehicle is a ramp scene, the main road is the main road and the auxiliary road is the ramp; the number of auxiliary road lanes that need to be obtained from the high-precision map is the number of ramps, and the number of main road lanes that need to be obtained from the high-precision map is the number of main road lanes.

[0123] Furthermore, for different complex traffic scenarios, the corresponding number of auxiliary lanes and main lanes of the current road can be obtained from the high-precision map.

[0124] 504. If the number of lanes on the auxiliary road and the number of lanes on the main road are determined and meet the preset conditions, then determining the final position of the vehicle indicates that the vehicle is outside the geographic fence.

[0125] In one example, the preset condition is that the number of lanes on the auxiliary road is greater than or equal to the number of lanes on the main road. Alternatively, the preset condition is that the total number of lanes is different from the number of lanes on the auxiliary road, and the total number of lanes is different from the number of lanes on the main road.

[0126] Exemplarily, the number of auxiliary road lanes and the number of main road lanes of the current road are determined to determine whether the number of auxiliary road lanes and the number of main road lanes meet preset conditions.

[0127] Among them, through the above Figure 6 The navigation module 605 in the outputs the number of lanes n_mr of the secondary road and the number of lanes n_mm of the main road, that is, Figure 6 The navigation module 605 sends the number of lanes n_mr of the auxiliary road of the current road and the number of lanes n_mm of the main road of the current road to the Figure 6 The ODD verification module 606 in.

[0128] For example, the value range of the number of lanes on the auxiliary road n_mr is [1,7]. The value range of the number of lanes on the main road n_mm is [1,7].

[0129] It is also necessary to identify the total number of lanes on the road where the vehicle is currently located. Among them, the road image can be obtained by an image acquisition unit (for example, a camera) on the vehicle; the road image is recognized based on an image recognition algorithm to obtain the road edge and lane lines, for example, the lane lines are a combination of white / yellow, solid / dotted, single / double, etc.; then, the road edge and lane lines are determined to obtain the total number of lanes. Alternatively, the road point cloud data is obtained by a radar on the vehicle; the road point cloud data is recognized based on a preset recognition algorithm to obtain the road edge and lane lines, for example, the lane lines are a combination of white / yellow, solid / dotted, single / double, etc.; then, the road edge and lane lines are determined to obtain the total number of lanes. Among them, the total number of lanes is identified by any one of a variety of methods such as an image acquisition unit or a radar; the total number of lanes obtained at this time is to determine whether the initial fence information obtained in step 501 is inaccurate.

[0130] Among them, through the above Figure 6 The visual perception module 603 in the outputs the total number of lanes n of the current road, that is, Figure 6 The visual perception module 603 in the current road sends the total number of lanes n to Figure 6 The ODD verification module 606 in.

[0131] Based on the ODD verification module 606, if it is determined that the number of auxiliary road lanes n_mr of the current road is the same as the number of main road lanes n_mm of the current road (that is, n_mr=n_mm), then the number of auxiliary road lanes n_mr of the current road and the number of main road lanes n_mm of the current road are determined to meet the preset conditions, and then the initial verification value z=-1 is output; then it is determined that the initial fence information obtained in step 501 is inaccurate (at this time, no matter what the result of the initial fence information obtained in step 501 is, it is determined that the initial fence information obtained in step 501 is inaccurate), and the final position of the vehicle is determined to be that the vehicle is outside the geographic fence.

[0132] Based on the ODD verification module 606, if it is determined that the number of auxiliary road lanes n_mr of the current road is greater than the number of main road lanes n_mm of the current road (i.e., n_mr>n_mm), then it is determined that the number of auxiliary road lanes n_mr of the current road and the number of main road lanes n_mm of the current road meet the preset conditions, and then the initial verification value z=-1 is output; then it is determined that the initial fence information obtained in step 501 is inaccurate (at this time, no matter what the result of the initial fence information obtained in step 501 is, it is determined that the initial fence information obtained in step 501 is inaccurate), and the final position of the vehicle is determined to be that the vehicle is outside the geographic fence.

[0133] Based on the ODD verification module 606, if it is determined that the total number of lanes n is different from the number of auxiliary road lanes n_mr of the current road, and it is determined that the total number of lanes n is different from the number of main road lanes n_mm of the current road (that is, (n≠n_mr)&(n≠n_mm)), then it is determined that the number of auxiliary road lanes n_mr of the current road and the number of main road lanes n_mm of the current road meet the preset conditions, and then the initial verification value z=-1 is output; then it is determined that the initial fence information obtained in step 501 is inaccurate (at this time, no matter what the result of the initial fence information obtained in step 501 is, it is determined that the initial fence information obtained in step 501 is inaccurate), and the final position of the vehicle is determined to be that the vehicle is outside the geographic fence.

[0134] Then, through the relationship between the number of auxiliary road lanes and the number of main road lanes, or through the relationship between the number of auxiliary road lanes, the number of main road lanes and the total number of lanes, it is assisted to determine whether the initial fence information obtained in step 501 is inaccurate; if it is determined that the initial fence information obtained in step 501 is inaccurate, the final position of the vehicle is determined to be that the vehicle is outside the geographic fence.

[0135] 505. If the number of lanes on the auxiliary road and the number of lanes on the main road are determined to not meet the preset conditions, the total number of lanes on the road where the vehicle is currently located is identified, and the final position of the vehicle is determined based on the total number of lanes.

[0136] In one example, “identifying the total number of lanes on the road where the vehicle is currently located” includes: acquiring a road image through an image acquisition unit on the vehicle, and identifying the road image to obtain the total number of lanes. Alternatively, acquiring road point cloud data through a radar on the vehicle, and identifying the road point cloud data to obtain the total number of lanes.

[0137] In one example, the step of “determining the final position of the vehicle according to the total number of lanes” includes:

[0138] The first step is to obtain the initial fence information of the vehicle; wherein the initial fence information indicates whether the initially identified vehicle is located outside the geographic fence.

[0139] In the second step, if it is determined that the initial fence information indicates that the vehicle is within the geo-fence, then determining that the final position of the vehicle indicates that the vehicle is within the geo-fence.

[0140] In the third step, if it is determined that the initial fence information indicates that the vehicle is outside the geo-fence, then determining that the final position of the vehicle indicates that the vehicle is outside the geo-fence.

[0141] For example, if the number of auxiliary road lanes and the number of main road lanes of the current road do not meet the preset conditions, it is necessary to assist in determining the final position of the vehicle based on the total number of lanes of the road on which the vehicle is currently located.

[0142] That is, if the number of lanes on the auxiliary road of the current road and the number of lanes on the main road of the current road are determined to be inconsistent with the following conditions: the number of lanes on the auxiliary road of the current road n_mr is the same as the number of lanes on the main road of the current road n_mm (i.e., n_mr=n_mm); the number of lanes on the auxiliary road of the current road n_mr is greater than the number of lanes on the main road of the current road n_mm (i.e., n_mr>n_mm); the total number of lanes n is different from the number of lanes on the auxiliary road of the current road n_mr, and the total number of lanes n is different from the number of lanes on the main road of the current road n_mm (i.e., (n≠n_mr)&(n≠n_mm)). Thus, the initial check value z=1 is output, and the final position of the vehicle needs to be determined based on the total number of lanes on the road where the vehicle is currently located.

[0143] In step 505 , the initial fence information of the vehicle and the total number n of lanes of the road where the vehicle is currently located have been obtained.

[0144] Step 505 has determined that the initial check value z=1. If it is determined that the initial fence information indicates that the vehicle is within the geographic fence (i.e., (s max =1), then the final check value Odd_result=1 is output, and the final position of the vehicle is determined to be the vehicle being within the geo-fence. At this time, it is determined that the initial fence information is correct, and because the initial fence information indicates that the vehicle is within the geo-fence, the final position of the vehicle is determined to be the vehicle being within the geo-fence.

[0145] Step 505 has determined that the initial check value z=1. If it is determined that the initial fence information indicates that the vehicle is outside the geo-fence (i.e., (s max =-1), then the final check value Odd_result=-1 is output, and the final position of the vehicle is determined to be the vehicle outside the geographic fence; at this time, it is further determined that the initial fence information obtained in step 501 is inaccurate (at this time, no matter what the result of the initial fence information obtained in step 501 is, it is determined that the initial fence information obtained in step 501 is inaccurate), and the final position of the vehicle is determined to be the vehicle outside the geographic fence.

[0146] Thus, in step 505, if it is determined that the number of auxiliary road lanes of the current road and the number of main road lanes of the current road do not meet the preset conditions, the initial verification value z=1 is output, and the initial fence information determines the final position of the vehicle. The final position of the vehicle can be determined by a simple test of the number of auxiliary road lanes, the number of main road lanes, and the total number of lanes. In the process of obtaining the data of the number of auxiliary road lanes, the number of main road lanes, and the total number of lanes, no new hardware results are referenced, and they are only determined and stored based on the original hardware results in the vehicle. Therefore, in the process of determining the final position of the vehicle by a simple test of the number of auxiliary road lanes, the number of main road lanes, and the total number of lanes, there is no need to reference new hardware and increase hardware costs; and through the above determination process, it can be seen that the algorithm complexity of the verification process involved in step 505 is low, and it will not consume too many system resources of the vehicle, and the resource consumption of the system can be ignored.

[0147] The final position of the vehicle can be determined by a simple inspection of the number of lanes on the auxiliary road, the number of lanes on the main road, and the total number of lanes; and then it can be accurately determined whether the vehicle is outside the geographic fence. The data used in the above steps 502-505 are pre-stored data of the vehicle itself; for example, the number of lanes on the auxiliary road and the number of lanes on the main road are data in the high-precision map, and the number of lanes on the auxiliary road and the number of lanes on the main road are pre-stored in the positioning module; the total number of lanes is data determined based on the camera or radar, and the total number of lanes is pre-stored in the notification visual perception module. The data used in the above steps 502-505, in the process of obtaining these data, do not refer to new hardware results, but only rely on the original hardware results in the vehicle to determine and store them. Therefore, in the process of determining the final position of the vehicle by a simple inspection of the number of lanes on the auxiliary road, the number of lanes on the main road, and the total number of lanes, there is no need to refer to new hardware and increase hardware costs; and through the above determination process, it can be seen that the algorithm complexity of the verification process involved in steps 502-505 is low, and it will not consume too much system resources of the vehicle, and the resource consumption of the system can be ignored. Moreover, the initial fence information obtained in step 501 is affected by the visual perception results of the camera in the vehicle, and the initial fence information obtained in step 501 may be inaccurate; then, the initial fence information is verified through steps 502-505 to determine whether to adopt the initial fence information; the verification process of steps 502-505 will not be affected by the visual perception results of the camera in the vehicle, nor will it be affected by the noise interference of other data in the positioning module in the vehicle.

[0148] 506. In response to the vehicle's final position indicating that the vehicle is outside the geo-fence, turning on the assisted driving system in the vehicle; or, in response to the vehicle's final position indicating that the vehicle is within the geo-fence, turning off the assisted driving system in the vehicle.

[0149] Exemplarily, through the above steps 502-505, the final position of the vehicle is obtained. If the final position of the vehicle indicates that the vehicle is outside the geographic fence, it is determined that the vehicle is located on the auxiliary road, and the auxiliary driving system in the vehicle needs to be turned on so that the vehicle can travel based on the auxiliary driving system.

[0150] If the final position of the vehicle indicates that the vehicle is within the geo-fence, it is determined that the vehicle is on the main road, and the assisted driving system in the vehicle needs to be turned off.

[0151] Through the above steps 502-505, the initial fence information is verified using the number of lanes on the auxiliary road of the current road, the number of lanes on the main road of the current road, and the total number of lanes on the current road to obtain the final position of the vehicle; the final position of the vehicle represents whether the vehicle is outside the geographic fence. Through the above verification process, whether the vehicle is outside the geographic fence is further verified to avoid errors in the initial fence information obtained, so as to obtain the final final position of the vehicle. When it is determined that the vehicle is outside the geographic fence and the vehicle is located on the auxiliary road, the auxiliary driving system in the vehicle needs to be turned on; when it is determined that the vehicle is inside the geographic fence and the vehicle is located on the main road, the auxiliary driving system in the vehicle needs to be turned off. Therefore, for different complex traffic scenarios (for example, viaduct scenarios, parallel main and auxiliary road scenarios, ramp scenarios, etc.), the verification of the geographic fence avoids the erroneous activation of the auxiliary driving system or the erroneous shutdown of the auxiliary driving system; thereby avoiding the unexpected activation or unexpected shutdown of the auxiliary driving system, improving the safety of the auxiliary driving system, and avoiding vehicle driving safety issues.

[0152] Figure 7 is a schematic diagram according to the third embodiment of the present disclosure, Figure 7 As shown, the present disclosure provides a vehicle position determination device 700, comprising:

[0153] The acquisition unit 701 is used to acquire the number of auxiliary road lanes and the number of main road lanes of the current road in the high-precision map when it is determined that there is an auxiliary road on the current road where the vehicle is currently located according to the vehicle position coordinates.

[0154] The first determination unit 702 is used to determine the final position of the vehicle according to the number of lanes on the auxiliary road and the number of lanes on the main road; wherein the final position of the vehicle indicates whether the vehicle is outside the geographic fence.

[0155] The device of this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same and will not be repeated here.

[0156] Figure 8 is a schematic diagram according to a fourth embodiment of the present disclosure, Figure 8 As shown, the present disclosure provides a vehicle position determination device 800, comprising:

[0157] The acquisition unit 801 is used to acquire the number of auxiliary road lanes and the number of main road lanes of the current road in the high-precision map when it is determined that there is an auxiliary road on the current road where the vehicle is currently located according to the vehicle position coordinates.

[0158] The first determination unit 802 is used to determine the final position of the vehicle according to the number of lanes on the auxiliary road and the number of lanes on the main road; wherein the final position of the vehicle indicates whether the vehicle is outside the geographic fence.

[0159] In one example, the first determining unit 802 includes:

[0160] The first determining subunit 8021 is configured to determine that the final position of the vehicle indicates that the vehicle is outside the geographic fence if the number of lanes on the auxiliary road and the number of lanes on the main road meet a preset condition.

[0161] The second determination subunit 8022 is used to identify the total number of lanes of the road where the vehicle is currently located, and determine the final position of the vehicle according to the total number of lanes if the number of lanes of the auxiliary road and the number of lanes of the main road do not meet the preset conditions.

[0162] In one example, the preset condition is that the number of lanes on the auxiliary road is greater than or equal to the number of lanes on the main road; or, the preset condition is that the total number of lanes is different from the number of lanes on the auxiliary road, and the total number of lanes is different from the number of lanes on the main road.

[0163] In one example, when identifying the total number of lanes on the road where the vehicle is currently located, the second determination subunit 8022 is specifically used to: acquire a road image through an image acquisition unit on the vehicle, and identify the road image to obtain the total number of lanes; or, acquire road point cloud data through a radar on the vehicle, and identify the road point cloud data to obtain the total number of lanes.

[0164] In one example, when the second determining subunit 8022 determines the final position of the vehicle according to the total number of lanes, the method includes:

[0165] The acquisition module 80221 is used to obtain the initial fence information of the vehicle; wherein the initial fence information represents whether the initially identified vehicle is located outside the geographic fence.

[0166] The first determination module 80222 is configured to determine that the final position of the vehicle indicates that the vehicle is within the geo-fence if it is determined that the initial fence information indicates that the vehicle is within the geo-fence.

[0167] The second determination module 80223 is configured to determine that the final position of the vehicle indicates that the vehicle is outside the geo-fence if it is determined that the initial fence information indicates that the vehicle is outside the geo-fence.

[0168] In one example, the acquisition module 80221 includes:

[0169] The acquisition submodule 802211 is used to acquire vehicle information of the vehicle and object information of objects in the surrounding environment of the vehicle; wherein the vehicle information represents the vehicle position and vehicle posture.

[0170] The determination submodule 802212 is used to determine the initial fence information according to the vehicle information, the object information and the road segment position information of the road segment in the high-precision map; wherein the road segment position information represents the position of the road segment on the road.

[0171] In one example, the submodule 802212 is determined to be specifically used for:

[0172] Based on the vehicle information, object information and the road segment position information of the road segment in the high-precision map, the distance relationship information and the angle relationship information of the road segment are determined; wherein the distance relationship information represents the distance relationship between the vehicle and the object on the road segment, and the angle relationship information represents the angle relationship between the vehicle and the object on the road segment; the matching information between the vehicle's perception system and the vehicle's positioning system is determined; wherein the matching information represents the degree of matching between the perception system and the positioning system.

[0173] Initial fence information is determined according to the distance relationship information of the road segments, the angle relationship information of the road segments, and the matching information.

[0174] In one example, when determining the initial fence information according to the distance relationship information of the road segment, the angle relationship information of the road segment, and the matching information, the determination submodule 802212 is specifically used to: determine the weight information of the road segment according to the distance relationship information of the road segment, the angle relationship information of the road segment, and the matching information; wherein the weight information represents the positional relationship between the vehicle and the road segment. If the value of the weight information of the road segment is determined to be greater than a preset threshold, the initial fence information is determined according to the value of the weight information of the road segment.

[0175] In one example, when determining the weight information of the road segment according to the distance relationship information of the road segment, the angle relationship information of the road segment, and the matching information, the determination submodule 802212 is specifically used to:

[0176] According to the distance relationship information of the road segment and the first mapping relationship, the distance weight of the road segment is determined; according to the angle relationship information of the road segment and the second mapping relationship, the angle weight of the road segment is determined; and according to the matching information and the third mapping relationship, the matching weight corresponding to the matching information is determined; wherein the first mapping relationship is the corresponding relationship between the distance relationship information and the distance weight, the second mapping relationship is the corresponding relationship between the angle relationship information and the angle weight, and the third mapping relationship is the corresponding relationship between the matching information and the matching weight; the distance weight of the road segment, the angle weight of the road segment and the matching weight corresponding to the matching information are weighted and summed to obtain the weight information of the road segment.

[0177] In one example, the device provided by this embodiment further includes:

[0178] The second determination unit 803 is used to match the vehicle position coordinates with the high-precision map to obtain the traffic scene where the vehicle is currently located; wherein the traffic scene represents whether there is an auxiliary road on the current road where the vehicle is currently located.

[0179] In one example, the second determining unit 803 is specifically configured to:

[0180] A location area corresponding to the vehicle's location coordinates is determined from the high-precision map; if it is determined that there is an auxiliary road in the location area, then it is determined that the traffic scene represents that there is an auxiliary road on the current road where the vehicle is currently located; if it is determined that there is no auxiliary road in the location area, then it is determined that the traffic scene represents that there is no auxiliary road on the current road where the vehicle is currently located.

[0181] In one example, the location area is an area within a preset longitude and latitude range with the vehicle location coordinates as the center point; or, the location area is an area within a preset three-dimensional space range with the vehicle location coordinates as the center point.

[0182] In one example, when the traffic scene currently in which the vehicle is located is an overpass scene, the auxiliary road on the road currently in which the vehicle is located is the auxiliary road under the overpass, and the number of auxiliary road lanes is the number of lanes of the lower auxiliary road; when the traffic scene currently in which the vehicle is located is a parallel main and auxiliary road scene, the auxiliary road on the road currently in which the vehicle is located is an auxiliary road parallel to the main road, and the number of auxiliary road lanes is the number of lanes of the auxiliary road parallel to the main road; when the traffic scene currently in which the vehicle is located is a ramp scene, the auxiliary road on the road currently in which the vehicle is located is a ramp, and the number of auxiliary road lanes is the number of ramps.

[0183] In one example, the device provided by this embodiment further includes:

[0184] The activation unit 804 is configured to activate the auxiliary driving system in the vehicle in response to the final position of the vehicle indicating that the vehicle is outside the geographic fence.

[0185] Alternatively, the shut-down unit 805 is configured to shut down the auxiliary driving system in the vehicle in response to the final position of the vehicle indicating that the vehicle is within a geographic fence.

[0186] The device of this embodiment can execute the technical solution in the above method. Its specific implementation process and technical principles are the same and will not be repeated here.

[0187] Fig. 9 is a schematic diagram according to a fifth embodiment of the present disclosure, Fig. 9 As shown, the electronic device 900 in this embodiment may include: a processor 901 and a memory 902 .

[0188] The memory 902 is used to store programs; the memory 902 may include volatile memory (English: volatile memory), such as random-access memory (English: random-access memory, abbreviated: RAM), such as static random-access memory (English: static random-access memory, abbreviated: SRAM), double data rate synchronous dynamic random access memory (English: Double Data Rate Synchronous Dynamic Random Access Memory, abbreviated: DDR SDRAM), etc.; the memory may also include non-volatile memory (English: non-volatile memory), such as flash memory (English: flash memory). The memory 902 is used to store computer programs (such as applications, functional modules, etc. that implement the above method), computer instructions, etc., and the above computer programs, computer instructions, etc. can be partitioned and stored in one or more memories 902. And the above computer programs, computer instructions, data, etc. can be called by the processor 901.

[0189] The above-mentioned computer programs, computer instructions, etc. may be stored in one or more memories 902 in partitions. And the above-mentioned computer programs, computer instructions, etc. may be called by the processor 901.

[0190] The processor 901 is used to execute the computer program stored in the memory 902 to implement each step of the method involved in the above embodiment.

[0191] For details, please refer to the relevant description in the previous method embodiment.

[0192] The processor 901 and the memory 902 may be independent structures or integrated structures. When the processor 901 and the memory 902 are independent structures, the memory 902 and the processor 901 may be coupled and connected via a bus 903 .

[0193] The electronic device of this embodiment can execute the technical solution in the above method, and its specific implementation process and technical principle are the same, which will not be repeated here.

[0194] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.

[0195] According to an embodiment of the present disclosure, the present disclosure also provides a computer program product, which includes: a computer program, the computer program is stored in a readable storage medium, at least one processor of an electronic device can read the computer program from the readable storage medium, and at least one processor executes the computer program so that the electronic device executes the solution provided by any of the above embodiments.

[0196] Fig.10 A schematic block diagram of an example electronic device 1000 that can be used to implement an embodiment of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein.

[0197] like Fig.10 As shown, the device 1000 includes a computing unit 1001, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1002 or a computer program loaded from a storage unit 1008 into a random access memory (RAM) 1003. In the RAM 1003, various programs and data required for the operation of the device 1000 can also be stored. The computing unit 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0198] A number of components in the device 1000 are connected to the I / O interface 1005, including: an input unit 1006, such as a keyboard, a mouse, etc.; an output unit 1007, such as various types of displays, speakers, etc.; a storage unit 1008, such as a disk, an optical disk, etc.; and a communication unit 1009, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 1009 allows the device 1000 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0199] The computing unit 1001 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1001 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1001 performs the various methods and processes described above, such as a method for determining the position of a vehicle. For example, in some embodiments, the determination of the position of a vehicle may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1008. In some embodiments, part or all of the computer program may be loaded and / or installed on the device 1000 via the ROM 1002 and / or the communication unit 1009. When the computer program is loaded into the RAM 1003 and executed by the computing unit 1001, one or more steps of the determination of the position of the vehicle described above may be performed. Alternatively, in other embodiments, the computing unit 1001 may be configured to execute the determination of the vehicle position in any other appropriate manner (eg, by means of firmware).

[0200] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0201] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0202] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0203] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0204] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0205] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services ("Virtual Private Server", or "VPS" for short). The server may also be a server of a distributed system, or a server combined with a blockchain.

[0206] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0207] The above specific implementations do not constitute a limitation on the protection scope of the present disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present disclosure shall be included in the protection scope of the present disclosure.

Claims

1. A method for determining a vehicle position, comprising: When it is determined according to the vehicle position coordinates that the current road on which the vehicle is currently located has an auxiliary road, the number of auxiliary road lanes and the number of main road lanes of the current road in the high-precision map are obtained; The final position of the vehicle is determined according to the number of lanes on the auxiliary road and the number of lanes on the main road; wherein the final position of the vehicle indicates whether the vehicle is outside the geographic fence.

2. The method according to claim 1, wherein: Determining a final position of the vehicle according to the number of lanes on the auxiliary road and the number of lanes on the main road includes: If the number of lanes on the auxiliary road and the number of lanes on the main road are determined to meet a preset condition, determining that the final position of the vehicle indicates that the vehicle is outside the geographic fence; If the number of lanes of the auxiliary road and the number of lanes of the main road are determined to not meet the preset conditions, the total number of lanes of the road where the vehicle is currently located is identified, and the final position of the vehicle is determined based on the total number of lanes.

3. The method according to claim 2, wherein: The preset condition is that the number of lanes on the auxiliary road is greater than or equal to the number of lanes on the main road; Alternatively, the preset condition is that the total number of lanes is different from the number of lanes on the auxiliary road, and the total number of lanes is different from the number of lanes on the main road.

4. The method according to claim 2 or 3, wherein: Identify the total number of lanes on the road the vehicle is currently on, including: Acquiring a road image through an image acquisition unit on the vehicle, and recognizing the road image to obtain the total number of lanes; Alternatively, the road point cloud data is acquired by a radar on the vehicle, and the road point cloud data is identified to obtain the total number of lanes.

5. The method according to any one of claims 2 to 4, wherein: Determining the final position of the vehicle according to the total number of lanes includes: Obtaining initial fence information of the vehicle; wherein the initial fence information indicates whether the initially identified vehicle is located outside the geographic fence; If it is determined that the initial fence information indicates that the vehicle is within the geo-fence, determining that the final position of the vehicle indicates that the vehicle is within the geo-fence; If it is determined that the initial fence information indicates that the vehicle is outside the geo-fence, then it is determined that the final position of the vehicle indicates that the vehicle is outside the geo-fence.

6. The method according to claim 5, wherein: Get the initial fence information of the vehicle, including: Acquire vehicle information of the vehicle, and acquire object information of objects in the surrounding environment of the vehicle; wherein the vehicle information represents the vehicle position and vehicle posture; The initial fence information is determined according to the vehicle information, the object information and the road segment position information of the road segment in the high-precision map; wherein the road segment position information represents the position of the road segment on the road.

7. The method according to claim 6, wherein: Determining the initial fence information according to the vehicle information, the object information, and the road segment position information of the road segment in the high-precision map includes: Determine distance relationship information of the road segment and angle relationship information of the road segment according to the vehicle information, the object information and the road segment position information of the road segment in the high-precision map; wherein the distance relationship information represents the distance relationship between the vehicle and the object on the road segment, and the angle relationship information represents the angle relationship between the vehicle and the object on the road segment; Determining matching information between the perception system of the vehicle and the positioning system of the vehicle; wherein the matching information represents the matching degree between the perception system and the positioning system; The initial fence information is determined according to the distance relationship information of the road segments, the angle relationship information of the road segments, and the matching information.

8. The method according to claim 7, wherein: Determining the initial fence information according to the distance relationship information of the road segments, the angle relationship information of the road segments, and the matching information includes: Determining weight information of the road segment according to the distance relationship information of the road segment, the angle relationship information of the road segment, and the matching information; wherein the weight information represents the positional relationship between the vehicle and the road segment; If it is determined that the value of the weight information of the road segment is greater than a preset threshold, the initial fence information is determined according to the value of the weight information of the road segment.

9. The method according to claim 8, wherein: Determining weight information of the road segment according to the distance relationship information of the road segment, the angle relationship information of the road segment, and the matching information includes: Determine the distance weight of the road segment according to the distance relationship information of the road segment and the first mapping relationship; determine the angle weight of the road segment according to the angle relationship information of the road segment and the second mapping relationship; and determine the matching weight corresponding to the matching information according to the matching information and the third mapping relationship; wherein the first mapping relationship is the corresponding relationship between the distance relationship information and the distance weight, the second mapping relationship is the corresponding relationship between the angle relationship information and the angle weight, and the third mapping relationship is the corresponding relationship between the matching information and the matching weight; The distance weight of the road segment, the angle weight of the road segment, and the matching weight corresponding to the matching information are weighted and summed to obtain the weight information of the road segment.

10. The method according to any one of claims 1 to 8, wherein: Determining whether the current road where the vehicle is currently located has an auxiliary road according to the vehicle position coordinates includes: The vehicle position coordinates are matched with the high-precision map to obtain the traffic scene in which the vehicle is currently located; wherein the traffic scene represents whether there is an auxiliary road on the current road where the vehicle is currently located.

11. The method according to claim 9, wherein: Matching the vehicle position coordinates with the high-precision map to obtain the current traffic scene of the vehicle, including: Determining a location area corresponding to the vehicle location coordinates from the high-precision map; If it is determined that there is an auxiliary road in the location area, determining that the traffic scene represents that there is an auxiliary road on the current road where the vehicle is currently located; If it is determined that there is no secondary road in the location area, it is determined that the traffic scene represents that there is no secondary road on the current road where the vehicle is currently located.

12. The method according to claim 11, wherein: The location area is an area within a preset longitude and latitude range with the vehicle location coordinates as the center point; or, the location area is an area within a preset three-dimensional space range with the vehicle location coordinates as the center point.

13. The method according to any one of claims 1 to 11, wherein: When the traffic scene currently located by the vehicle is an elevated bridge scene, the auxiliary road on the road currently located by the vehicle is an auxiliary road under the elevated bridge, and the number of lanes of the auxiliary road is the number of lanes of the lower auxiliary road; When the traffic scene currently located by the vehicle is a parallel main and auxiliary road scene, the auxiliary road on the road currently located by the vehicle is an auxiliary road parallel to the main road, and the number of lanes of the auxiliary road is the number of lanes of the auxiliary road parallel to the main road; When the traffic scene currently located by the vehicle is a ramp scene, the auxiliary road on the road currently located by the vehicle is a ramp, and the number of lanes on the auxiliary road is the number of ramps.

14. The method according to any one of claims 1 to 12, further comprising: In response to the final position of the vehicle indicating that the vehicle is outside the geo-fence, activating an auxiliary driving system in the vehicle; Alternatively, in response to the final position of the vehicle indicating that the vehicle is within a geo-fence, the auxiliary driving system in the vehicle is turned off.

15. A device for determining a vehicle position, comprising: an acquisition unit, configured to acquire the number of lanes of the auxiliary road and the number of lanes of the main road of the current road in the high-precision map when it is determined according to the vehicle position coordinates that the current road on which the vehicle is currently located has an auxiliary road; The first determination unit is used to determine the final position of the vehicle according to the number of lanes of the auxiliary road and the number of lanes of the main road; wherein the final position of the vehicle represents whether the vehicle is outside the geographic fence.

16. The device according to claim 15, wherein: The first determining unit includes: A first determining subunit is configured to determine that the final position of the vehicle represents that the vehicle is outside the geographic fence if the number of lanes of the auxiliary road and the number of lanes of the main road meet a preset condition; The second determination subunit is used to identify the total number of lanes of the road where the vehicle is currently located, and determine the final position of the vehicle according to the total number of lanes if the number of lanes of the auxiliary road and the number of lanes of the main road do not meet the preset conditions.

17. The device according to claim 16, wherein: The preset condition is that the number of lanes on the auxiliary road is greater than or equal to the number of lanes on the main road; Alternatively, the preset condition is that the total number of lanes is different from the number of lanes on the auxiliary road, and the total number of lanes is different from the number of lanes on the main road.

18. The device according to claim 16 or 17, wherein: When identifying the total number of lanes of the road on which the vehicle is currently located, the second determination subunit is specifically used to: Acquiring a road image through an image acquisition unit on the vehicle, and recognizing the road image to obtain the total number of lanes; Alternatively, the road point cloud data is acquired by a radar on the vehicle, and the road point cloud data is identified to obtain the total number of lanes.

19. The device according to any one of claims 16 to 18, wherein: When the second determining subunit determines the final position of the vehicle according to the total number of lanes, the second determining subunit includes: An acquisition module, used to acquire initial fence information of the vehicle; wherein the initial fence information indicates whether the initially identified vehicle is located outside the geographic fence; A first determination module, configured to determine that the final position of the vehicle indicates that the vehicle is within the geo-fence if it is determined that the initial fence information indicates that the vehicle is within the geo-fence; The second determination module is configured to determine that the final position of the vehicle indicates that the vehicle is outside the geo-fence if it is determined that the initial fence information indicates that the vehicle is outside the geo-fence.

20. The device according to claim 19, wherein The acquisition module comprises: An acquisition submodule, used to acquire vehicle information of the vehicle and object information of objects in the surrounding environment of the vehicle; wherein the vehicle information represents the vehicle position and vehicle posture; A determination submodule is used to determine the initial fence information based on the vehicle information, the object information and the road segment position information of the road segment in the high-precision map; wherein the road segment position information represents the position of the road segment on the road.

21. The device according to claim 20, wherein: The determination submodule is specifically used for: Determine distance relationship information of the road segment and angle relationship information of the road segment according to the vehicle information, the object information and the road segment position information of the road segment in the high-precision map; wherein the distance relationship information represents the distance relationship between the vehicle and the object on the road segment, and the angle relationship information represents the angle relationship between the vehicle and the object on the road segment; Determining matching information between the perception system of the vehicle and the positioning system of the vehicle; wherein the matching information represents the matching degree between the perception system and the positioning system; The initial fence information is determined according to the distance relationship information of the road segments, the angle relationship information of the road segments, and the matching information.

22. The device according to claim 21, wherein When the determination submodule determines the initial fence information according to the distance relationship information of the road segments, the angle relationship information of the road segments, and the matching information, it is specifically used to: Determining weight information of the road segment according to the distance relationship information of the road segment, the angle relationship information of the road segment, and the matching information; wherein the weight information represents the positional relationship between the vehicle and the road segment; If it is determined that the value of the weight information of the road segment is greater than a preset threshold, the initial fence information is determined according to the value of the weight information of the road segment.

23. The device according to claim 22, wherein: When the determination submodule determines the weight information of the road segment according to the distance relationship information of the road segment, the angle relationship information of the road segment, and the matching information, it is specifically used to: Determine the distance weight of the road segment according to the distance relationship information of the road segment and the first mapping relationship; determine the angle weight of the road segment according to the angle relationship information of the road segment and the second mapping relationship; and determine the matching weight corresponding to the matching information according to the matching information and the third mapping relationship; wherein the first mapping relationship is the corresponding relationship between the distance relationship information and the distance weight, the second mapping relationship is the corresponding relationship between the angle relationship information and the angle weight, and the third mapping relationship is the corresponding relationship between the matching information and the matching weight; The distance weight of the road segment, the angle weight of the road segment, and the matching weight corresponding to the matching information are weighted and summed to obtain the weight information of the road segment.

24. The device according to any one of claims 15 to 23, wherein: The device further comprises: The second determination unit is used to match the vehicle position coordinates with the high-precision map to obtain a traffic scene in which the vehicle is currently located; wherein the traffic scene represents whether there is an auxiliary road on the current road where the vehicle is currently located.

25. The device according to claim 24, wherein: The second determining unit is specifically configured to: Determining a location area corresponding to the vehicle location coordinates from the high-precision map; If it is determined that there is an auxiliary road in the location area, determining that the traffic scene represents that there is an auxiliary road on the current road where the vehicle is currently located; If it is determined that there is no secondary road in the location area, it is determined that the traffic scene represents that there is no secondary road on the current road where the vehicle is currently located.

26. The device according to claim 25, wherein The location area is an area within a preset longitude and latitude range with the vehicle location coordinates as the center point; or, the location area is an area within a preset three-dimensional space range with the vehicle location coordinates as the center point.

27. The device according to any one of claims 15 to 26, wherein: When the traffic scene currently located by the vehicle is an elevated bridge scene, the auxiliary road on the road currently located by the vehicle is an auxiliary road under the elevated bridge, and the number of lanes of the auxiliary road is the number of lanes of the lower auxiliary road; When the traffic scene currently located by the vehicle is a parallel main and auxiliary road scene, the auxiliary road on the road currently located by the vehicle is an auxiliary road parallel to the main road, and the number of lanes of the auxiliary road is the number of lanes of the auxiliary road parallel to the main road; When the traffic scene currently located by the vehicle is a ramp scene, the auxiliary road on the road currently located by the vehicle is a ramp, and the number of lanes on the auxiliary road is the number of ramps.

28. The device according to any one of claims 15 to 27, further comprising: an activation unit, configured to activate an auxiliary driving system in the vehicle in response to the final position of the vehicle indicating that the vehicle is outside the geo-fence; Alternatively, a shut-down unit is configured to shut down an auxiliary driving system in the vehicle in response to the final position of the vehicle indicating that the vehicle is within a geographic fence.

29. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 14.

30. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1-14.

31. A computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1 to 14.

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