Automatic driving area identification method and device, storage medium and vehicle equipment

Abstract

The invention relates to an automatic driving area identification method and device, a storage medium and vehicle equipment. The method comprises the steps that first road condition parameters are obtained, and the first road condition parameters are road condition parameters collected in the process that a vehicle runs along a to-be-recognized route in a to-be-recognized area in a manual driving state; whether the first road condition parameter meets a preset first automatic driving road condition or not is judged, and if the first automatic driving road condition is met, it is determined that the to-be-recognized route is an automatic driving route; under the condition that it is determined that the to-be-recognized route is the route capable of being automatically driven, second road condition parameters are obtained, and the second road condition parameters are road condition parameters collected in the process that the vehicle runs along the route capable of being automatically driven in the automatic driving state; and judging whether the second road condition parameter meets a preset second automatic driving road condition, and if the second road condition parameter meets the second automatic driving road condition, determining that the to-be-identified area is an automatic driving area. According to the invention, the accuracy of automatic driving area identification is improved.

Description

Technical Field

[0001] The present application relates to the field of autonomous driving technology, and in particular to a method, device, storage medium and vehicle equipment for identifying an area capable of autonomous driving. Background Art

[0002] With the continuous development of autonomous driving technology, the application scope of autonomous driving systems has become wider. However, the current application of autonomous driving technology still faces many challenges, one of which is how to accurately and effectively identify and determine the areas where autonomous driving can be safely implemented.

[0003] In traditional technology, the identification of autonomous driving areas mainly relies on static maps and pre-annotated high-precision data. However, the identification method in traditional technology has limitations in actual environments, especially under dynamically changing road conditions. The accuracy and reliability of the identification method based only on static maps and pre-annotated data are low, which is prone to safety hazards. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, storage medium and vehicle equipment for identifying autonomous driving areas that can improve the reliability of recognition accuracy in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a method for identifying an autonomous driving area. The method comprises: Acquire a first road condition parameter, where the first road condition parameter is a road condition parameter collected when the vehicle is in a manual driving state and is traveling along a route to be identified in the area to be identified; determining whether the first road condition parameter satisfies a preset first automatic driving road condition, and if so, determining that the to-be-identified route is an automatic driving route; In the case where it is determined that the route to be identified is an autonomous driving route, obtaining a second road condition parameter, where the second road condition parameter is a road condition parameter collected when the vehicle is in an autonomous driving state and is traveling along the autonomous driving route; It is determined whether the second road condition parameter satisfies a preset second autonomous driving road condition; if the second autonomous driving road condition is satisfied, the area to be identified is determined to be an autonomous driving area.

[0006] In one embodiment, the first traffic condition parameter includes a plurality of first traffic condition sub-parameters, the first preset condition includes a plurality of first preset sub-conditions corresponding to the plurality of first traffic condition sub-parameters; and the determining whether the first traffic condition sub-parameters satisfy the corresponding first preset sub-conditions includes: Determining whether the first road condition sub-parameter satisfies a corresponding first preset sub-condition; If all first road condition sub-parameters satisfy the corresponding first preset sub-conditions, it is determined that the first road condition parameters satisfy the preset first automatic driving road condition.

[0007] In one embodiment, the first traffic condition sub-parameter includes a first network status parameter; and determining whether the first traffic condition sub-parameter satisfies a corresponding first preset sub-condition includes: Based on the first network status parameters and the first network status threshold of the plurality of first network detection points, respectively determining the network status detection result of each first network detection point, wherein every two adjacent first network detection points are separated by a first preset distance in the route to be identified; Determine a first network status qualification rate based on the network status detection results of all first network detection points and the number of first network detection points; If the first network status qualification rate is greater than a first qualification rate threshold, it is determined that the corresponding first preset sub-condition is met.

[0008] In one embodiment, the first road condition sub-parameter includes road width; and determining whether the first road condition sub-parameter satisfies the corresponding first preset sub-condition includes: Based on the road widths of the plurality of road width detection points and the road width threshold, determining whether the road width of each road width detection point is greater than the road width threshold, wherein every two adjacent road width detection points are separated by a second preset distance in the route to be identified; If the road widths of all road width detection points are greater than the road width threshold, it is determined that the corresponding first preset sub-condition is met.

[0009] In one embodiment, the first road condition sub-parameter includes ambient light intensity; and determining whether the first road condition sub-parameter satisfies the corresponding first preset sub-condition includes: Based on the ambient light intensity and the light intensity threshold of the multiple light intensity detection points, respectively judging whether the ambient light intensity of each light intensity detection point is greater than the light intensity threshold, wherein every two adjacent light intensity detection points are separated by a third preset distance in the route to be identified; If the ambient light intensities at all light intensity detection points are greater than the light intensity threshold, it is determined that the corresponding first preset sub-condition is met.

[0010] In one embodiment, the first traffic condition sub-parameter includes a minimum turning radius and an intersection spacing, and the intersection spacing includes a distance between every two adjacent intersections in the route to be identified; and determining whether the first traffic condition sub-parameter satisfies the corresponding first preset sub-condition includes: Determine whether the minimum turning radius of each intersection in the to-be-identified route is greater than a radius threshold, and determine whether the intersection spacing is greater than an intersection spacing threshold; If the minimum turning radius of all intersections is greater than the radius threshold and the intersection spacing is greater than the intersection spacing threshold, it is determined that the corresponding first preset sub-condition is met.

[0011] In one embodiment, the second traffic condition parameter includes a plurality of second traffic condition sub-parameters, and the second preset condition includes a plurality of second preset sub-conditions corresponding to the plurality of second traffic condition sub-parameters; and determining whether the second traffic condition sub-parameters satisfy the corresponding second preset sub-conditions includes: Determining whether the second road condition sub-parameter satisfies a corresponding second preset sub-condition; If all the second road condition sub-parameters satisfy the corresponding second preset sub-conditions, it is determined that the second road condition parameters satisfy the preset second automatic driving road condition.

[0012] In one embodiment, the second road condition sub-parameter includes the number of autonomous driving tests and the number of autonomous driving completions in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determining whether the number of autonomous driving tests is greater than a preset number of tests, and determining whether the number of autonomous driving completions is greater than a preset number of completions, the preset number of tests being greater than or equal to the preset number of completions; If the number of autonomous driving tests is greater than the preset number of tests and the number of autonomous driving completions is greater than the preset number of completions, it is determined that the corresponding second preset sub-condition is met.

[0013] In one embodiment, the second road condition sub-parameter includes the number of oncoming vehicles passing, and the number of oncoming vehicles passing includes the number of vehicles passing during a single oncoming waiting period in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determine whether the number of oncoming vehicles passing is less than a preset threshold number of passing vehicles; If the number of oncoming vehicles passing is less than the preset passing vehicle number threshold, it is determined that the corresponding second preset sub-condition is met.

[0014] In one embodiment, the second road condition sub-parameter includes the number of vehicles passing through the intersection, and the number of vehicles passing through the intersection includes the number of vehicles traveling together and the number of vehicles traveling against the flow at each intersection in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determining whether the number of the same-traveling vehicles is less than a preset threshold number of the same-traveling vehicles, and determining whether the number of the wrong-traveling vehicles is less than a preset threshold number of the wrong-traveling vehicles; If the number of the same-travel vehicles is less than the preset threshold number of the same-travel vehicles and the number of the wrong-way vehicles is less than the preset threshold number of the wrong-way vehicles, it is determined that the corresponding second preset sub-condition is met.

[0015] In one embodiment, the second traffic condition sub-parameter includes a second network status parameter; and determining whether the second traffic condition sub-parameter satisfies the corresponding second preset sub-condition includes: Based on the second network status parameters and the second network status threshold of the plurality of second network detection points, respectively determining the network status detection result of each second network detection point, wherein every two adjacent second network detection points are separated by a fourth preset distance in the route to be identified; Determining a second network status qualification rate based on the network status detection results of all second network detection points and the number of second network detection points; If the second network status qualification rate is greater than a second qualification rate threshold, it is determined that the corresponding second preset sub-condition is met.

[0016] In one embodiment, the second road condition sub-parameter includes the number of pedestrian recognition, and the number of pedestrian recognition includes the number of pedestrians recognized every fifth preset distance traveled in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determining whether the number of pedestrians identified is less than a preset pedestrian number threshold; If the number of pedestrians identified is less than the preset pedestrian number threshold, it is determined that the corresponding second preset sub-condition is met.

[0017] In one embodiment, the second road condition sub-parameter includes at least one pedestrian recognition duration, and the pedestrian recognition duration includes the duration of each pedestrian recognition in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determining a cumulative pedestrian recognition duration in the autonomous driving route based on the at least one pedestrian recognition duration; It is determined whether the accumulated pedestrian recognition time is less than a preset time threshold, and if so, it is determined that the corresponding second preset sub-condition is met.

[0018] In a second aspect, the present application also provides a device for identifying an area in which autonomous driving can be performed. The device comprises: A first road condition parameter acquisition module, used to acquire a first road condition parameter, where the first road condition parameter is a road condition parameter collected when the vehicle is in a manual driving state and is traveling along a route to be identified in the area to be identified; an autonomous driving route determination module, configured to determine whether the first road condition parameter satisfies a preset first autonomous driving road condition, and if so, determine that the to-be-identified route is an autonomous driving route; A second road condition parameter acquisition module is used to acquire a second road condition parameter when it is determined that the route to be identified is an autonomous driving route, where the second road condition parameter is a road condition parameter collected when the vehicle is in an autonomous driving state and is traveling along the autonomous driving route; The module for determining an area capable of automatic driving is used to determine whether the second road condition parameter satisfies a preset second road condition condition for automatic driving. If the second road condition condition for automatic driving is satisfied, the area to be identified is determined to be an area capable of automatic driving.

[0019] In a third aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods for identifying autonomous driving areas in the first aspect.

[0020] In a fourth aspect, the present application further provides a vehicle device. The vehicle device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of any of the methods for identifying an autonomous driving area in the first aspect when executing the computer program.

[0021] The above-mentioned method, device, storage medium and vehicle equipment for identifying an area that can be autonomously driven firstly manually drive along the route to be identified and obtain the first road condition parameter to determine whether the preset first autonomous driving road condition is met. If the preset first autonomous driving road condition is met, the vehicle automatically drives along the route that can be autonomously driven and obtains the second road condition parameter to further determine whether the second road condition sub-parameter meets the preset second autonomous driving road condition. The present application obtains the first road condition parameter and the second road condition parameter for multiple judgments, fully considering the dynamically changing road conditions. Compared with the traditional technology, it overcomes the limitation of the traditional technology's reliance on static maps and improves the flexibility, reliability and accuracy of identifying areas that can be autonomously driven.

[0022] Details of one or more embodiments of the present application are set forth in the following drawings and description to make other features, objects, and advantages of the present application more readily apparent. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1A diagram of an application environment of a method for identifying an area capable of autonomous driving in one embodiment; Figure 2 1 is a flow chart of a method for identifying an area capable of autonomous driving in one embodiment; Figure 3 It is a flowchart of a method for identifying an area capable of autonomous driving in a specific embodiment; Figure 4 is a structural block diagram of a device for identifying an area capable of autonomous driving in one embodiment; Figure 5 FIG. 4 is a diagram showing the internal structure of a vehicle device in one embodiment. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0025] The terms "module", "unit", etc. used below are a combination of software and / or hardware that can implement a predetermined function. Although the devices described in the following embodiments are preferably implemented in hardware, the implementation of software, or a combination of software and hardware is also possible and conceivable.

[0026] The method for identifying an autonomous driving area provided in the embodiment of the present application can be applied to the following examples: Figure 1 In the application environment shown in FIG. 1 , a vehicle 102 is provided with a data acquisition device 104 and a vehicle device 106 ( Figure 1), the data acquisition device 104 may include a camera device, a laser radar, etc., and the vehicle device 106 includes a memory and a processor. The data acquisition device 104 obtains a first road condition parameter and sends it to the vehicle device 106. The first road condition parameter is the road condition parameter collected during the vehicle 102 is in a manual driving state and driving along the route to be identified in the area to be identified. The vehicle device 106 determines whether the first road condition parameter meets the preset first automatic driving road condition condition. If the first automatic driving road condition condition is met, the route to be identified is determined to be an automatic driving route. In the case of determining that the route to be identified is an automatic driving route, the data acquisition device 104 obtains a second road condition parameter and sends it to the vehicle device 106. The second road condition parameter is the road condition parameter collected during the vehicle 102 is in an automatic driving state and driving along the route to be identified. The vehicle device 106 determines whether the second road condition parameter meets the preset second automatic driving road condition condition. If the second automatic driving road condition condition is met, the area to be identified is determined to be an automatic driving area. In other embodiments, the vehicle equipment can also be connected to the cloud server network to send the first road condition parameter and the second road condition parameter to the cloud server, and the cloud server determines whether the first road condition parameter meets the preset first automatic driving road condition and whether the second road condition parameter meets the preset second automatic driving road condition.

[0027] In one embodiment, Figure 2 As shown, a method for identifying an autonomous driving area is provided, comprising the following steps: S201: Acquire a first road condition parameter, where the first road condition parameter is a road condition parameter collected when a vehicle is in a manual driving state and is traveling along a route to be identified in an area to be identified.

[0028] In the embodiment of the present application, the area to be identified may include a driving area and / or a parking area. The driving area may include a road for motor vehicles, and may also include a road for non-motor vehicles, a pedestrian road, etc. The parking area may include a parking lot, a temporary parking area, etc. The parking lot may include a road parking lot, an underground parking lot, or other parking areas where parking is available.

[0029] The route to be identified includes the vehicle's driving route in the area to be identified, and it has not yet been determined whether the vehicle can be driven automatically. In some embodiments, the driving start point and the driving end point are determined in the area to be identified, and the route to be identified can be the driving route of the vehicle from the driving start point to the driving end point. In other embodiments, if the area to be identified is a parking area, such as an underground parking lot, the route to be identified can be the driving route of the vehicle from the entrance of the underground parking lot to the target parking space.

[0030] When the vehicle is in manual driving mode and is traveling along the route to be identified in the area to be identified, the first road condition parameter may be obtained. The first road condition parameter may include road condition information collected while the vehicle is traveling along the route to be identified. In some embodiments, the first road condition parameter may include a plurality of first road condition sub-parameters, and the first road condition sub-parameter may be a first network state parameter, road width, ambient light intensity, minimum turning radius, and intersection spacing, etc. The first road condition parameter may be used to determine whether the route to be identified meets the automatic driving conditions.

[0031] In some embodiments, the first road condition parameter can be obtained by a data acquisition device disposed on the vehicle. Specifically, the road width, the minimum turning radius, and the intersection spacing can be obtained by a vehicle-mounted radar, and the ambient light intensity can be obtained by a light sensor. This application does not limit the specific method of obtaining the first road condition parameter.

[0032] S203: Determine whether the first road condition parameter satisfies a preset first autonomous driving road condition; if so, determine that the route to be identified is an autonomous driving route.

[0033] In an embodiment of the present application, by setting a preset first autonomous driving road condition and determining whether the first road condition parameter meets the preset first autonomous driving road condition, it can be determined whether the route to be identified is an autonomous driving route.

[0034] In some embodiments, if the first road condition parameter includes multiple first road condition sub-parameters, a first preset sub-condition can be set for each road condition sub-parameter respectively. If all first road condition sub-parameters satisfy the first preset sub-condition, or a first preset number of first road condition sub-parameters satisfy the first preset sub-condition, the route to be identified is determined to be an autonomous driving route, otherwise the route to be identified is determined to be a non-autonomous driving route. In other embodiments, it is also possible to determine whether the route to be identified is an autonomous driving route through a machine learning model or a deep learning model. The model is obtained through historical data training. After the first road condition parameter is input, the model can be used to determine whether the first road condition parameter satisfies the preset first autonomous driving road condition, and output an output result that the route to be identified is an autonomous driving route or a non-autonomous driving route.

[0035] In some embodiments, the first traffic condition parameter includes a plurality of first traffic condition sub-parameters, and the first preset condition includes a plurality of first preset sub-conditions corresponding to the plurality of first traffic condition sub-parameters; and determining whether the first traffic condition sub-parameter satisfies the corresponding first preset sub-condition includes: S301: Determine whether the first road condition sub-parameter satisfies a corresponding first preset sub-condition.

[0036] S303: If all first road condition sub-parameters satisfy the corresponding first preset sub-conditions, it is determined that the first road condition parameters satisfy the preset first automatic driving road condition.

[0037] In the embodiment of the present application, the first road condition parameter may include multiple first road condition sub-parameters, and a corresponding first preset sub-condition is set for each first road condition sub-parameter. It is determined whether each first road condition sub-parameter satisfies the corresponding first preset sub-condition. If all first road condition sub-parameters satisfy the corresponding first preset sub-condition, it is determined that the first road condition parameter satisfies the preset first automatic driving road condition. If there is at least one first road condition sub-parameter that does not satisfy the corresponding first preset sub-condition, it is determined that the first road condition parameter does not satisfy the preset first automatic driving road condition.

[0038] S205: When it is determined that the route to be identified is an autonomous driving route, a second road condition parameter is obtained, where the second road condition parameter is a road condition parameter collected when the vehicle is in an autonomous driving state and is traveling along the autonomous driving route.

[0039] In an embodiment of the present application, when it is determined that the route to be identified is an autonomous driving route, it is necessary to test driving along the autonomous driving route in an autonomous driving state to further determine whether the area to be identified is an autonomous driving area.

[0040] When the vehicle is in the automatic driving state and is traveling along the route that can be automatically driven, the second road condition parameter can be obtained. The second road condition parameter may include road condition information collected while the vehicle is traveling along the route that can be automatically driven. In some embodiments, the second road condition parameter may include multiple second road condition sub-parameters, and the second road condition sub-parameters may include the number of automatic driving tests and the number of automatic driving completions in the route that can be automatically driven, the number of oncoming vehicles passing, the number of vehicles passing at the intersection, the second network status parameter, the number of pedestrian recognitions, at least one pedestrian recognition duration, etc. The second road condition parameter can be used to further determine whether the area to be recognized meets the automatic driving conditions.

[0041] In some embodiments, the second road condition parameter can be obtained through a data acquisition device installed on the vehicle. Specifically, the number of pedestrians recognized can be obtained through a vehicle camera, and the second network status parameter can be obtained through a vehicle network test device. This application does not limit the specific method of obtaining the second road condition parameter.

[0042] S207: Determine whether the second road condition parameter satisfies a preset second autonomous driving road condition. If the second autonomous driving road condition is satisfied, determine that the area to be identified is an autonomous driving area.

[0043] In an embodiment of the present application, by setting a preset second autonomous driving road condition and determining whether the second road condition parameter meets the preset second autonomous driving road condition, it can be further determined whether the area to be identified is an autonomous driving area.

[0044] In some embodiments, if the second road condition parameter includes multiple second road condition sub-parameters, a second preset sub-condition can be set for each second road condition sub-parameter respectively. If all second road condition sub-parameters satisfy the second preset sub-condition, or a second preset number of second road condition sub-parameters satisfy the second preset sub-condition, the area to be identified is determined to be an area where the autonomous driving can be performed, otherwise the area to be identified is determined to be an area where the autonomous driving cannot be performed. In other embodiments, it is also possible to determine whether the area to be identified is an area where the autonomous driving can be performed by a machine learning model or a deep learning model. The model is obtained by training with historical data. After the second road condition parameter is input, the model can be used to determine whether the second road condition parameter satisfies the preset second autonomous driving road condition, and output an output result that the area to be identified is an area where the autonomous driving can be performed or an area where the autonomous driving cannot be performed.

[0045] In some embodiments, the second traffic condition parameter includes a plurality of second traffic condition sub-parameters, and the second preset condition includes a plurality of second preset sub-conditions corresponding to the plurality of second traffic condition sub-parameters; and determining whether the second traffic condition sub-parameter satisfies the corresponding second preset sub-condition includes: S401: Determine whether the second road condition sub-parameter satisfies a corresponding second preset sub-condition.

[0046] S403: If all the second road condition sub-parameters satisfy the corresponding second preset sub-conditions, it is determined that the second road condition parameters satisfy the preset second automatic driving road condition.

[0047] In the embodiment of the present application, the second road condition parameter may include multiple second road condition sub-parameters, and a corresponding second preset sub-condition is set for each second road condition sub-parameter. It is determined whether each second road condition sub-parameter satisfies the corresponding second preset sub-condition. If all second road condition sub-parameters satisfy the corresponding second preset sub-condition, it is determined that the second road condition parameter satisfies the preset second automatic driving road condition. If there is at least one second road condition sub-parameter that does not satisfy the corresponding second preset sub-condition, it is determined that the second road condition parameter does not satisfy the preset second automatic driving road condition.

[0048] The method for identifying an area that can be autonomously driven provided in an embodiment of the present application first manually drives in the route to be identified and obtains a first road condition parameter to determine whether the preset first autonomous driving road condition condition is met. If the preset first autonomous driving road condition condition is met, the vehicle automatically drives along the route that can be autonomously driven and obtains a second road condition parameter to further determine whether the second road condition sub-parameter meets the preset second autonomous driving road condition condition. The present application makes multiple judgments on the road condition of the area to be identified by obtaining the first road condition parameter and the second road condition parameter, fully considering the dynamically changing road condition conditions. Compared with traditional technologies, the present application overcomes the limitation of traditional technologies that rely on static maps and improves the flexibility, reliability and accuracy of identifying areas that can be autonomously driven.

[0049] The following describes an implementation method for determining whether a first road condition sub-parameter satisfies a corresponding first preset sub-condition through an embodiment of the present application.

[0050] In some embodiments, the first traffic condition sub-parameter includes a first network status parameter; and determining whether the first traffic condition sub-parameter satisfies a corresponding first preset sub-condition includes: S501: Based on first network status parameters and a first network status threshold of a plurality of first network detection points, respectively determine a network status detection result of each first network detection point, wherein every two adjacent first network detection points are separated by a first preset distance in the route to be identified.

[0051] S503: Determine a first network status qualification rate based on the network status detection results of all first network detection points and the number of first network detection points.

[0052] S505: If the first network status qualification rate is greater than a first qualification rate threshold, it is determined that a corresponding first preset sub-condition is met.

[0053] In the embodiment of the present application, the network status on the route to be identified can be used to determine whether the autonomous driving condition is met. The first road condition sub-parameter includes a first network status parameter, which can be a network signal strength value or a network uplink and downlink data transmission value per second.

[0054] A plurality of first network detection points are set in the route to be identified, and the interval between each two adjacent first network detection points is a first preset distance. The first preset distance may also include a plurality of first preset sub-distances, and each first preset sub-distance may be the same or different. That is, the interval between each two adjacent first network detection points may be the same or different. In some specific embodiments, the first preset distance may be 10 meters, that is, a first network detection point is set every 10 meters in the route to be identified.

[0055] A first network status parameter is obtained at each first network detection point, and the network status parameter of each first network detection point is compared with the first network status threshold to determine the network status detection result of each first network detection point. The network status detection result may include a qualified network status detection or an unqualified network status detection. In some specific embodiments, if the first network status parameter is a network signal strength value, such as a signal strength value determined by the vehicle network module through a network connection test with the cloud, when the network signal strength value is greater than the preset first network strength threshold, the network status detection result of the network detection point is determined to be qualified, otherwise the network status detection result is determined to be unqualified.

[0056] Based on the network status detection results of all first network detection points and the number of first network detection points, the first network status qualification rate is determined. In some specific embodiments, the first network status qualification rate can be determined by dividing the number of first network detection points with qualified network status detection results by the number of first network detection points.

[0057] If the first network status pass rate is greater than the first pass rate threshold, it is determined that the corresponding first preset sub-condition is met. Specifically, if there are 100 network detection points in total, and the detection results of 96 network detection points are qualified, the first network status pass rate is 96 / 100=96%. If the first pass rate threshold is set to 95%, the first network status pass rate is greater than the first pass rate threshold, and it is determined that the corresponding first preset sub-condition is met.

[0058] The present application sets a network detection point and a first pass rate threshold in the route to be identified to judge the overall network connection status on the route to be identified. When the network connection status is good, it is determined that the first road condition sub-parameter meets the corresponding first preset sub-condition, that is, it meets the judgment requirements of autonomous driving, and can fully consider the network connection status in actual road conditions, thereby improving the reliability and accuracy of identifying the autonomous driving area.

[0059] In some embodiments, the first road condition sub-parameter includes road width; and determining whether the first road condition sub-parameter satisfies the corresponding first preset sub-condition includes: S601: Based on the road widths of multiple road width detection points and a road width threshold, determine whether the road width of each road width detection point is greater than the road width threshold, wherein every two adjacent road width detection points are separated by a second preset distance in the route to be identified.

[0060] S603: If the road widths of all road width detection points are greater than the road width threshold, it is determined that the corresponding first preset sub-condition is met.

[0061] In the embodiment of the present application, the width of the road on the route to be identified can be used to determine whether the automatic driving condition is met. The first road condition sub-parameter includes the road width. It can be understood that the wider the road width, the higher the safety of the vehicle's automatic driving.

[0062] A plurality of road width detection points are set in the route to be identified, and the interval between each two adjacent road width detection points is a second preset distance. The second preset distance may also include a plurality of second preset sub-distances, and each second preset sub-distance may be the same or different. That is, the interval between each two adjacent road width detection points may be the same or different. In some specific embodiments, the second preset distance may be 10 meters, that is, a road width detection point is set every 10 meters in the route to be identified.

[0063] The road width is obtained at each road width detection point, and the road width at each road width detection point is compared with the road width threshold, and it is determined whether the road width at each road width detection point is greater than the road width threshold. If the road widths of all road width detection points are greater than the road width threshold, it is determined that the corresponding first preset sub-condition is met. In some specific embodiments, the road condition threshold can be set to 6 meters.

[0064] The present application sets a road width detection point and a road width threshold in the route to be identified to judge the overall road width on the route to be identified, and when the road width meets the conditions, determines that the first road condition sub-parameter satisfies the corresponding first preset sub-condition, that is, it meets the judgment requirements of autonomous driving, and can fully consider the road width in actual road conditions, thereby improving the reliability and accuracy of identifying the autonomous driving area.

[0065] In some embodiments, the first road condition sub-parameter includes ambient light intensity; and determining whether the first road condition sub-parameter satisfies the corresponding first preset sub-condition includes: S701: Based on the ambient light intensity and the light intensity threshold of multiple light intensity detection points, determine whether the ambient light intensity of each light intensity detection point is greater than the light intensity threshold, wherein every two adjacent light intensity detection points are separated by a third preset distance in the route to be identified.

[0066] S703: If the ambient light intensities of all light intensity detection points are greater than the light intensity threshold, it is determined that the corresponding first preset sub-condition is met.

[0067] In the embodiment of the present application, the ambient light intensity on the route to be identified can be used to determine whether the automatic driving condition is met. The first road condition sub-parameter includes the ambient light intensity. It can be understood that the ambient light intensity needs to meet the lighting conditions of the vehicle data acquisition device, thereby realizing the automatic driving of the vehicle.

[0068] A plurality of light intensity detection points are set in the route to be identified, and the interval between each two adjacent light intensity detection points is a third preset distance. The third preset distance may also include a plurality of third preset sub-distances, and each third preset sub-distance may be the same or different. That is, the interval between each two adjacent light intensity detection points may be the same or different. In some specific embodiments, the third preset distance may be 10 meters, that is, a light intensity detection point is set every 10 meters in the route to be identified.

[0069] The ambient light intensity is obtained at each light intensity detection point, and the ambient light intensity at each light intensity detection point is compared with the light intensity threshold, and it is determined whether the ambient light intensity at each light intensity detection point is greater than the light intensity threshold. If the ambient light intensity at all light intensity detection points is greater than the light intensity threshold, it is determined that the corresponding first preset sub-condition is met. In some specific embodiments, the light intensity threshold can be set to 80LUX.

[0070] The present application sets light intensity detection points and light intensity thresholds in the route to be identified to determine the overall ambient light intensity on the route to be identified, and when the ambient light intensity meets the conditions, determines that the first road condition sub-parameter satisfies the corresponding first preset sub-condition, that is, it meets the judgment requirements of autonomous driving, and can fully consider the ambient light intensity in actual road conditions, thereby improving the reliability and accuracy of identifying the autonomous driving area.

[0071] In some embodiments, the first traffic condition sub-parameter includes a minimum turning radius and an intersection spacing, and the intersection spacing includes a distance between every two adjacent intersections in the route to be identified; and determining whether the first traffic condition sub-parameter satisfies the corresponding first preset sub-condition includes: S801: Determine whether the minimum turning radius of each intersection in the to-be-identified route is greater than a radius threshold, and determine whether the intersection spacing is greater than an intersection spacing threshold.

[0072] S803: If the minimum turning radius of all intersections is greater than the radius threshold and the intersection spacing is greater than the intersection spacing threshold, it is determined that the corresponding first preset sub-condition is met.

[0073] In the embodiment of the present application, whether the automatic driving condition is met can be determined by the minimum turning radius and the intersection spacing on the route to be identified. The first road condition sub-parameter includes the minimum turning radius and the intersection spacing. It can be understood that the larger the minimum turning radius and the intersection spacing, the higher the safety of the vehicle's automatic driving.

[0074] Determine whether the minimum turning radius of each intersection in the route to be identified is greater than a radius threshold, and determine whether the intersection spacing is greater than an intersection spacing threshold. Wherein, the intersection may include a corner intersection, a T-junction, a crossroads, etc. in the route to be identified. The intersection spacing includes the distance between each two adjacent intersections in the route to be identified.

[0075] If the minimum turning radius of all intersections is greater than the radius threshold and the intersection spacing is greater than the intersection spacing threshold, it is determined that the corresponding first preset sub-condition is met. In some specific embodiments, the radius threshold may be 6 meters, and the intersection spacing threshold may be 100 meters.

[0076] The present application determines that the first road condition sub-parameter meets the corresponding first preset sub-condition when it is judged that the intersection spacing and intersection density on the route to be identified meet the requirements, that is, it meets the judgment requirements of autonomous driving. It can fully consider the difficulty of the vehicle automatically passing through the intersection under actual road conditions, thereby improving the reliability and accuracy of the identification of the autonomous driving area.

[0077] The following is an explanation of an implementation method of determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition when it is determined that the route to be identified is an autonomous driving route through an embodiment of the present application.

[0078] In some embodiments, the second road condition sub-parameter includes the number of autonomous driving tests and the number of autonomous driving completions in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: S901: Determine whether the number of autonomous driving tests is greater than the preset number of tests, and determine whether the number of autonomous driving completions is greater than the preset number of completions, and the preset number of tests is greater than or equal to the preset number of completions.

[0079] S903: If the number of autonomous driving tests is greater than the preset number of tests and the number of autonomous driving completions is greater than the preset number of completions, it is determined that the corresponding second preset sub-condition is met.

[0080] In an embodiment of the present application, when it is determined that the route to be identified is an autonomous driving route, it is necessary to control the vehicle in an autonomous driving state, travel along the autonomous driving route multiple times and obtain a second road condition parameter to further determine whether the area to be identified meets the conditions for autonomous driving.

[0081] The second road condition sub-parameter includes the number of autonomous driving tests and the number of autonomous driving completions on the autonomous driving route. The number of autonomous driving tests can be the number of attempts to drive along the autonomous driving route. If the autonomous driving function is terminated before reaching the end of the autonomous driving route, or the vehicle successfully drives to the end of the autonomous driving route, the number of autonomous driving tests increases by 1. The number of autonomous driving completions is the number of times the vehicle drives along the autonomous driving route from the starting point of the autonomous driving route and successfully drives to the end of the autonomous driving route.

[0082] If the number of autonomous driving tests is greater than the preset number of tests and the number of autonomous driving completions is greater than the preset number of completions, it is determined that the corresponding second preset sub-condition is met. In some specific embodiments, the preset number of tests is 5 and the preset number of completions is 3, that is, if the autonomous driving test is completed 3 times out of 5 times, it is considered that the corresponding second preset sub-condition is met.

[0083] In an embodiment of the present application, by performing autonomous driving tests multiple times on autonomous driving routes in the area to be identified, the number of autonomous driving tests and the number of autonomous driving completions are used to further determine whether the actual road conditions meet the autonomous driving conditions, thereby effectively improving the reliability and accuracy of identifying the autonomous driving area.

[0084] In some embodiments, the second road condition sub-parameter includes the number of oncoming vehicles passing, and the number of oncoming vehicles passing includes the number of vehicles passing during a single oncoming waiting period in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: S1001: Determine whether the number of oncoming vehicles passing is less than a preset threshold value of the number of oncoming vehicles.

[0085] S1003: If the number of oncoming vehicles passing is less than the preset threshold number of passing vehicles, determine that the corresponding second preset sub-condition is met.

[0086] In the embodiment of the present application, the number of oncoming vehicles passing in the area to be identified can be used to determine whether the autonomous driving condition is met. The second road condition sub-parameter includes the number of oncoming vehicles passing, and the number of oncoming vehicles passing includes the number of vehicles passing during a single oncoming waiting period in the route that can be autonomously driven. Specifically, the number of oncoming vehicles passing can be the sum of the number of passing vehicles and the number of oncoming vehicles detected when the vehicle is in a single oncoming deceleration or braking state.

[0087] It is determined whether the number of oncoming vehicles passing is less than a preset threshold number of passing vehicles, and if the number of oncoming vehicles passing is less than the preset threshold number of passing vehicles, it is determined that the corresponding second preset sub-condition is met. In some specific embodiments, the threshold number of vehicles may be 3.

[0088] The embodiment of the present application can determine the volume of traffic in the actual road conditions in the area to be identified by obtaining the number of oncoming vehicles passing through, and further determine whether the actual road conditions meet the conditions for autonomous driving, thereby effectively improving the reliability and accuracy of identifying the area that can be autonomously driven.

[0089] In some embodiments, the second road condition sub-parameter includes the number of vehicles passing through the intersection, and the number of vehicles passing through the intersection includes the number of vehicles traveling together and the number of vehicles traveling against the flow at each intersection in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: S1101: Determine whether the number of the same-traveling vehicles is less than a preset threshold number of the same-traveling vehicles, and determine whether the number of the wrong-traveling vehicles is less than a preset threshold number of the wrong-traveling vehicles.

[0090] S1103: If the number of the same-traveling vehicles is less than the preset threshold number of the same-traveling vehicles and the number of the wrong-traveling vehicles is less than the preset threshold number of the wrong-traveling vehicles, it is determined that the corresponding second preset sub-condition is met.

[0091] In the embodiment of the present application, the number of vehicles passing through the intersection in the area to be identified can be used to determine whether the autonomous driving conditions are met. The second road condition sub-parameter includes the number of vehicles passing through the intersection, and the number of vehicles passing through the intersection includes the number of vehicles traveling together and the number of vehicles traveling against the flow at each intersection in the autonomous driving route. Specifically, the number of vehicles passing through the intersection includes the number of vehicles traveling together and the number of vehicles traveling against the flow detected at each intersection in the autonomous driving route when braking or not braking.

[0092] Determine whether the number of traveling together vehicles is less than a preset traveling together vehicle number threshold, and determine whether the number of vehicles traveling against the flow is less than a preset traveling against the flow threshold. If the number of traveling together vehicles is less than the preset traveling together vehicle number threshold and the number of vehicles traveling against the flow is less than the preset traveling against the flow threshold, determine that the corresponding second preset sub-condition is met. In some specific embodiments, the traveling together vehicle number threshold may be 3, and the traveling against the flow threshold may be 3.

[0093] By obtaining the number of vehicles passing through the intersection, the embodiment of the present application can determine the volume of traffic at the intersection in the actual road conditions in the area to be identified, and further determine whether the actual road conditions meet the conditions for autonomous driving, thereby effectively improving the reliability and accuracy of identifying the area that can be autonomously driven.

[0094] In some embodiments, the second traffic condition sub-parameter includes a second network status parameter; and determining whether the second traffic condition sub-parameter satisfies the corresponding second preset sub-condition includes: S1201: Based on second network status parameters and second network status thresholds of multiple second network detection points, determine a network status detection result of each second network detection point respectively, wherein every two adjacent second network detection points are separated by a fourth preset distance in the route to be identified.

[0095] S1203: Determine a second network status qualification rate based on the network status detection results of all second network detection points and the number of second network detection points.

[0096] S1205: If the second network status qualification rate is greater than a second qualification rate threshold, determine that the corresponding second preset sub-condition is met.

[0097] In the embodiment of the present application, the network status on the autonomous driving route can be used to further determine whether the autonomous driving condition is met. The second road condition sub-parameter includes a second network status parameter, which can be a network signal strength value or a network uplink and downlink data transmission value per second.

[0098] A plurality of second network detection points are set in the route to be identified, and the interval between each two adjacent second network detection points is a fourth preset distance. The fourth preset distance may also include a plurality of fourth preset sub-distances, and each fourth preset sub-distance may be the same or different. That is, the interval between each two adjacent second network detection points may be the same or different. In some specific embodiments, the fourth preset distance may be 10 meters, that is, a second network detection point is set every 10 meters in the route to be identified.

[0099] The second network status parameter is obtained at each second network detection point, and the network status parameter of each second network detection point is compared with the second network status threshold to determine the network status detection result of each second network detection point. The network status detection result may include a qualified network status detection or an unqualified network status detection. In some specific embodiments, if the second network status parameter is a network signal strength value, such as a signal strength value determined by the vehicle network module through a network connection test with the cloud, when the network signal strength value is greater than the preset second network strength threshold, the network status detection result of the network detection point is determined to be qualified, otherwise the network status detection result is determined to be unqualified.

[0100] Based on the network status detection results of all second network detection points and the number of second network detection points, the second network status qualification rate is determined. In some specific embodiments, the second network status qualification rate can be determined by dividing the number of second network detection points with qualified network status detection results by the number of second network detection points.

[0101] If the second network status pass rate is greater than the second pass rate threshold, it is determined that the corresponding second preset sub-condition is met. Specifically, if there are 100 network detection points in total, and the detection results of 98 network detection points are qualified, the second network status pass rate is 98 / 100=98%. If the second pass rate threshold is set to 95%, the second network status pass rate is greater than the second pass rate threshold, and it is determined that the corresponding second preset sub-condition is met.

[0102] The present application further determines the overall network connection status on the autonomous driving route by setting a second network detection point and a second pass rate threshold in the autonomous driving route. When the network connection status is good, it determines that the second road condition sub-parameter meets the corresponding second preset sub-condition, that is, it meets the judgment requirements of autonomous driving, and can further verify the network connection status in actual road conditions, thereby improving the reliability and accuracy of identifying the autonomous driving area.

[0103] In some embodiments, the second road condition sub-parameter includes the number of pedestrian recognitions, and the number of pedestrian recognitions includes the number of pedestrians recognized every fifth preset distance traveled in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: S1301: Determine whether the number of pedestrians identified is less than a preset pedestrian number threshold.

[0104] S1303: If the number of pedestrians identified is less than the preset pedestrian number threshold, determine that the corresponding second preset sub-condition is met.

[0105] In the embodiment of the present application, the number of pedestrians identified in the area to be identified can be used to determine whether the autonomous driving condition is met. The second road condition sub-parameter includes the number of pedestrians identified, and the number of pedestrians identified includes the number of pedestrians identified every fifth preset distance in the route that can be autonomously driven.

[0106] Determine whether the number of pedestrians recognized is less than a preset pedestrian number threshold. If the number of pedestrians recognized is less than the preset pedestrian number threshold, determine that the corresponding second preset sub-condition is met. In some specific embodiments, the fifth preset distance may be 20 meters, and the pedestrian number threshold may be 3. If the number of pedestrians recognized every 20 meters in the autonomous driving route is less than 3, determine that the corresponding second preset sub-condition is met.

[0107] By obtaining the number of pedestrians identified, the embodiment of the present application can determine the size of the pedestrian flow in the actual road conditions in the area to be identified, and further determine whether the actual road conditions meet the conditions for autonomous driving, thereby effectively improving the reliability and accuracy of identifying the area that can be autonomously driven.

[0108] In some embodiments, the second road condition sub-parameter includes at least one pedestrian recognition duration, and the pedestrian recognition duration includes the duration of each pedestrian recognition in the autonomous driving route; and determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: S1401: Determine the cumulative pedestrian recognition duration in the autonomous driving route based on the at least one pedestrian recognition duration.

[0109] S1403: Determine whether the accumulated pedestrian recognition time is less than a preset time threshold, and if so, determine that the corresponding second preset sub-condition is satisfied.

[0110] In the embodiment of the present application, whether the automatic driving conditions are met can be determined by the duration of pedestrian recognition in the area to be recognized. The second road condition sub-parameter includes at least one pedestrian recognition duration, and the pedestrian recognition duration includes the duration of each pedestrian recognition in the route that can be automatically driven. Specifically, when the vehicle is driving along the route that can be automatically driven in the automatic driving state, each time a pedestrian is detected is the starting point of the timing, and the timing ends when there are no pedestrians in the detection range this time. The pedestrian recognition duration is the duration from the starting point of the timing to the end point of the timing.

[0111] The cumulative pedestrian recognition time in the autonomous driving route is determined based on at least one pedestrian recognition time. Specifically, all pedestrian recognition times are accumulated to determine the cumulative pedestrian recognition time. It is determined whether the cumulative pedestrian recognition time is less than a preset time threshold. If it is less than the preset time threshold, it is determined that the corresponding second preset sub-condition is met. In some specific embodiments, the preset time threshold may be 3 minutes.

[0112] The following describes the implementation of the method for identifying areas that can be driven autonomously through specific examples of the present application.

[0113] like Figure 3 As shown, after creating a route to be identified in the area to be identified, the network status is obtained every 10m during the vehicle driving process and it is judged whether it is qualified, the road width is obtained every 10m, the ambient light intensity is obtained every 10m, the minimum turning radius of each intersection on the route to be identified and the number of intersections passed every 100m are obtained, and the judgment is made according to the above-mentioned first road condition parameter. If the qualified rate of the network status in the route to be identified is greater than 95%, all road widths are greater than or equal to 6m, all ambient light intensities are greater than or equal to 80LUX, the minimum turning radius of all intersections is greater than or equal to 6m, and the number of intersections passed every 100m is less than or equal to 1, then the route to be identified is determined to be a route that can be driven without human drivers; if at least one of the above judgment conditions is not met, then the route to be identified is determined to be a route that cannot be driven without human drivers.

[0114] Further, verify the unmanned driving route and determine whether the area to be identified is an unmanned driving area. Control the vehicle to drive continuously on the unmanned driving route in an automatic driving state for 5 times, and determine whether the number of successful driving to the end point is greater than 3 times. Control the vehicle to drive continuously on the unmanned driving route in an automatic driving state for 3 times, and determine whether the number of vehicles passing during the single waiting time of the vehicle when meeting is less than or equal to 4 vehicles, and when the vehicle is within 5m of the intersection, determine whether the sum of the number of vehicles traveling together and the number of vehicles going against the flow at each intersection on the route when the vehicle stops is less than or equal to 3, determine whether the number of different pedestrians identified within every 20m of the vehicle is less than or equal to 3, determine whether the cumulative continuous identification time of pedestrians is less than or equal to 3min, obtain the network status every 10m and determine whether it is qualified, and determine whether the qualified rate of the network status is greater than 95%. If all the above judgment conditions are met, the area to be identified is determined to be an unmanned driving area; if at least one of the above judgment conditions is not met, the area to be identified is determined to be a non-unmanned driving area.

[0115] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0116] Based on the same inventive concept, the embodiment of the present application also provides an autonomous driving area identification device 1900 for implementing the autonomous driving area identification method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations of one or more embodiments of the autonomous driving area identification device 1900 provided below can refer to the limitations of the autonomous driving area identification method above, and will not be repeated here.

[0117] In one embodiment, Figure 4 As shown, a device 1900 for identifying an area capable of automatic driving is provided, comprising: A first road condition parameter acquisition module 1901 is used to acquire a first road condition parameter, where the first road condition parameter is a road condition parameter collected when the vehicle is in a manual driving state and is traveling along a route to be identified in the area to be identified; The autonomous driving route determining module 1902 is used to determine whether the first road condition parameter satisfies a preset first autonomous driving road condition, and if the first autonomous driving road condition is satisfied, determine that the route to be identified is an autonomous driving route; A second road condition parameter acquisition module 1903 is used to acquire a second road condition parameter when it is determined that the to-be-identified route is an autonomous driving route, where the second road condition parameter is a road condition parameter collected when the vehicle is in an autonomous driving state and is traveling along the autonomous driving route; The autonomous driving area determination module 1904 is used to determine whether the second road condition parameter meets a preset second autonomous driving road condition. If the second autonomous driving road condition is met, the area to be identified is determined to be an autonomous driving area.

[0118] In some embodiments, the first road condition parameter includes multiple first road condition sub-parameters, and the first preset condition includes multiple first preset sub-conditions corresponding to the multiple first road condition sub-parameters; the autonomous driving route judgment module 1902 is also used to determine whether the first road condition sub-parameters meet the corresponding first preset sub-conditions; if all the first road condition sub-parameters meet the corresponding first preset sub-conditions, it is determined that the first road condition parameter meets the preset first autonomous driving road condition.

[0119] In some embodiments, the first road condition sub-parameter includes a first network status parameter; the autonomous driving route judgment module 1902 is further used to determine the network status detection result of each first network detection point based on the first network status parameters of multiple first network detection points and the first network status threshold, wherein every two adjacent first network detection points are separated by a first preset distance in the route to be identified; based on the network status detection results of all first network detection points and the number of first network detection points, determine the first network status qualification rate; if the first network status qualification rate is greater than the first qualification rate threshold, it is determined that the corresponding first preset sub-condition is met.

[0120] In some embodiments, the first road condition sub-parameter includes road width; the autonomous driving route judgment module 1902 is further used to judge whether the road width of each road width detection point is greater than the road width threshold based on the road width of multiple road width detection points and the road width threshold, wherein every two adjacent road width detection points are separated by a second preset distance in the route to be identified; if the road widths of all road width detection points are greater than the road width threshold, it is determined that the corresponding first preset sub-condition is met.

[0121] In some embodiments, the first road condition sub-parameter includes ambient light intensity; the autonomous driving route judgment module 1902 is further used to judge whether the ambient light intensity of each light intensity detection point is greater than the light intensity threshold based on the ambient light intensity of multiple light intensity detection points and the light intensity threshold, wherein each two adjacent light intensity detection points are separated by a third preset distance in the route to be identified; if the ambient light intensity of all light intensity detection points is greater than the light intensity threshold, it is determined that the corresponding first preset sub-condition is met.

[0122] In some embodiments, the first road condition sub-parameter includes a minimum turning radius and an intersection spacing, and the intersection spacing includes the distance between each two adjacent intersections in the route to be identified; the autonomous driving route judgment module 1902 is also used to determine whether the minimum turning radius of each intersection in the route to be identified is greater than a radius threshold, and to determine whether the intersection spacing is greater than an intersection spacing threshold; if the minimum turning radius of all intersections is greater than the radius threshold and the intersection spacing is greater than the intersection spacing threshold, it is determined that the corresponding first preset sub-condition is met.

[0123] In some embodiments, the second road condition parameter includes multiple second road condition sub-parameters, and the second preset condition includes multiple second preset sub-conditions corresponding to the multiple second road condition sub-parameters; the autonomous driving area judgment module 1904 is also used to determine whether the second road condition sub-parameters meet the corresponding second preset sub-conditions; if all the second road condition sub-parameters meet the corresponding second preset sub-conditions, it is determined that the second road condition parameter meets the preset second autonomous driving road condition.

[0124] In some embodiments, the second road condition sub-parameter includes the number of autonomous driving tests and the number of autonomous driving completions in the autonomous driving route; the autonomous driving area judgment module 1904 is also used to determine whether the number of autonomous driving tests is greater than a preset number of tests, and to determine whether the number of autonomous driving completions is greater than a preset number of completions, and the preset number of tests is greater than or equal to the preset number of completions; if the number of autonomous driving tests is greater than the preset number of tests and the number of autonomous driving completions is greater than the preset number of completions, it is determined that the corresponding second preset sub-condition is met.

[0125] In some embodiments, the second road condition sub-parameter includes the number of oncoming vehicles passing, and the number of oncoming vehicles passing includes the number of vehicles passing during a single oncoming waiting period in the autonomous driving route; the autonomous driving area judgment module 1904 is further used to determine whether the number of oncoming vehicles passing is less than a preset threshold value of the number of passing vehicles; if the number of oncoming vehicles passing is less than the preset threshold value of the number of passing vehicles, it is determined that the corresponding second preset sub-condition is met.

[0126] In some embodiments, the second road condition sub-parameter includes the number of vehicles passing through the intersection, and the number of vehicles passing through the intersection includes the number of accompanying vehicles and the number of oncoming vehicles at each intersection in the autonomous driving route; the autonomous driving area judgment module 1904 is also used to determine whether the number of accompanying vehicles is less than a preset threshold value of the number of accompanying vehicles, and to determine whether the number of oncoming vehicles is less than a preset threshold value of the number of oncoming vehicles; if the number of accompanying vehicles is less than the preset threshold value of the number of accompanying vehicles and the number of oncoming vehicles is less than the preset threshold value of the number of oncoming vehicles, it is determined that the corresponding second preset sub-condition is met.

[0127] In some embodiments, the second road condition sub-parameter includes a second network status parameter; the autonomous driving area judgment module 1904 is further used to determine the network status detection result of each second network detection point based on the second network status parameters of multiple second network detection points and the second network status threshold, wherein every two adjacent second network detection points are separated by a fourth preset distance in the route to be identified; based on the network status detection results of all second network detection points and the number of second network detection points, determine the second network status qualification rate; if the second network status qualification rate is greater than the second qualification rate threshold, it is determined that the corresponding second preset sub-condition is met.

[0128] In some embodiments, the second road condition sub-parameter includes the number of pedestrian recognitions, and the number of pedestrian recognitions includes the number of pedestrians recognized every fifth preset distance traveled in the autonomous driving route; the autonomous driving area judgment module 1904 is also used to determine whether the number of pedestrian recognitions is less than a preset pedestrian number threshold; if the number of pedestrian recognitions is less than the preset pedestrian number threshold, it is determined that the corresponding second preset sub-condition is met.

[0129] In some embodiments, the second road condition sub-parameter includes at least one pedestrian recognition duration, and the pedestrian recognition duration includes the duration of each pedestrian recognition in the autonomous driving route; the autonomous driving area judgment module 1904 is also used to determine the cumulative pedestrian recognition duration in the autonomous driving route based on the at least one pedestrian recognition duration; determine whether the cumulative pedestrian recognition duration is less than a preset duration threshold, and if it is less than the preset duration threshold, determine that the corresponding second preset sub-condition is met.

[0130] Each module in the above-mentioned automatic driving area identification device 1900 can be implemented in whole or in part by software, hardware, or a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0131] In one embodiment, a vehicle device is provided, whose internal structure diagram can be as follows: Figure 5 As shown. The vehicle device includes a processor, a memory and a network interface connected via a system bus. Among them, the processor of the vehicle device is used to provide computing and control capabilities. The memory of the vehicle device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the vehicle device is used to store data. The network interface of the vehicle device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for identifying an area that can be driven automatically is implemented.

[0132] Those skilled in the art will understand that Figure 5 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the vehicle equipment to which the scheme of the present application is applied. The specific vehicle equipment may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0133] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any method for identifying an autonomous driving area in the first aspect described above are implemented.

[0134] In one embodiment, a computer program product is provided, comprising a computer program, which, when executed by a processor, implements the steps of any of the methods for identifying an autonomous driving area in the first aspect described above.

[0135] Unless otherwise defined, the technical terms or scientific terms involved in this application shall have the general meaning understood by people with general skills in the technical field to which this application belongs. The words "one", "a", "the", "these" and the like in this application do not indicate a quantitative limitation, and they may be singular or plural. The terms "include", "comprise", "have" and any variants thereof involved in this application are intended to cover non-exclusive inclusions; for example, a process, method and system, product or device comprising a series of steps or modules (units) is not limited to the listed steps or modules (units), but may include unlisted steps or modules (units), or may include other steps or modules (units) inherent to these processes, methods, products or devices. The words "connect", "connected", "coupled" and the like involved in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The "multiple" involved in this application refers to two or more. "And / or" describes the association relationship of associated objects, indicating that there may be three relationships, for example, "A and / or B" may mean: A exists alone, A and B exist at the same time, and B exists alone. Generally, the character " / " indicates that the objects associated with each other are in an "or" relationship. The terms "first", "second", "third", etc. in this application are only used to distinguish similar objects and do not represent a specific ordering of the objects.

[0136] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0137] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0138] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0139] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for identifying an autonomous driving area, characterized in that: The method comprises: Acquire a first road condition parameter, where the first road condition parameter is a road condition parameter collected when the vehicle is in a manual driving state and is traveling along a route to be identified in the area to be identified; determining whether the first road condition parameter satisfies a preset first automatic driving road condition, and if so, determining that the to-be-identified route is an automatic driving route; In the case where it is determined that the route to be identified is an autonomous driving route, obtaining a second road condition parameter, where the second road condition parameter is a road condition parameter collected when the vehicle is in an autonomous driving state and is traveling along the autonomous driving route; It is determined whether the second road condition parameter satisfies a preset second autonomous driving road condition; if the second autonomous driving road condition is satisfied, the area to be identified is determined to be an autonomous driving area.

2. The method according to claim 1, characterized in that: The first road condition parameter includes a plurality of first road condition sub-parameters, the first preset condition includes a plurality of first preset sub-conditions corresponding to the plurality of first road condition sub-parameters; and the determining whether the first road condition sub-parameter satisfies the corresponding first preset sub-condition includes: Determining whether the first road condition sub-parameter satisfies a corresponding first preset sub-condition; If all first road condition sub-parameters satisfy the corresponding first preset sub-conditions, it is determined that the first road condition parameters satisfy the preset first automatic driving road condition.

3. The method according to claim 2, characterized in that The first traffic condition sub-parameter includes a first network status parameter; and the determining whether the first traffic condition sub-parameter satisfies a corresponding first preset sub-condition includes: Based on the first network status parameters and the first network status threshold of the plurality of first network detection points, respectively determining the network status detection result of each first network detection point, wherein every two adjacent first network detection points are separated by a first preset distance in the route to be identified; Determine a first network status qualification rate based on the network status detection results of all first network detection points and the number of first network detection points; If the first network status qualification rate is greater than a first qualification rate threshold, it is determined that the corresponding first preset sub-condition is met.

4. The method according to claim 2, characterized in that: The first road condition sub-parameter includes road width; and the step of determining whether the first road condition sub-parameter satisfies the corresponding first preset sub-condition includes: Based on the road widths of the plurality of road width detection points and the road width threshold, determining whether the road width of each road width detection point is greater than the road width threshold, wherein every two adjacent road width detection points are separated by a second preset distance in the route to be identified; If the road widths of all road width detection points are greater than the road width threshold, it is determined that the corresponding first preset sub-condition is met.

5. The method according to claim 2, characterized in that: The first road condition sub-parameter includes ambient light intensity; and the step of determining whether the first road condition sub-parameter satisfies the corresponding first preset sub-condition includes: Based on the ambient light intensity and the light intensity threshold of the multiple light intensity detection points, respectively judging whether the ambient light intensity of each light intensity detection point is greater than the light intensity threshold, wherein every two adjacent light intensity detection points are separated by a third preset distance in the route to be identified; If the ambient light intensities at all light intensity detection points are greater than the light intensity threshold, it is determined that the corresponding first preset sub-condition is met.

6. The method according to claim 2, characterized in that The first traffic condition sub-parameter includes a minimum turning radius and an intersection spacing, and the intersection spacing includes a distance between every two adjacent intersections in the route to be identified; and determining whether the first traffic condition sub-parameter satisfies a corresponding first preset sub-condition includes: Determine whether the minimum turning radius of each intersection in the to-be-identified route is greater than a radius threshold, and determine whether the intersection spacing is greater than an intersection spacing threshold; If the minimum turning radius of all intersections is greater than the radius threshold and the intersection spacing is greater than the intersection spacing threshold, it is determined that the corresponding first preset sub-condition is met.

7. The method according to claim 1, characterized in that The second road condition parameter includes a plurality of second road condition sub-parameters, and the second preset condition includes a plurality of second preset sub-conditions corresponding to the plurality of second road condition sub-parameters; and the determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determining whether the second road condition sub-parameter satisfies a corresponding second preset sub-condition; If all the second road condition sub-parameters satisfy the corresponding second preset sub-conditions, it is determined that the second road condition parameters satisfy the preset second automatic driving road condition.

8. The method according to claim 7, characterized in that The second road condition sub-parameter includes the number of autonomous driving tests and the number of autonomous driving completions in the autonomous driving route; and the determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determining whether the number of autonomous driving tests is greater than a preset number of tests, and determining whether the number of autonomous driving completions is greater than a preset number of completions, the preset number of tests being greater than or equal to the preset number of completions; If the number of autonomous driving tests is greater than the preset number of tests and the number of autonomous driving completions is greater than the preset number of completions, it is determined that the corresponding second preset sub-condition is met.

9. The method according to claim 7, characterized in that: The second road condition sub-parameter includes the number of oncoming vehicles passing, and the number of oncoming vehicles passing includes the number of vehicles passing during a single oncoming waiting period in the autonomous driving route; The determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determine whether the number of oncoming vehicles passing is less than a preset threshold number of passing vehicles; If the number of oncoming vehicles passing is less than the preset passing vehicle number threshold, it is determined that the corresponding second preset sub-condition is met.

10. The method according to claim 7, characterized in that The second road condition sub-parameter includes the number of vehicles passing through the intersection, and the number of vehicles passing through the intersection includes the number of vehicles traveling together and the number of vehicles traveling against the flow at each intersection in the autonomous driving route; The determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determining whether the number of the same-traveling vehicles is less than a preset threshold number of the same-traveling vehicles, and determining whether the number of the wrong-traveling vehicles is less than a preset threshold number of the wrong-traveling vehicles; If the number of the same-travel vehicles is less than the preset threshold number of the same-travel vehicles and the number of the wrong-way vehicles is less than the preset threshold number of the wrong-way vehicles, it is determined that the corresponding second preset sub-condition is met.

11. The method according to claim 7, characterized in that The second traffic condition sub-parameter includes a second network status parameter; and the determining whether the second traffic condition sub-parameter satisfies the corresponding second preset sub-condition includes: Based on the second network status parameters and the second network status threshold of the plurality of second network detection points, respectively determining the network status detection result of each second network detection point, wherein every two adjacent second network detection points are separated by a fourth preset distance in the route to be identified; Determine a second network status qualification rate based on the network status detection results of all second network detection points and the number of second network detection points; If the second network status qualification rate is greater than a second qualification rate threshold, it is determined that the corresponding second preset sub-condition is met.

12. The method according to claim 7, characterized in that The second road condition sub-parameter includes the number of pedestrian recognition, and the number of pedestrian recognition includes the number of pedestrians recognized every fifth preset distance traveled on the autonomous driving route; The determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determining whether the number of pedestrians identified is less than a preset pedestrian number threshold; If the number of pedestrians identified is less than the preset pedestrian number threshold, it is determined that the corresponding second preset sub-condition is met.

13. The method according to claim 7, characterized in that The second road condition sub-parameter includes at least one pedestrian recognition duration, and the pedestrian recognition duration includes the duration of each pedestrian recognition in the autonomous driving route; The determining whether the second road condition sub-parameter satisfies the corresponding second preset sub-condition includes: Determining a cumulative pedestrian recognition duration in the autonomous driving route based on the at least one pedestrian recognition duration; It is determined whether the accumulated pedestrian recognition time is less than a preset time threshold, and if so, it is determined that the corresponding second preset sub-condition is met.

14. An autonomous driving area recognition device, characterized in that: The device comprises: A first road condition parameter acquisition module, used to acquire a first road condition parameter, where the first road condition parameter is a road condition parameter collected when the vehicle is in a manual driving state and is traveling along a route to be identified in the area to be identified; an autonomous driving route determination module, configured to determine whether the first road condition parameter satisfies a preset first autonomous driving road condition, and if the first autonomous driving road condition is satisfied, determine that the to-be-identified route is an autonomous driving route; A second road condition parameter acquisition module is used to acquire a second road condition parameter when it is determined that the route to be identified is an autonomous driving route, where the second road condition parameter is a road condition parameter collected when the vehicle is in an autonomous driving state and is traveling along the autonomous driving route; The module for determining an area capable of automatic driving is used to determine whether the second road condition parameter satisfies a preset second road condition condition for automatic driving. If the second road condition condition for automatic driving is satisfied, the area to be identified is determined to be an area capable of automatic driving.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 13 are implemented.

16. A vehicle device, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 13 are implemented.

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