Data comparison method and device

Abstract

The invention provides a data comparison method and device, which are used for comparing perception data acquired by a vehicle with map data, so as to give a targeted prompt aiming at the difference between the perception data and the map data, so that a user can pay attention to a region which is possibly changed in an actual scene, various emergencies can be dealt with, and the user experience is improved. And the vehicle driving safety is improved. The method comprises the steps that firstly, map data and perception data are obtained, the map data comprise data of a map used in the vehicle driving process, and the perception data comprise data, collected by a sensor in a vehicle in the vehicle driving process, of the environment where the vehicle is located; comparing the map data with the perception data to obtain a comparison result which can be used for representing whether there is a difference between the map data and the perception data; and then generating prompt information according to the comparison result, wherein the prompt information is used for prompting the difference between the map data and the perception data.

Description

Technical Field

[0001] The present application relates to the field of vehicles, and in particular to a data comparison method and device. Background Art

[0002] With the development of the automotive industry, intelligent driving functions such as autonomous driving and assisted driving are becoming more and more common. Maps have a significant impact on intelligent driving functions. Accurate maps can provide more accurate guidance for vehicle driving.

[0003] Map collection can be done in a variety of ways, including manually drawn high-precision maps, vehicle-generated data mapping (also known as self-constructed or self-built maps), or existing standard-based maps. Manually drawn high-precision maps are very expensive. To achieve a balance between cost and performance, self-constructed maps—that is, using sensory information collected while the vehicle is driving—will become the mainstream method for map collection.

[0004] However, in various map collection methods, especially for self-constructed maps, some road sections may be missing or inconsistent with actual road conditions. The actual environment may differ from the self-constructed map, such as the number of lanes increasing or decreasing over time, or certain road sections may be missing from the map. This can lead to inconsistencies between the actual road information and the map, which in turn affects the accuracy of the vehicle's intelligent driving functions. Therefore, obtaining more accurate road information to support intelligent driving functions has become a pressing issue. Summary of the Invention

[0005] The present application provides a data comparison method and device for comparing perception data and map data collected by a vehicle, thereby providing targeted prompts for differences between the perception data and the map data, allowing users to pay attention to areas that may change in actual scenarios, thereby being able to respond to various emergencies and improve vehicle driving safety.

[0006] In view of this, in a first aspect, the present application provides a data comparison method, comprising: first, obtaining map data and perception data, the map data including data of a map used during vehicle driving, the map data may be pre-saved in the vehicle or data received from the cloud, the perception data including data including the vehicle's environment collected by sensors in the vehicle during vehicle driving; then, comparing the map data with the perception data to obtain a comparison result, which may be used to indicate whether there is a difference between the map data and the perception data; then, generating prompt information based on the comparison result, which is used to prompt the difference between the map data and the perception data.

[0007] In the implementation mode of the present application, the data collected by the sensors during vehicle driving can be used to compare with the existing map to identify possible differences in the map, and provide targeted prompts so that the user can determine whether to adjust the driving strategy based on the prompts to improve vehicle driving safety.

[0008] In one possible implementation, the aforementioned comparison of the map data with the perception data to obtain a comparison result may include: obtaining information about at least one perception element included in the perception data, such as information about the coordinates, size, or shape of each entity in the perception data; obtaining information about at least one map element included in the map data, such as information about the coordinates, size, or shape of an entity in the map; and comparing the information about the at least one perception element with the information about the at least one map element to obtain a comparison result. In implementations of the present application, perception elements in the perception data may be compared with map elements in the map data to identify differences between the perception elements and the map elements, so that prompt information can be subsequently generated based on the differences between the elements.

[0009] In one possible implementation, the aforementioned perception data packet includes one or more of the following: information about entities in the vehicle's environment, such as the number of lanes, lane types, corresponding turns in the lanes, whether there is a waiting area in the lane, whether there is a stop line in the lane, the number of traffic lights, corresponding turns in the traffic lights. In this implementation, entities in the vehicle's environment can be perceived, thereby identifying the specific conditions in the vehicle's actual environment.

[0010] In one possible implementation, the aforementioned map data includes data from a map constructed based on data collected by sensors of at least one vehicle during a historical period. Therefore, the method provided in this application can be applied to self-mapped scenarios, thereby comparing the perceived data with the self-map to identify areas in the self-map that do not match the actual scene. This allows the user to promptly adjust the vehicle's driving style, ensuring safe driving.

[0011] In a possible embodiment, the aforementioned generation of prompt information based on the comparison results includes: obtaining navigation data; when the comparison results include differences between map data and perception data, superimposing annotation data on the area in the navigation data where there are differences between the map data and the perception data to obtain prompt information.

[0012] In the implementation manner of the present application, annotation data can be superimposed on the navigation data, so that the user can more intuitively know the difference between the actual scene and the map data.

[0013] In one possible implementation, generating prompt information based on the comparison results further includes: evaluating the map data based on the comparison results to obtain an evaluation value corresponding to the map data, the evaluation value including the confidence level of lane information in the map data; and overlaying the evaluation value on the navigation path to obtain the prompt information. In this implementation, a risk assessment of the map data can be performed based on the comparison results, thereby allowing the user to obtain the confidence level of each element in the map.

[0014] In one possible implementation, evaluating the map data based on the comparison results to obtain an evaluation value corresponding to the map data includes: performing a correctness or completeness evaluation on at least one of the following items in the map data based on the comparison results to obtain an evaluation value, the at least one item including at least one of lane information, road connectivity information, traffic light information, or intersection information. In this implementation, the correctness or completeness evaluation of the map data can be performed to allow users to promptly determine whether the map data is correct or complete.

[0015] In one possible implementation, the aforementioned method further includes: modifying the map data using the comparison results to obtain modified map data. In the implementation of the present application, the map data can also be modified based on the comparison results to improve the accuracy or completeness of the map data, thereby improving the usability of the map.

[0016] In a possible implementation, the aforementioned acquisition of perception data includes: acquiring data collected by sensors in the vehicle; and performing desensitization processing on the data collected by the sensors in the vehicle to obtain perception data.

[0017] In the embodiments of the present application, the data collected by the sensor may be desensitized to improve the usability of the perception data.

[0018] In a second aspect, the present application provides a data comparison device, comprising:

[0019] A map acquisition module is used to acquire map data, including map data used during vehicle driving;

[0020] A perception module is used to obtain perception data, which includes data collected by sensors in the vehicle during the vehicle's driving process;

[0021] A comparison module is used to compare the map data with the perception data to obtain a comparison result, which is used to indicate whether there is a difference between the map data and the perception data;

[0022] The prompt module is used to generate prompt information based on the comparison results, and the prompt information is used to prompt the differences between the map data and the perception data.

[0023] Among them, the effects achieved by the second aspect and any optional implementation of the second aspect can be found in the corresponding description of the aforementioned first aspect or any optional implementation of the first aspect, and will not be repeated here.

[0024] In a possible implementation, the aforementioned comparison module is specifically used to: obtain information of at least one perception element included in the perception data; obtain information of at least one map element included in the map data; and compare the information of at least one perception element with the information of at least one map element to obtain a comparison result.

[0025] In a possible implementation, the aforementioned perception data packet includes one or more of the following: the number of lanes, the lane type, the turn corresponding to the lane, whether there is a waiting area in the lane, whether there is a stop line in the lane, the number of traffic lights, and the turn corresponding to the traffic light.

[0026] In a possible implementation, the aforementioned map data includes map data constructed based on data collected by sensors of at least one vehicle during a historical period.

[0027] In a possible implementation, the aforementioned prompt module is specifically used to: obtain navigation data; and when the comparison result includes a difference between the map data and the perception data, superimpose annotation data on the area in the navigation data where the difference exists between the map data and the perception data to obtain prompt information.

[0028] In a possible implementation, the aforementioned comparison module is further configured to evaluate the map data based on the comparison result to obtain an evaluation value corresponding to the map data, where the evaluation value includes a confidence level of lane information in the map data;

[0029] The prompt module is specifically used to superimpose the evaluation value in the navigation path to obtain prompt information.

[0030] In a possible implementation, the aforementioned comparison module is specifically used to evaluate the correctness or completeness of at least one of the following items in the map data based on the comparison results to obtain an evaluation value, and the at least one item includes: at least one of lane information, road connectivity information, traffic light information or intersection information.

[0031] In a possible implementation, the aforementioned device further includes: a correction module, configured to correct the map data using the comparison result to obtain corrected map data.

[0032] In a possible implementation, the aforementioned perception module is specifically used to: obtain data collected by sensors in the vehicle; and perform desensitization processing on the data collected by the sensors in the vehicle to obtain perception data.

[0033] In a third aspect, embodiments of the present application provide a data comparison device comprising: a processor and a memory, wherein the processor and the memory are interconnected via a circuit, and the processor invokes program code in the memory to execute the processing-related functions of the data comparison method described in any of the first aspects above. Optionally, the data comparison device may be a chip.

[0034] In a fourth aspect, an embodiment of the present application provides an autonomous driving vehicle, which includes a processor and a memory, wherein the processor and the memory are interconnected through a line, and the processor calls the program code in the memory to execute the processing-related functions in the data comparison method shown in any one of the first aspects above.

[0035] In the fifth aspect, an embodiment of the present application provides a digital processing chip or chip, the chip includes a processing unit and a communication interface, the processing unit obtains program instructions through the communication interface, the program instructions are executed by the processing unit, and the processing unit is used to perform processing-related functions in any optional implementation of the first to third aspects above.

[0036] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions, which, when executed on a computer, enables the computer to execute the method in the above-mentioned first aspect or any optional embodiment of the first aspect.

[0037] In a seventh aspect, an embodiment of the present application provides a computer program product comprising a computer program / instructions, which, when executed by a processor, enables the processor to execute the method in the above-mentioned first aspect or any optional implementation of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A schematic diagram of the architecture of a vehicle provided in this application;

[0039] Figure 2 A schematic diagram of the system architecture provided for this application;

[0040] Figure 3 A flow chart of a data comparison method provided in this application;

[0041] Figure 4 A flow chart of another data comparison method provided in this application;

[0042] Figure 5 A flow chart of another data comparison method provided in this application;

[0043] Figure 6 A flow chart of another data comparison method provided in this application;

[0044] Figure 7A schematic diagram of an application scenario provided for this application;

[0045] Figure 8 A schematic diagram of another application scenario provided for this application;

[0046] Figure 9 A schematic structural diagram of a data comparison device provided in this application;

[0047] Figure 10 This is a schematic structural diagram of another data comparison device provided in this application. DETAILED DESCRIPTION

[0048] The following will describe the technical solutions in the embodiments of this application in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0049] The method and device provided by this application are introduced below.

[0050] First of all, the method provided in this application can be applied to vehicles. The vehicle structure provided in this application is introduced below.

[0051] See Figure 1 , Figure 1 This is a schematic diagram of the structure of a vehicle provided in an embodiment of the present application. Vehicle 100 can be configured for autonomous driving mode. For example, vehicle 100 can control itself while in autonomous driving mode, and can also determine the current state of the vehicle and its surroundings through human operation, determine whether there are obstacles in the surrounding environment, and control vehicle 100 based on the obstacle information. While vehicle 100 is in autonomous driving mode, vehicle 100 can also be set to operate without human interaction.

[0052] Please see first Figure 1 , Figure 1 A schematic structural diagram of a vehicle provided in an embodiment of the present application is shown. Figure 1 This is a functional block diagram of a vehicle 100 provided in an embodiment of the present application. Vehicle 100 can be configured for either fully or partially autonomous driving. For example, vehicle 100 can obtain environmental information about its surroundings through perception system 120 and, based on analysis of the environmental information, derive an autonomous driving strategy to achieve fully autonomous driving, or present the analysis results to a user to achieve partially autonomous driving.

[0053] Vehicle 100 may include various subsystems, such as an infotainment system 110, a perception system 120, a decision control system 130, a drive system 140, and a computing platform 150. Alternatively, vehicle 100 may include more or fewer subsystems, and each subsystem may include multiple components. Furthermore, each subsystem and component of vehicle 100 may be interconnected via wired or wireless means.

[0054] In some embodiments, the infotainment system 110 may include a communication system 111 , an entertainment system 112 , and a navigation system 113 .

[0055] The communication system 111 may include a wireless communication system 111 that can communicate wirelessly with one or more devices directly or via a communication network. For example, the wireless communication system 111 may use 3G cellular communication, such as CDMA, EVDO, GSM / GPRS, or 4G cellular communication, such as LTE, or 5G cellular communication. The wireless communication system 111 may communicate with a wireless local area network (WLAN) using WiFi. In some embodiments, the wireless communication system 146 may communicate directly with the device using an infrared link, Bluetooth, or ZigBee. The wireless communication system 111 may include one or more dedicated short range communications (DSRC) devices that may include public and / or private data communications between vehicles and / or roadside stations.

[0056] The entertainment system 112 may include a central control screen, a microphone, and speakers. Users can use the entertainment system 112 to listen to the radio and play music in the vehicle. Alternatively, they can connect their mobile phone to the vehicle and project their phone's screen onto the central control screen, which may be touch-sensitive and user-operated. In some cases, the microphone can capture the user's voice signal and, based on analysis of the voice signal, enable the user to control certain aspects of the vehicle 100, such as adjusting the vehicle's temperature. In other cases, the speakers can play music to the user.

[0057] The navigation system 113 may include a map service to provide navigation for the vehicle 100. The navigation system 113 may be used in conjunction with the vehicle's global positioning system 121 and inertial measurement unit 122. The map may be a two-dimensional map, a high-precision map, or a map constructed based on data collected during vehicle travel.

[0058] The perception system 120 may include several types of sensors that sense information about the environment surrounding the vehicle 100. For example, the perception system 120 may include a global positioning system 121 (the global positioning system may be a GPS system, or a BeiDou system or other positioning systems), an inertial measurement unit (IMU) 122, a lidar 123, a millimeter-wave radar 124, an ultrasonic radar 125, and a camera 126. The perception system 120 may also include sensors of the internal systems of the monitored vehicle 100 (for example, an in-vehicle air quality monitor, a fuel gauge, an oil temperature gauge, etc.). Sensor data from one or more of these sensors can be used to detect objects and their corresponding characteristics (position, shape, direction, speed, etc.). Such detection and identification are key functions for the safe operation of the vehicle 100. The perception data collected by the sensors in the vehicle mentioned below in this application may include information collected by the various units in the perception system 120.

[0059] The global positioning system 121 may be used to determine the geographic location of the vehicle 100 .

[0060] The inertial measurement unit 122 is used to sense changes in position and orientation of the vehicle 100 based on inertial acceleration. In some embodiments, the inertial measurement unit 122 may be a combination of an accelerometer and a gyroscope.

[0061] LiDAR 123 may utilize laser light to sense objects in the environment in which vehicle 100 is located. In some embodiments, LiDAR 123 may include one or more laser sources, a laser scanner, and one or more detectors, among other system components.

[0062] The millimeter wave radar 124 can use radio signals to sense objects in the surrounding environment of the vehicle 100. In some embodiments, in addition to sensing objects, the millimeter wave radar 124 can also be used to sense the speed and / or heading of the objects.

[0063] The ultrasonic radar 125 may sense objects around the vehicle 100 using ultrasonic signals.

[0064] The camera device 126 may be used to capture image information of the surrounding environment of the vehicle 100. The camera device 126 may include a monocular camera, a binocular camera, a structured light camera, a panoramic camera, etc. The image information acquired by the camera device 126 may include static image information or video stream information.

[0065] The decision control system 130 includes a computing system 131 that analyzes and makes decisions based on the information obtained by the perception system 120. The decision control system 130 also includes a vehicle controller 132 that controls the power system of the vehicle 100, as well as a steering system 133, throttle 134 and braking system 135 for controlling the vehicle 100.

[0066] The computing system 131 can process and analyze the various information obtained by the perception system 120 in order to identify targets, objects and / or features in the environment surrounding the vehicle 100. The targets may include pedestrians or animals, and the objects and / or features may include traffic signals, road boundaries and obstacles. The computing system 131 may use object recognition algorithms, Structure from Motion (SFM) algorithms, video tracking and other technologies. In some embodiments, the computing system 131 can be used to map the environment, track objects, estimate the speed of objects, and so on. The computing system 131 can analyze the various information obtained and derive a control strategy for the vehicle.

[0067] The vehicle controller 132 can be used to coordinate and control the vehicle's power battery and engine 141 to improve the power performance of the vehicle 100.

[0068] The steering system 133 may be used to adjust the forward direction of the vehicle 100. For example, in one embodiment, the steering system 133 may be a steering wheel system.

[0069] The throttle 134 is used to control the operating speed of the engine 141 and thus the speed of the vehicle 100 .

[0070] Braking system 135 is used to control the deceleration of vehicle 100. Braking system 135 can use friction to slow the rotation of wheels 144. In some embodiments, braking system 135 can convert the kinetic energy of wheels 144 into electric current. Braking system 135 can also take other forms to slow the rotation of wheels 144 and thus control the speed of vehicle 100.

[0071] Drive system 140 includes components that provide power to vehicle 100. In one embodiment, drive system 140 may include engine 141, energy source 142, transmission system 143, and wheels 144. Engine 141 may be an internal combustion engine, an electric motor, an air compression engine, or another combination of engines, such as a hybrid engine consisting of a gasoline engine and an electric motor, or a hybrid engine consisting of an internal combustion engine and an air compression engine. Engine 141 converts energy source 142 into mechanical energy.

[0072] Examples of energy source 142 include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and other sources of electricity. Energy source 142 may also provide energy to other systems of vehicle 100.

[0073] The transmission system 143 can transmit mechanical power from the engine 141 to the wheels 144. The transmission system 143 may include a gearbox, a differential, and a drive shaft. In one embodiment, the transmission system 143 may also include other components, such as a clutch. The drive shaft may include one or more shafts that can be coupled to one or more wheels 144.

[0074] Some or all functions of the vehicle 100 are controlled by a computing platform 150. The computing platform 150 may include at least one processor 151 that can execute instructions 153 stored in a non-transitory computer-readable medium such as a memory 152. In some embodiments, the computing platform 150 may also be a plurality of computing devices that control individual components or subsystems of the vehicle 100 in a distributed manner.

[0075] The processor 151 may be any conventional processor, such as a commercially available CPU. Alternatively, the processor 151 may include a graphics processing unit (GPU), a field programmable gate array (FPGA), a system on chip (SOC), an application-specific integrated circuit (ASIC), or a combination thereof. The processor 151 may be located on a device remote from the vehicle and communicate wirelessly with the vehicle.

[0076] In some embodiments, memory 152 may include instructions 153 (e.g., program logic) that are executable by processor 151 to perform various functions of vehicle 100. Memory 152 may also include additional instructions, including instructions for sending data to, receiving data from, interacting with, and / or controlling one or more of infotainment system 110, perception system 120, decision control system 130, and drive system 140.

[0077] In addition to instructions 153, memory 152 may also store data such as road maps, route information, the vehicle's location, direction, speed, and other similar vehicle data, as well as other information. This information may be used by vehicle 100 and computing platform 150 during operation of vehicle 100 in autonomous, semi-autonomous, and / or manual modes.

[0078] The computing platform 150 may control functions of the vehicle 100 based on input received from various subsystems, such as the drive system 140, the perception system 120, and the decision control system 130. For example, the computing platform 150 may utilize input from the decision control system 130 to control the steering system 133 to avoid an obstacle detected by the perception system 120. In some embodiments, the computing platform 150 may be operable to provide control over many aspects of the vehicle 100 and its subsystems.

[0079] Alternatively, one or more of the above components may be installed or associated separately from the vehicle 100. For example, the memory 152 may be partially or completely separate from the vehicle 100. The above components may be communicatively coupled together in a wired and / or wireless manner.

[0080] Optionally, the above components are just an example. In actual applications, the components in the above modules may be added or deleted according to actual needs. Figure 1 It should not be understood as limiting the embodiments of the present application.

[0081] The above-mentioned vehicle 100 can be a car, truck, motorcycle, bus, ship, airplane, helicopter, entertainment vehicle, amusement park vehicle, construction equipment, tram, golf cart or train, etc., which can realize intelligent driving or a vehicle-mounted terminal, etc., and the embodiments of the present application do not make special limitations.

[0082] In combination with the aforementioned vehicle structure, the method provided in this application can be applied to scenarios where a map is used for vehicle navigation. The map can be derived from a map set inside the vehicle or from a cloud server. The map can be a manually drawn high-precision map, or a map constructed based on environmental information collected by sensors during vehicle driving, or a self-decoded map (RC) or self-built map, or a map provided by a navigation service provider.

[0083] The following is an exemplary introduction using the method provided in this application as an example of using a self-composed image scene.

[0084] For example, the system provided by this application can be Figure 2 As shown, the system may include a cloud platform 21 and vehicles 22, wherein the number of vehicles 22 may include one or more.

[0085] The structure of the vehicle 22 can refer to the aforementioned Figure 1 During the driving process of the vehicle 22, sensors provided in the vehicle can be used to collect environmental information of the vehicle, such as point cloud data or environmental images of the vehicle's environment, and upload them to the cloud platform 21.

[0086] The cloud platform 21 can be used to extract entity elements from environmental information uploaded by each vehicle 22 to build a map, thereby achieving map construction at a lower cost and obtaining a self-constructed map. The self-constructed map is then sent to the vehicle 22, allowing the vehicle 22 to navigate based on the self-constructed map.

[0087] However, due to the complexity of road conditions, the maps used for vehicle navigation may differ from the actual environment. Therefore, a map evaluation is necessary. However, map evaluation has a lag. If the map is not updated promptly during the vehicle's current driving process, there may be scenarios where the vehicle map differs from the actual environment, potentially affecting the vehicle's driving process.

[0088] Therefore, in order to improve the driving safety of the vehicle, the present application provides a data comparison method, which can be used to compare the map data used for vehicle navigation with the environmental perception data actually collected by the vehicle, so as to identify the differences between the map and the actual environment in which the vehicle is located, and provide targeted prompts, so that the user can pay attention to the areas where there are differences between the map and the actual environment in a timely manner, and thus determine whether to change the vehicle's driving plan to improve the vehicle's driving safety.

[0089] The method provided in this application can be executed by a vehicle or an onboard terminal, or by a cloud platform. If the method provided in this application is executed by a vehicle, the vehicle can directly compare the collected perception data with the map data issued by the cloud platform. If the method provided in this application is executed by a cloud platform, after the vehicle collects the perception data, it can upload the perception data to the cloud platform, and the cloud platform will execute the following method steps.

[0090] See Figure 3 , a flow chart of a data comparison method provided in this application is described as follows.

[0091] 301. Obtain map data.

[0092] The map data includes map data used by the vehicle during driving. The map data may include pre-stored data in the vehicle or map data received from a cloud platform. The map data may be used for vehicle navigation, as a route reference for the user while driving the vehicle, or for display on a user's display interface.

[0093] For example, combining the above Figure 2In the architecture shown, the map data stored in the vehicle can come from a cloud platform. The cloud platform can construct a self-constructed map based on road information collected by multiple vehicles, and send the self-constructed map to the vehicle connected to the cloud platform, so that the vehicle can use the self-constructed map sent by the cloud platform for subsequent navigation or map display in intelligent driving and other functions.

[0094] In one scenario, multiple vehicles can be connected to the cloud platform. During the driving process of these multiple vehicles, the collected perception information can be uploaded to the cloud platform. The cloud platform can extract map elements based on the data uploaded by multiple vehicles to build a map, thereby obtaining a self-composed map at a lower cost. The map data mentioned in this application can specifically be the self-composed map sent by the cloud platform to the vehicle. For example, the cloud platform can be connected to a large number of vehicles. When the vehicle is driving in various areas, with the authorization of the user, the vehicle can upload the data collected by the sensor to the cloud platform. The cloud platform builds a map of the area where the vehicle has traveled based on the data uploaded by each vehicle, obtains a self-composed map and sends it to the vehicle. Then, when the vehicle drives to an area where the self-composed map has been constructed, the self-composed map can be used for navigation or other intelligent driving operations, thereby achieving map construction at a lower cost, reducing the map cost of each vehicle, and making full use of the vehicle resources connected to the cloud platform.

[0095] In some scenarios, the intelligent driving solution based on self-map construction can also be called a map-free driving solution, that is, intelligent driving is performed without constructing a high-precision map.

[0096] 302. Acquire perception data.

[0097] The sensor data may include data collected by the sensors in the vehicle during the vehicle's driving process. Figure 1 The sensors in the perception system will not be described in detail here.

[0098] The perception data may specifically include point cloud data or images, which can be used to represent the vehicle's environment. For example, the perception data may specifically include, but is not limited to, one or more of the following: the number of lanes, lane types, corresponding turns in the lanes, whether there is a waiting area in the lane, whether there is a stop line in the lane, the number of traffic lights, corresponding turns in the traffic lights, and other information.

[0099] For example, a laser radar or a millimeter-wave radar can be installed in the vehicle to collect the actual environment of the vehicle while the vehicle is driving, and obtain point cloud data including the environment in which the vehicle is located.

[0100] For another example, an image sensor may be provided in the vehicle, and images of the environment in which the vehicle is located may be captured while the vehicle is traveling, thereby capturing images including the environment in which the vehicle is located.

[0101] In one possible approach, the specific method for obtaining the perception data may include: obtaining data collected by sensors in the vehicle; and performing desensitization processing on the data collected by the sensors in the vehicle to obtain the perception data. Therefore, in the embodiments of the present application, the data collected by the sensors may be desensitized to improve the security of the collected data.

[0102] It should be noted that this application does not limit the execution order of step 301 and step 302. Step 301 can be executed first, step 302 can be executed first, or step 301 and step 302 can be executed simultaneously. The specific order can be adjusted according to the actual application scenario.

[0103] 303. Compare the map data with the perception data to obtain a comparison result.

[0104] After obtaining the map data and the perception data, the map data and the perception data can be compared to identify whether there is a difference between the map data and the perception data, and obtain a comparison result, that is, the comparison result is used to indicate whether there is a difference between the map data and the perception data.

[0105] Specifically, information about one or more elements can be extracted from the perception data. For ease of distinction, the elements extracted from the perception data are referred to as perception elements. Information about one or more elements can be read or extracted from the map data. For ease of distinction, the elements retrieved from the map data are referred to as map elements. Subsequently, the information about the perception elements is compared with the information about the corresponding map elements to obtain a comparison result. The comparison step can specifically include comparing the position, shape, or size of the perception elements with the map elements, thereby identifying differences between the map data and the perception data and obtaining a comparison result.

[0106] 304. Generate prompt information based on the comparison result, where the prompt information is used to indicate the difference between the map data and the perception data.

[0107] After obtaining the comparison results, prompt information can be generated based on the comparison results to prompt the differences between the map data and the perception data, so as to improve the user's driving safety.

[0108] Specifically, the prompt information can be displayed in a display interface, or can be broadcasted by voice, etc. The playback mode of the prompt information can be preset or determined by the user.

[0109] Therefore, in the implementation mode of the present application, the environmental perception data collected by the vehicle in real time can be compared with the map data, and the differences between the actual environmental perception data and the map data can be identified, so as to promptly prompt the areas where there are differences between the map and the actual environment, so that the user can adjust the vehicle's driving plan in time based on the difference to improve the vehicle's driving safety.

[0110] In one possible implementation, for a vehicle navigation scenario, navigation data may be obtained; and when a comparison result includes a difference between map data and perception data, annotation data may be superimposed on an area in the navigation data where the difference exists between the map data and the perception data to obtain prompt information.

[0111] In one possible implementation, the map data can be evaluated based on the comparison results to obtain an evaluation value corresponding to the map data, including the confidence level of the lane information in the map data. This evaluation value can be overlaid on the navigation path to provide prompt information. Therefore, in this implementation, the evaluation value can be overlaid on the navigation path to allow the user to obtain the confidence level of the map information corresponding to the navigation path. The user can then refer to this confidence level to more accurately determine the vehicle's driving plan, thereby improving driving safety.

[0112] Specifically, based on the comparison results, the correctness or completeness of at least one of the following items in the map data can be evaluated to obtain an evaluation value. The at least one item includes: lane information, road connectivity information, traffic light information, or intersection information. Correctness refers to whether the information of each map element in the map data is correct, or whether it matches the elements in the actual scene, etc. Completeness refers to whether the information included in the map data is complete. Therefore, in the implementation of the present application, the map data can be evaluated from dimensions such as correctness or completeness to identify whether the data in the map data is complete or correct, so that more accurate road prompts can be provided to the user in the future.

[0113] In one possible implementation, the map data can be modified based on the comparison results to obtain modified map data. For example, if the comparison determines that the number of lanes or lane types between the perception data and the map data differ, the number of lanes or lane types in the map data can be modified to improve the accuracy of the map data. For example, in a self-map scenario, after identifying the differences between the perception data and the self-map, these differences can be used to update the self-map, thereby improving the accuracy of the self-map. This allows the next vehicle using the self-map to perform navigation or other intelligent driving operations based on the more accurate self-map, thereby improving vehicle driving safety.

[0114] In addition, in some scenarios, if the vehicle is in autonomous driving mode and there is a discrepancy between the map data and the perception data, the vehicle's driving decisions can be adjusted. For example, if the map data indicates a steering error corresponding to a traffic light, the vehicle can be adjusted to change lanes or stop, etc., to improve driving safety. For example, in an autonomous driving scenario, if there is a discrepancy between the self-composed map and the perception data, an alarm can be displayed on the vehicle's display screen to remind the user to take over the vehicle, or the vehicle can be directly braked or changed lanes, etc., to improve driving safety.

[0115] The above is an introduction to the method flow provided by this application. The following is an illustrative introduction to the method flow provided by this application in combination with specific application scenarios.

[0116] For example, taking the navigation scenario as an example, the architecture corresponding to the method provided by this application can be as follows: Figure 4 shown.

[0117] First, the method provided in this application can be applied to Figure 4 In the data comparison module shown in the figure, the source of map data can come from the cloud; the perception data can be collected by the vehicle's perception system; the navigation path is generated by the server corresponding to the navigation application (application, APP) deployed in the vehicle; the data comparison module generates a comparison result between the map data and the perception data through the method provided by this application; the comparison result can be displayed in the alarm prompt module, for example, it can be specifically displayed in the automatic adjustment and continuous damping control system (continuous damping control, CDC) display interface or the human-machine interface (human machine interface, HMI).

[0118] Based on this, the method steps provided in this application are exemplarily introduced below in combination with application scenarios.

[0119] See Figure 5 , a flow chart of another data comparison method provided in this application is described as follows.

[0120] 501. Obtaining a true value: When the vehicle is driving on the road, obtain a road surface desensitized true value.

[0121] As a vehicle drives on the road, sensors installed in the vehicle collect sensory data about its environment. Optionally, desensitization processing can be performed, such as removing or masking facial features or other features of pedestrians in the sensory data, to filter out sensitive data and obtain usable sensory data.

[0122] 502. Annotating ground truth: identifying and extracting perceptual elements.

[0123] Extracting perception elements from the perception data. The perception elements may include information about one or more entities included in the perception data. The entities may include buildings, lanes, traffic signs, or other obstacles in the environment. For example, the perception elements may include information about lanes, traffic lights, or other buildings in the vehicle's environment.

[0124] After obtaining the perception data, the data that needs to be compared with the map data can be annotated. This data is called the ground truth. For example, the number of lanes, lane turns, lane type, the presence of a waiting area / waiting area, the presence of a stop line, the number of traffic lights, or the direction of traffic lights can be annotated.

[0125] 503. True value comparison: Compare the true value with the self-composed image to identify the differences in the self-composed image.

[0126] After obtaining the self-composed map and perception data, the labeled perception elements can be compared with the map elements in the self-composed map to identify differences between the perception elements and the map elements in the self-composed map, such as different positions, sizes, or quantities of perception elements and map elements, and record the differences that may affect vehicle safety or user experience.

[0127] Map elements can be obtained by recognizing map data or directly reading from the map data. Map elements can include information about entities included in the map data. Similar to the aforementioned perception elements, these entities can include buildings, lanes, traffic signs, or other obstacles in the environment.

[0128] Specifically, a neural network can be used to compare the true value with the self-constructed image, or a similarity algorithm or a comparison algorithm can be directly used to calculate the similarity between the perception elements and the map elements, so as to identify the difference between the self-constructed image and the true value.

[0129] 504. HMI display: display difference visualization results.

[0130] Based on the difference between the self-composed image and the perception data identified in step 503, a corresponding visualization result is generated, so that the difference is superimposed and displayed in the navigation path.

[0131] For example, the problem display in the navigation path can be shown as shown in Table 1.

[0132]

[0133] Table 1

[0134] The display interface can display information such as the vehicle's behavior, the vehicle's coordinates, the desensitized perception element ID, the evaluation classification of the difference between the perception element and the map element, the problem description corresponding to the difference, and the severity of the difference.

[0135] Therefore, in the embodiments of the present application, the difference in the navigation path can be displayed on the display interface, preserving the navigation direction, coordinate position, etc. for visualization. The user can observe the difference between the self-composed image and the perceived environment on the display interface, thereby promptly determining whether to adjust the vehicle's driving path, driving style, etc. to improve vehicle safety.

[0136] See Figure 6 , a flow chart of another data comparison method provided in this application is described as follows.

[0137] 601. Obtaining a true value: When the vehicle is driving on the road, obtain a road surface desensitized true value.

[0138] 602. Annotating ground truth: identifying and extracting perceptual elements.

[0139] Among them, step 601 and step 602 can refer to the aforementioned step 501 or step 502, and will not be repeated here.

[0140] 603. Perform truth value comparison on the cloud.

[0141] Among them, the true value comparison step can be performed by the cloud, and the risk assessment of the self-constructed map can be performed based on the difference between the true value and the self-constructed map, so as to obtain the problems of each map element and the corresponding confidence level.

[0142] For example, the location, lane information, and traffic light information corresponding to ramps, cruise sections, and intersections on a route can be extracted to assess cartographic accuracy and completeness. Location refers to the coordinates corresponding to the true value; lane information includes the number of lanes, lane turns, lane type, turn zones, and stop lines; and traffic light information includes the number of traffic lights, traffic light type, and traffic light turns.

[0143] For example, the correctness and completeness of the self-composed graph can be evaluated, such as Figure 7As shown, the correctness assessment may specifically include, but is not limited to, lane issues, connectivity issues, traffic light issues, or intersection issues. Lane issues may specifically include lane redundancy, lane-corresponding steering errors, or lane type errors. Connectivity issues may specifically include incorrect connection to subsequent roads. Traffic light issues may include traffic light-corresponding steering errors or traffic light association errors. Intersection issues may include incorrectly created intersection surfaces. Integrity issues may include lane issues, connectivity issues, or intersection issues. Lane issues may specifically include missing lanes or broken lanes. Connectivity issues may include lack of subsequent road connectivity. Intersection issues may include uncreated intersections.

[0144] 604. Obtain the coordinates of the difference between the true value and the self-composed image, and mark them as difference points.

[0145] After risk assessment of the self-constructed image, the coordinates of the difference between the true value and the self-constructed image are obtained and marked as difference points.

[0146] For example, if the number of lanes in a certain area in the perception data is different from that in the self-composed map, the area where the lanes corresponding to the lane number are located is regarded as the difference point; if the lane type in a certain area in the perception data is different, the area where the lane type is located can be marked as the difference point.

[0147] 605. Match the location of the difference point with the navigation path.

[0148] The location of the difference point is matched with the navigation path to determine its location within the navigation path. For example, if the number of lanes in a certain section of the navigation path determined based on the self-constructed map differs from the number of perceived vehicles, the area where these lanes are located can be determined.

[0149] 606. Display the difference points in the navigation path in real time.

[0150] The difference points and corresponding confidence values ​​can then be superimposed and displayed in real time on the navigation path displayed in the display interface.

[0151] For example, Figure 8 As shown, the incomplete composition area in the self-composition can be marked, such as Figure 8 Points 2, 4, and 5 in the image are used to indicate that the vehicle's intelligent driving function is unavailable. They can also mark the difference between the self-composed image and the real road conditions, such as Figure 8Points 1, 3, and 6 in the image should be noted. It is also possible to mark intersections where the self-composed image and the actual road conditions have a high degree of match. For example, sections with a high degree of match can be marked as passable sections, and the intelligent driving function can continue to be used. For example, they can be distinguished by color, marking the aforementioned points 2, 4, and 5 as purple, and points 1, 3, and 6 as red, or marking the passable sections as green, so that users can navigate based on the navigation path displayed on the display.

[0152] Therefore, in the embodiments of the present application, the correctness and completeness of the self-constructed map can be evaluated based on the perception data, and the evaluation results can be superimposed and displayed on the navigation path in the display interface, so that the user can intuitively observe the difference between the self-constructed map and the actual road conditions in the display interface, and thus adjust the vehicle's driving plan when the difference is large to improve the vehicle's driving safety.

[0153] The above describes the method steps provided in this application. The following describes the device structure for executing the method steps of this application.

[0154] See Figure 9 , a schematic structural diagram of a data comparison device provided in this application, the data comparison device comprising:

[0155] A map acquisition module 901 is used to acquire map data, where the map data includes map data used during vehicle driving;

[0156] A perception module 902 is configured to obtain perception data, wherein the perception data includes data collected by sensors in the vehicle during driving;

[0157] A comparison module 903 is used to compare the map data with the perception data to obtain a comparison result, which is used to indicate whether there is a difference between the map data and the perception data;

[0158] The prompt module 904 is used to generate prompt information according to the comparison result, and the prompt information is used to prompt the difference between the map data and the perception data.

[0159] In a possible implementation, the aforementioned comparison module 903 is specifically used to: obtain information of at least one perception element included in the perception data; obtain information of at least one map element included in the map data; and compare the information of at least one perception element with the information of at least one map element to obtain a comparison result.

[0160] In a possible implementation, the aforementioned perception data packet includes one or more of the following: the number of lanes, the lane type, the turn corresponding to the lane, whether there is a waiting area in the lane, whether there is a stop line in the lane, the number of traffic lights, and the turn corresponding to the traffic light.

[0161] In a possible implementation, the aforementioned map data includes map data constructed based on data collected by sensors of at least one vehicle during a historical period.

[0162] In a possible implementation, the aforementioned prompt module 904 is specifically used to: obtain navigation data; and when the comparison result includes a difference between the map data and the perception data, superimpose annotation data on the area in the navigation data where the difference exists between the map data and the perception data to obtain prompt information.

[0163] In a possible implementation, the comparison module 903 is further configured to evaluate the map data based on the comparison result to obtain an evaluation value corresponding to the map data, where the evaluation value includes a confidence level of lane information in the map data.

[0164] The prompt module is specifically used to superimpose the evaluation value in the navigation path to obtain prompt information.

[0165] In one possible implementation, the aforementioned comparison module 903 is specifically used to evaluate the correctness or completeness of at least one of the following items in the map data based on the comparison results to obtain an evaluation value, and the at least one item includes: at least one of lane information, road connectivity information, traffic light information or intersection information.

[0166] In a possible implementation, the aforementioned apparatus further includes: a correction module 905, configured to correct the map data using the comparison result to obtain corrected map data.

[0167] In a possible implementation, the aforementioned perception module 902 is specifically configured to: obtain data collected by sensors in the vehicle; and perform desensitization processing on the data collected by the sensors in the vehicle to obtain perception data.

[0168] like Figure 10 FIG. 1 is a schematic diagram of the hardware structure of a data processing device 100 provided in an embodiment of the present application. The data processing device 100 can be used to implement the aforementioned Figures 3 to 8 The steps of the method.

[0169] Figure 10 The data processing device 100 shown may include: a processor 1001 , a memory 1002 , a communication interface 1003 , and a bus 1004 . The processor 1001 , the memory 1002 , and the communication interface 1003 may be connected via the bus 1004 .

[0170] The processor 1001 is the control center of the data processing device 100 and can be a general-purpose central processing unit (CPU) or other general-purpose processor. The general-purpose processor can be a microprocessor or any conventional processor, such as a GPU or NPU, and can be adaptively configured according to the actual application scenario.

[0171] As an example, the processor 1001 may include one or more CPUs, and may also include other processors, such as Figure 10 The CPU, NPU or GPU shown in .

[0172] The memory 1002 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0173] In one possible implementation, the memory 1002 may exist independently of the processor 1001. The memory 1002 may be connected to the processor 1001 via a bus 1004 and used to store data, instructions, or program codes. When the processor 1001 calls and executes the instructions or program codes stored in the memory 1002, the method provided in the embodiment of the present application can be implemented, for example, Figures 3 to 8 The method shown.

[0174] In another possible implementation, the memory 1002 may also be integrated with the processor 1001 .

[0175] Communication interface 1003 is used to connect data processing apparatus 100 to other devices via a communication network. The communication network may be Ethernet, a radio access network (RAN), a wireless local area network (WLAN), or the like. Communication interface 1003 may include a receiving unit for receiving data and a transmitting unit for transmitting data.

[0176] The bus 1004 may be an ISA bus, a PCI bus, or an EISA bus. The bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of presentation, Figure 10 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0177] It should be pointed out that Figure 10 The structure shown in the figure does not constitute a limitation on the data processing device 100. Figure 10 In addition to the components shown, the data processing apparatus 100 may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0178] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general-purpose hardware, and of course can also be implemented by dedicated hardware including application-specific integrated circuits, dedicated CPUs, dedicated memories, dedicated components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, USB flash drive, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., including a number of instructions for causing a device (which can be a personal computer, server, or network equipment, etc.) to execute the methods described in each embodiment of the present application.

[0179] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0180] In an embodiment of the present application, a computer-readable storage medium is also provided. The computer-readable storage medium stores a program for training a model or performing an inference task. When the program is run on a computer, the computer performs the above-mentioned Figures 3 to 8 All or part of the steps in the method described in the embodiment shown in FIG.

[0181] The present application also provides a digital processing chip. The digital processing chip integrates circuitry and one or more interfaces for implementing the aforementioned processor or processor functions. When the digital processing chip integrates memory, it can perform the method steps of any one or more of the aforementioned embodiments. When the digital processing chip does not integrate memory, it can be connected to an external memory via a communication interface. The digital processing chip implements the method steps of any one or more of the aforementioned embodiments based on program code stored in the external memory.

[0182] A computer program product is also provided in the embodiment of the present application, and the computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium, (such as a floppy disk, hard disk, tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid-state drive Solid State Disk (SSD)).

[0183] The data comparison device provided in the embodiment of the present application can be a chip, which includes: a processing unit and a communication unit. The processing unit can be, for example, a processor, and the communication unit can be, for example, an input / output interface, a pin or a circuit. The processing unit can execute the computer execution instructions stored in the storage unit to enable the chip in the server to execute the above Figure 3-Figure 8The method described in the embodiment shown. Optionally, the storage unit is a storage unit within the chip, such as a register, a cache, etc. The storage unit may also be a storage unit located outside the chip within the wireless access device, such as a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a random access memory (RAM), etc.

[0184] Specifically, the aforementioned processing unit or processor may be a central processing unit (CPU), a neural-network processing unit (NPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0185] It should also be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided in this application, the connection relationship between the modules indicates that there is a communication connection between them, which can be specifically implemented as one or more communication buses or signal lines.

[0186] Through the description of the above embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary general-purpose hardware, and of course can also be implemented by dedicated hardware including application-specific integrated circuits, dedicated CPUs, dedicated memories, dedicated components, etc. In general, all functions performed by computer programs can be easily implemented with corresponding hardware, and the specific hardware structures used to implement the same function can also be diverse, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, software program implementation is a better implementation method in most cases. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer's floppy disk, USB flash drive, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc., including a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0187] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0188] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, a computer, a server, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website, a computer, a server, or a data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium can be a magnetic medium, (e.g., a floppy disk, a hard disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0189] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. The term "and / or" in this application is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The naming or numbering of the steps in this application does not mean that the steps in the method flow must be executed in the time / logical sequence indicated by the naming or numbering. The named or numbered process steps can be changed in the execution order according to the technical purpose to be achieved, as long as the same or similar technical effects can be achieved. The division of modules in this application is a logical division. There may be other division methods when implementing in actual applications. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some ports, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in this application. In addition, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed in multiple circuit modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this application.

Claims

1. A data comparison method, characterized in that: include: Acquiring map data, wherein the map data includes data of a map used during vehicle driving; Acquiring sensory data, the sensory data including data collected by sensors in the vehicle during driving of the vehicle; Comparing the map data with the perception data to obtain a comparison result, wherein the comparison result is used to indicate whether there is a difference between the map data and the perception data; Prompt information is generated according to the comparison result, where the prompt information is used to prompt the difference between the map data and the perception data.

2. The method according to claim 1, characterized in that The comparing the map data with the perception data to obtain a comparison result includes: Acquiring information of at least one perception element included in the perception data; Acquiring information of at least one map element included in the map data; The information of the at least one perception element is compared with the information of the at least one map element to obtain the comparison result.

3. The method according to claim 2, characterized in that The sensing data packet includes one or more of the following: The number of lanes, lane types, turns corresponding to the lanes, whether there is a waiting area in the lane, whether there is a stop line in the lane, the number of traffic lights, and turns corresponding to the traffic lights.

4. The method according to any one of claims 1 to 3, characterized in that The map data includes map data constructed based on data collected by sensors of at least one vehicle during a historical period.

5. The method according to any one of claims 1 to 4, characterized in that Generating prompt information according to the comparison result includes: Get navigation data; In a case where the comparison result includes a difference between the map data and the perception data, annotation data is superimposed on an area in the navigation data where the difference exists between the map data and the perception data to obtain the prompt information.

6. The method according to any one of claims 1 to 5, characterized in that Generating prompt information according to the comparison result further includes: evaluating the map data according to the comparison result to obtain an evaluation value corresponding to the map data, the evaluation value including a confidence level of lane information in the map data; The evaluation value is superimposed on the navigation path to obtain the prompt information.

7. The method according to claim 6, characterized in that The step of evaluating the map data according to the comparison result to obtain an evaluation value corresponding to the map data includes: According to the comparison result, the correctness or completeness of at least one of the following items in the map data is evaluated to obtain the evaluation value, where the at least one item includes at least one of lane information, road connectivity information, traffic light information, or intersection information.

8. The method according to any one of claims 1 to 7, characterized in that The method further comprises: The map data is corrected using the comparison result to obtain corrected map data.

9. The method according to any one of claims 1 to 8, characterized in that The acquiring of the perception data includes: Acquiring data collected by sensors in the vehicle; Desensitizing the data collected by the sensors in the vehicle to obtain the perception data.

10. A data comparison device, characterized in that: include: A map acquisition module, configured to acquire map data, wherein the map data includes map data used during vehicle driving; a perception module, configured to acquire perception data, wherein the perception data includes data collected by sensors in the vehicle during driving of the vehicle; a comparison module, configured to compare the map data with the perception data to obtain a comparison result, wherein the comparison result is used to indicate whether there is a difference between the map data and the perception data; A prompt module is used to generate prompt information according to the comparison result, wherein the prompt information is used to prompt the difference between the map data and the perception data.

11. The device according to claim 10, characterized in that The comparison module is specifically used for: Acquiring information of at least one perception element included in the perception data; Acquiring information of at least one map element included in the map data; The information of the at least one perception element is compared with the information of the at least one map element to obtain the comparison result.

12. The device according to claim 11, characterized in that The sensing data packet includes one or more of the following: The number of lanes, lane types, turns corresponding to the lanes, whether there is a waiting area in the lane, whether there is a stop line in the lane, the number of traffic lights, and turns corresponding to the traffic lights.

13. The device according to claim 11 or 12, characterized in that The map data includes map data constructed based on data collected by sensors of at least one vehicle during a historical period.

14. The device according to any one of claims 10 to 13, characterized in that The prompt module is specifically used to: Get navigation data; In a case where the comparison result includes a difference between the map data and the perception data, annotation data is superimposed on an area in the navigation data where the difference exists between the map data and the perception data to obtain the prompt information.

15. The device according to any one of claims 10 to 14, characterized in that The comparison module is further configured to evaluate the map data based on the comparison result to obtain an evaluation value corresponding to the map data, wherein the evaluation value includes a confidence level of lane information in the map data; The prompt module is specifically configured to superimpose the evaluation value on the navigation path to obtain the prompt information.

16. The device according to claim 15, characterized in that According to the comparison results, The comparison module is specifically used to evaluate the correctness or completeness of at least one of the following items in the map data based on the comparison result to obtain the evaluation value, and the at least one item includes: at least one of lane information, road connectivity information, traffic light information or intersection information.

17. The device according to any one of claims 10 to 16, characterized in that The device further comprises: The correction module is used to correct the map data using the comparison result to obtain corrected map data.

18. The device according to any one of claims 10 to 17, characterized in that The perception module is specifically used to: Acquiring data collected by sensors in the vehicle; Desensitizing the data collected by the sensors in the vehicle to obtain the perception data.

19. A data comparison device, characterized in that: Comprising: It is characterized in that it comprises a processor, the processor is coupled to a memory, the memory stores a program, and when the program instructions stored in the memory are executed by the processor, the steps of the method according to any one of claims 1 to 9 are implemented.

20. A computer-readable storage medium comprising a program, which, when executed by a processing unit, performs the steps of the method according to any one of claims 1 to 9.

21. A computer program product, characterized in that The computer program product comprises software code for executing the steps of the method according to any one of claims 1 to 9.

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