Control method of mobile equipment, electronic equipment and storage medium
By evaluating and switching between offline maps and local maps, combined with sensor priority, the control accuracy problem of mobile devices in different scenarios is solved, efficient and accurate mobile device control is achieved, and the cost of map production and maintenance is reduced.
Patent Information
- Application Number
- CN202510888166.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-30
- Publication Date
- 2025-10-10
AI Technical Summary
How to accurately control the movement of mobile devices, especially in scenarios such as unmanned vehicles and logistics vehicles. Existing technologies find it difficult to provide efficient and accurate path planning and control in different control scenarios.
By evaluating the control scenario representation data of mobile devices, selecting offline maps or local maps as target maps, and using the collected data from sensors to establish local maps, combined with the priorities of visual sensors and radar sensors, maps can be flexibly switched to adapt to different control scenarios, reducing dependence on high-precision maps.
It improves the adaptability and control accuracy of mobile devices in different control scenarios, reduces the production and maintenance costs of high-precision maps, and improves the control efficiency and safety of mobile devices.
Smart Images
Figure CN120756519A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of artificial intelligence technology, and in particular to a control method for a mobile device, an electronic device, and a storage medium. Background Art
[0002] With the rapid development of artificial intelligence technology, there are more and more mobile devices on the road, such as driverless ride-hailing vehicles and driverless logistics vehicles. How to accurately control the movement of mobile devices has become a technical problem that needs to be solved urgently. Summary of the Invention
[0003] Embodiments of the present application provide a control method for a mobile device, an electronic device, and a storage medium, for improving the control accuracy of the mobile device.
[0004] In a first aspect, an embodiment of the present application provides a method for controlling a mobile device, including:
[0005] When a preset condition is met, evaluating the control effect of the offline map based on control scenario representation data obtained from the mobile device, wherein the control scenario representation data includes data collected by sensors on the mobile device;
[0006] Based on the control effect evaluation result of the offline map, selecting the offline map or a local map as the target map, wherein the local map is established based on the collected data of the sensor;
[0007] Based on the target map, the mobile device is controlled to move.
[0008] In some embodiments, evaluating the control effect of the offline map based on the control scenario representation data obtained from the mobile device includes:
[0009] Based on the local map, evaluating the error of the offline map;
[0010] Based on the error evaluation result, a control effect evaluation result of the offline map is determined.
[0011] In some embodiments, the control scenario representation data further includes the speed of the mobile device, and the sensor collected data includes the distances of obstacles around the mobile device;
[0012] Based on the control scenario representation data obtained from the mobile device, the control effect of the offline map is evaluated, including:
[0013] evaluating, based on the speed of the mobile device and the distance to the obstacle, a degree of loss of control when controlling the vehicle based on the offline map;
[0014] Based on the error evaluation result and the out-of-control degree evaluation result, a control effect evaluation result of the offline map is determined.
[0015] In some embodiments, evaluating the degree of loss of control when controlling based on the offline map based on the speed of the mobile device and the distance to the obstacle includes:
[0016] The result of the out-of-control degree assessment is determined according to the rule that speed is positively correlated with the out-of-control degree, and the distance to the obstacle is negatively correlated with the out-of-control degree.
[0017] In some embodiments, determining a control effect evaluation result of the offline map based on the error evaluation result and the out-of-control degree evaluation result includes:
[0018] According to the rule that the error of the offline map is negatively correlated with the control effect, and the degree of loss of control when using the offline map for control is negatively correlated with the control effect, the error evaluation result and the degree of loss of control evaluation result are fused to obtain the control effect evaluation result of the offline map.
[0019] In some embodiments, based on the control effect evaluation result of the offline map, selecting the offline map or the local map as the target map includes:
[0020] When the control effect of the offline map reaches a preset standard, selecting the offline map as the target map;
[0021] When the control effect of the offline map does not meet the preset standard, the local map is selected as the target map.
[0022] In some embodiments, if the offline map is selected as the target map, the method further includes:
[0023] When an abnormality of the offline map is detected or a preset emergency occurs on the moving route of the mobile device, the target map is switched to the latest established local map.
[0024] In some embodiments, when the offline map is detected to be abnormal, the method further includes:
[0025] When it is determined that the map anomaly type is not a preset anomaly type, recording the location of the mobile device;
[0026] If the number of times the location is recorded reaches a preset number within a preset time period, information indicating that there is an error in the location of the offline map is reported.
[0027] In some embodiments, the sensor includes a visual sensor and a radar sensor, and the local map is established according to the following steps:
[0028] Determining a target movement scene of the mobile device based on the acquired movement scene representation data of the mobile device, wherein the movement scene representation data includes movement road information and weather information;
[0029] Determining the priorities of the visual sensor and the radar sensor in the target movement scene according to pre-established priority information of the visual sensor and the radar sensor in different movement scenes;
[0030] The local map is established using the collected data of the visual sensor and the radar sensor according to the priorities of the visual sensor and the radar sensor in the target movement scene.
[0031] In a second aspect, an embodiment of the present application provides a control device for a mobile device, comprising:
[0032] An evaluation module, configured to evaluate a control effect of the offline map based on control scenario representation data acquired from a mobile device when a preset condition is met, wherein the control scenario representation data includes data collected by sensors on the mobile device;
[0033] A selection module, configured to select the offline map or a local map as a target map based on a control effect evaluation result of the offline map, wherein the local map is established based on the collected data of the sensor;
[0034] A control module is used to control the movement of the mobile device based on the target map.
[0035] In a third aspect, an embodiment of the present application provides an electronic device, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein:
[0036] The memory stores a computer program that can be executed by at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to execute any of the above-mentioned control methods for the mobile device.
[0037] In a fourth aspect, an embodiment of the present application provides a storage medium. When a computer program in the storage medium is executed by a processor of an electronic device, the electronic device can execute any of the above-mentioned control methods of the mobile device.
[0038] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements the control method of any of the above-mentioned mobile devices when executed by a processor.
[0039] In an embodiment of the present application, when preset conditions are met, the control effect of the offline map is evaluated based on the control scenario representation data obtained from the mobile device. The control scenario representation data includes data collected by sensors on the mobile device. Based on the control effect evaluation results, an offline map or a local map is selected as the target map, and then the movement of the mobile device is controlled based on the target map, wherein the local map is established based on the data collected by the sensors. In this way, a map that better matches the current control scenario of the mobile device can be selected, thereby improving the adaptability of the mobile device in different control scenarios, giving full play to the respective control advantages of offline maps and local maps, and more accurately controlling the movement of the mobile device. In addition, the flexible selection of local maps or offline maps can also reduce dependence on offline maps and reduce the production and maintenance costs of offline maps. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] 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:
[0041] Figure 1 A schematic diagram of a scenario provided in an embodiment of the present application;
[0042] Figure 2 A flowchart of a method for controlling a mobile device provided in an embodiment of the present application;
[0043] Figure 3 A flow chart of a method for evaluating the control effect of an offline map provided in an embodiment of the present application;
[0044] Figure 4 A flowchart of another method for evaluating the control effect of an offline map provided in an embodiment of the present application;
[0045] Figure 5 A control flow chart of an autonomous driving vehicle provided in an embodiment of the present application;
[0046] Figure 6 A schematic structural diagram of a control device for a mobile device provided in an embodiment of the present application;
[0047] Figure 7 A schematic diagram of the hardware structure of an electronic device for implementing a method for controlling a mobile device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0048] In order to improve the control accuracy of a mobile device, embodiments of the present application provide a control method for a mobile device, an electronic device, and a storage medium.
[0049] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application can be combined with each other if there is no conflict.
[0050] To facilitate understanding of this application, the technical terms involved in this application are:
[0051] 1. Offline maps refer to high-precision maps (map accuracy is higher than the preset accuracy), which are effective in controlling traffic in simple road conditions such as highways. However, the production and maintenance costs of high-precision maps are relatively high, and they are difficult to provide reliable route planning support in areas not covered by high-precision maps or when map data is not updated in a timely manner;
[0052] 2. Local maps refer to online maps created in real time based on data collected by sensors on mobile devices. They can eliminate the need for high-precision maps and provide relatively high accuracy in path planning in complex scenarios. However, they are highly dependent on sensors and require high hardware computing speeds.
[0053] The mobile device in the embodiments of the present application can be various self-driving vehicles, such as unmanned online taxis, unmanned logistics vehicles, unmanned food delivery vehicles, etc., and can also be a vehicle with automatic driving mode turned on.
[0054] The following introduces the scenario of the embodiment of the present application by taking the mobile device as an unmanned online taxi as an example.
[0055] See also Figure 1 , Figure 1 A scenario diagram provided for an embodiment of the present application includes multiple passenger terminals, a server, and multiple driverless online taxis, wherein each passenger terminal and the server can be wirelessly connected, and each driverless online taxi and the server can also be wirelessly connected.
[0056] The passenger side, such as mobile phones, iPads, computers, etc., can install ride-hailing software. Passengers can send ride requests to the server through the ride-hailing software. The ride requests can include ride information such as the pick-up location and delivery location.
[0057] Upon receiving a ride request from any passenger, the server can select a self-driving online ride-hailing vehicle based on the ride information in the ride request and send an order acceptance instruction to the selected self-driving online ride-hailing vehicle. The order acceptance instruction includes order acceptance information such as the order acceptance location and delivery location. Furthermore, based on the control scenario representation data of the self-driving online ride-hailing vehicle, a target map matching the control scenario of the self-driving online ride-hailing vehicle can be selected from offline maps and local maps. The self-driving online ride-hailing vehicle can then be controlled based on the target map, where the local map is established based on data collected by various sensors periodically transmitted by the self-driving online ride-hailing vehicle.
[0058] Autonomous ride-hailing vehicles can be equipped with a variety of sensors, such as lidar, millimeter-wave radar, and cameras, which periodically transmit data collected by these sensors to a server. Upon receiving an order acceptance instruction from the server, the vehicle executes the order acceptance and delivery process under the server's control based on the order information contained in the instruction.
[0059] After introducing the application scenarios of the embodiments of the present application, the control method of the mobile device proposed in the present application is described below with specific embodiments.
[0060] See also Figure 2 , Figure 2 This is a flowchart of a method for controlling a mobile device provided in an embodiment of the present application. All steps of the method can be Figure 1 Execute in the server, all steps can be Figure 1 The driverless online car-hailing service can also perform some steps in Figure 1 Executed in the server, some steps are Figure 1 The method may include the following steps.
[0061] In step 201, when a preset condition is met, the control effect of the offline map is evaluated based on the control scenario representation data obtained from the mobile device, where the control scenario representation data includes data collected by sensors on the mobile device.
[0062] Among them, the preset conditions include the start-up of the mobile device, the arrival of the set period, etc.
[0063] In practical applications, sensors on mobile devices collect data in real time, and local maps are updated in real time based on this data. Furthermore, sensors can include both visual and radar sensors, and the quality of data collected by visual and radar sensors varies in different mobile scenarios. For example, radar sensor data quality is superior to that of visual sensors in scenarios such as strong sunlight / backlight, rain, fog, and complex road conditions. However, visual sensor data quality is superior to that of radar sensors in scenarios such as sunny days and on ordinary roads.
[0064] In order to better build a local map, the movement scene of the mobile device can be identified, and then combined with the movement scene of the mobile device, the data collected by the visual sensor and the data collected by the radar sensor can be used to build a local map to improve the accuracy of the local map.
[0065] Specifically, mobile scene representation data of a mobile device, such as mobile road information and weather information, can be obtained. Based on the mobile scene representation data, the target mobile scene of the mobile device can be determined. For example, the mobile scene representation data can be input into a pre-established scene model to obtain the target mobile scene. Then, based on the pre-established priority information of the visual sensor and radar sensor in different mobile scenes, the priority of the visual sensor and radar sensor in the target mobile scene can be determined. Then, based on this priority, the data collected by the visual sensor and the data collected by the radar sensor can be used to establish a local map. For example, if the priority is high for the visual sensor and low for the radar sensor, the local map can be established with the data collected by the visual sensor as the primary and the data collected by the radar sensor as the supplementary; if the priority is high for the radar sensor and low for the visual sensor, the local map can be established with the data collected by the radar sensor as the primary and the data collected by the visual sensor as the supplementary.
[0066] In some embodiments, the control effect of the offline map can be evaluated with the help of the local map.
[0067] See also Figure 3 , Figure 3 A flow chart of a method for evaluating the control effect of an offline map provided in an embodiment of the present application includes the following steps.
[0068] In step 2011a, the error of the offline map is evaluated based on the local map.
[0069] For example, the local map and the offline map are aligned to find matching points between the two (i.e., points that represent the same location in both maps). N matching points are selected from these matching points. Based on the position coordinates of each matching point in the local map and the position coordinates in the offline map, the distance difference between the matching point in the two maps is calculated. The average of the distance differences between the N matching points in the two maps is determined as the error assessment result Dmatch of the offline map.
[0070] In step 2012a, based on the error evaluation result, a control effect evaluation result of the offline map is determined.
[0071] During specific implementation, the error evaluation result can be processed according to the rule that the error of the offline map is negatively correlated with the control effect, so as to obtain the control effect evaluation result of the offline map.
[0072] For example, the control effect evaluation result of the offline map is determined according to the following formula:
[0073]
[0074] Among them, Score is the control effect evaluation result of the offline map, Dmatch is the error evaluation result of the offline map, Th is the preset threshold, and β is a predetermined adjustment factor.
[0075] In some embodiments, the control scenario representation data may also include the speed of the mobile device, and the sensor data may include the distance to obstacles around the mobile device, such as the distance to the nearest obstacle. This information can be used to assess the degree of loss of control when controlling the mobile device using an offline map. Therefore, in some embodiments, the control effectiveness assessment results of the offline map can be determined by combining the loss of control assessment results with the error assessment results.
[0076] See also Figure 4 , Figure 4 A flowchart of another method for evaluating the control effect of an offline map provided in an embodiment of the present application includes the following steps.
[0077] In step 2011b, the accuracy of the offline map is evaluated based on the local map.
[0078] The implementation of this step can be referred to step 2011a, which will not be described in detail here.
[0079] In step 2012b, the degree of loss of control when using the offline map for control is evaluated based on the speed of the mobile device and the distance to the obstacle.
[0080] In specific implementation, the out-of-control degree assessment result when controlling based on the offline map can be determined according to the rule that the speed is positively correlated with the out-of-control degree, and the distance to the obstacle is negatively correlated with the out-of-control degree.
[0081] For example, the evaluation result of the degree of loss of control when controlling based on an offline map is determined according to the following formula:
[0082]
[0083] Among them, Rrisk is the evaluation result of the degree of loss of control, distance is the distance of the obstacle, and speed is the speed of the mobile device.
[0084] In step 2013b, based on the error evaluation result and the out-of-control degree evaluation result, the control effect evaluation result of the offline map is determined.
[0085] In specific implementation, the error evaluation results and the out-of-control degree evaluation results can be fused according to the rule that the error of the offline map is negatively correlated with the control effect, and the out-of-control degree when using the offline map for control is negatively correlated with the control effect, to obtain the control effect evaluation result of the offline map.
[0086] For example, the control effect evaluation result of the offline map is determined according to the following formula:
[0087]
[0088] Among them, Score is the control effect evaluation result of the offline map, Dmatch is the error evaluation result of the offline map, Th is the preset threshold, Rrisk is the evaluation result of the degree of out-of-control, and β and δ are predetermined adjustment factors.
[0089] In step 202, based on the control effect evaluation result of the offline map, the offline map or the local map is selected as the target map, and the local map is established based on the collected data of the sensor.
[0090] For example, when the control effect evaluation result indicates that the control effect of the offline map reaches the preset standard, the offline map is selected as the target map; when the control effect evaluation result indicates that the control effect of the offline map does not reach the preset standard, the local map is selected as the target map.
[0091] Taking the preset standard as a preset value as an example, when the Score is greater than the preset value, the offline map can be selected as the target map; when the Score is not greater than the preset value, the local map can be selected as the target map.
[0092] In step 203, the mobile device is controlled to move based on the target map.
[0093] Furthermore, when an offline map is selected as the target map, the mobile device can be controlled based on the target map. If an anomaly is detected in the offline map or a pre-set emergency occurs on the mobile device's route, the target map can be switched to the latest local map. Examples of such anomalies include missing or unresponsive offline maps, and pre-set emergencies include traffic accidents and unexpected traffic cuts.
[0094] Furthermore, if the map anomaly type is determined to be other than a pre-set anomaly type, such as missing maps (because this is a known issue with the map provider), the location of the mobile device may be recorded. If this location is recorded a pre-set number of times within a pre-set time period, a message indicating an offline map error at this location may be reported.
[0095] It should be noted that any map switch at this location on any mobile device will be recorded. Therefore, when the number of times this location is recorded reaches the preset number, it indicates that multiple mobile devices have discovered some problem with the offline map when passing through this location (a problem unknown to the map provider). Reporting that the offline map has an error at this location means feeding back the problem to the map provider, which will help the map provider improve the map and provide better map services.
[0096] In the embodiment of the present application, it is possible to flexibly switch between the offline map and the local map according to the control scenario of the mobile device, fully utilizing the respective advantages of the two, thereby controlling the movement of the mobile device more accurately and efficiently.
[0097] The following describes the solution of the embodiment of the present application in detail by taking the mobile device as an example in which the mobile device is an autonomous driving vehicle.
[0098] See also Figure 5 , Figure 5 A control flow chart of an autonomous driving vehicle provided in an embodiment of the present application includes the following steps.
[0099] In step 501, after the autonomous driving vehicle is started, control scenario representation data of the autonomous driving vehicle is periodically obtained.
[0100] Among them, the control scenario representation data may include data collected by sensors on the autonomous driving vehicle, such as lidar, millimeter-wave radar, cameras, etc., as well as the speed of the autonomous driving vehicle.
[0101] In specific implementation, Kalman filtering, extended Kalman filtering, or unscented Kalman filtering can be used to fuse the data collected by each sensor to reduce noise and generate unified environmental perception data. The environmental perception data is then processed through the simultaneous localization and mapping (SLAM) algorithm to build a local map in real time.
[0102] Generally, weights are assigned to various sensors during fusion, and these weights can be adjusted in real time based on weather conditions and sensor status. For example, on clear days, the camera's weight can be appropriately increased; on cloudy days, the weights for lidar and millimeter-wave radar can be appropriately increased. If a sensor is older, its weight can be appropriately reduced; if a sensor is newer, its weight can be appropriately increased. This improves the accuracy of the generated environmental perception data, and thus the precision of the local maps built based on it.
[0103] In step 502 , the control effect of the offline map is evaluated based on the control scenario representation data of the mobile device that has been recently acquired.
[0104] The details of this step can be found in step 201 and will not be described again here.
[0105] In step 503 , based on the control effect evaluation result, it is determined whether the control effect of the offline map meets the preset standard. If not, the process proceeds to step 504 ; otherwise, the process proceeds to step 506 .
[0106] In step 504, global path planning is performed based on the latest local map, and the autonomous driving vehicle is controlled based on the planning results.
[0107] In step 505 , wait for a first time period, and then proceed to step 502 .
[0108] The first duration is a predetermined duration, such as 1 minute, 30 seconds, etc.
[0109] That is, the control effect of the offline map is re-evaluated every first period of time. Once it is found that the control effect of the offline map reaches the preset standard, it means that the autonomous driving vehicle has re-entered the coverage area of the offline map. Subsequently, the autonomous driving vehicle can be controlled based on the offline map.
[0110] In addition, when deciding to switch to the offline map, a delayed smooth switching strategy such as reducing the vehicle speed and delaying the switch can be adopted to ensure that it does not affect the driving of the autonomous driving vehicle and achieve seamless connection between the local map and the wireless map.
[0111] In step 506, global path planning is performed based on the offline map, and the autonomous driving vehicle is controlled based on the planning results.
[0112] When performing global path planning based on offline maps, real-time traffic information such as dynamic road conditions, accident areas, weather warnings, etc. can also be introduced to adjust path planning. Path safety assessments can also be added to prevent autonomous vehicles from entering areas with high driving risks.
[0113] In step 507, wait for a second time period.
[0114] The second duration is a predetermined duration, such as 1 minute, 30 seconds, etc.
[0115] In step 508 , it is determined whether an emergency occurs, such as an abnormality in the offline map or a traffic accident on the driving route. If so, the process proceeds to step 509 ; otherwise, the process proceeds to step 507 .
[0116] In step 509 , emergency obstacle avoidance is performed based on the latest local map to ensure safe driving, and then the process proceeds to step 504 .
[0117] In this way, in the process of controlling the driving of the autonomous driving vehicle based on the offline map, the second time period is used as a period to periodically judge whether the autonomous driving vehicle encounters an emergency situation, and when it is determined that an emergency situation has occurred, it switches to the latest local map, which is conducive to improving the driving safety of the autonomous driving vehicle.
[0118] Furthermore, if the map anomaly is determined to be something other than a pre-defined anomaly type, such as missing maps (because this is a known issue for the map provider), the mobile device's location can be recorded. If this location is recorded a pre-defined number of times within a pre-defined period, an error message will be reported for this location on the offline map. This allows map providers to improve their maps and provide better map services.
[0119] Based on the same technical concept, an embodiment of the present application also provides a control device for a mobile device. The principle of solving the problem by the control device of the mobile device is similar to that of the control method of the above-mentioned mobile device. Therefore, the implementation of the control device of the mobile device can refer to the implementation of the control method of the mobile device, and the repeated parts will not be repeated.
[0120] Figure 6 A schematic structural diagram of a control device for a mobile device provided in an embodiment of the present application includes:
[0121] An evaluation module 601 is configured to evaluate the control effect of the offline map based on control scenario representation data acquired from the mobile device when a preset condition is met, wherein the control scenario representation data includes data collected by sensors on the mobile device;
[0122] A selection module 602 is configured to select the offline map or a local map as a target map based on a control effect evaluation result of the offline map, wherein the local map is established based on the collected data of the sensor;
[0123] The control module 603 is configured to control the movement of the mobile device based on the target map.
[0124] In some embodiments, the evaluation module 601 is specifically configured to:
[0125] Based on the local map, evaluating the error of the offline map;
[0126] Based on the error evaluation result, a control effect evaluation result of the offline map is determined.
[0127] In some embodiments, the control scenario representation data further includes the speed of the mobile device, and the sensor collected data includes the distance of obstacles around the mobile device. The evaluation module 601 is specifically configured to:
[0128] evaluating, based on the speed of the mobile device and the distance to the obstacle, a degree of loss of control when controlling the vehicle based on the offline map;
[0129] Based on the error evaluation result and the out-of-control degree evaluation result, a control effect evaluation result of the offline map is determined.
[0130] In some embodiments, the evaluation module 601 is specifically configured to:
[0131] The result of the out-of-control degree assessment is determined according to the rule that speed is positively correlated with the out-of-control degree, and the distance to the obstacle is negatively correlated with the out-of-control degree.
[0132] In some embodiments, the evaluation module 601 is specifically configured to:
[0133] According to the rule that the error of the offline map is negatively correlated with the control effect, and the degree of loss of control when using the offline map for control is negatively correlated with the control effect, the error evaluation result and the degree of loss of control evaluation result are fused to obtain the control effect evaluation result of the offline map.
[0134] In some embodiments, the selection module 602 is specifically configured to:
[0135] When the control effect of the offline map reaches a preset standard, selecting the offline map as the target map;
[0136] When the control effect of the offline map does not meet the preset standard, the local map is selected as the target map.
[0137] In some embodiments, the selection module 602 is further configured to:
[0138] If the offline map is selected as the target map, when an abnormality of the offline map is detected or a preset emergency occurs on the moving route of the mobile device, the target map is switched to the latest established local map.
[0139] In some embodiments, an exception handling module 604 is further included for:
[0140] When the offline map anomaly is detected, if it is determined that the map anomaly type is not a preset anomaly type, recording the location of the mobile device;
[0141] If the number of times the location is recorded reaches a preset number within a preset time period, information indicating that there is an error in the location of the offline map is reported.
[0142] In some embodiments, the sensor includes a visual sensor and a radar sensor, and the local map is established according to the following steps:
[0143] According to the obtained mobile scene characterization data of the mobile device, a target mobile scene of the mobile device is determined, wherein the mobile scene characterization data comprises mobile road information and weather information;
[0144] According to the priority information of the visual sensor and the radar sensor in different mobile scenes established in advance, the priority of the visual sensor and the radar sensor in the target mobile scene is determined.
[0145] According to the priority of the visual sensor and the radar sensor in the target mobile scene, the local map is established by using the collected data of the visual sensor and the radar sensor.
[0146] The division of the modules in the embodiments of the present application is illustrative, and is merely a logical function division. In actual implementation, another division manner can be used. In addition, the function modules in the embodiments of the present application can be integrated in one processor, or can be physically separated, or two or more modules can be integrated in one module. The coupling between the modules can be realized through some interfaces. The interfaces are usually electrical communication interfaces, but can also be mechanical interfaces or other form interfaces. Therefore, the modules described as separate components can be or can not be physically separated, and can be located in one place or distributed to different locations of the same or different devices. The integrated modules can be realized in the form of hardware or in the form of software function modules.
[0147] After introducing the control method and device of the mobile device of the example embodiment of the present application, next, the electronic device according to another example embodiment of the present application is introduced.
[0148] The electronic device 130 implemented according to this embodiment of the present application is described below with reference to Figure 7 Figure 7 The displayed electronic device 130 is only an example, and should not bring any limitation to the function and use range of the embodiments of the present application.
[0149] As shown in Figure 7 , the electronic device 130 is shown in the form of a general electronic device. The components of the electronic device 130 can include but are not limited to the above-mentioned at least one processor 131, the above-mentioned at least one memory 132, and the bus 133 connecting different system components including the memory 132 and the processor 131.
[0150] The bus 133 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, a processor or a local bus using any of a variety of bus structures.
[0151] The memory 132 may include a readable medium in the form of a volatile memory, such as a random access memory (RAM) 1321 and / or a cache memory 1322 , and may further include a read-only memory (ROM) 1323 .
[0152] The memory 132 may also include a program / utility 1325 having a set (at least one) of program modules 1324, such program modules 1324 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.
[0153] The electronic device 130 may also communicate with one or more external devices 134 (e.g., a keyboard, pointing device, etc.), one or more devices that enable a user to interact with the electronic device 130, and / or any device that enables the electronic device 130 to communicate with one or more other electronic devices (e.g., a router, a modem, etc.). Such communication may occur via an input / output (I / O) interface 135. Furthermore, the electronic device 130 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 136. As shown, the network adapter 136 communicates with other modules of the electronic device 130 via a bus 133. It should be understood that, although not shown, other hardware and / or software modules may be used in conjunction with the electronic device 130, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0154] In an exemplary embodiment, the electronic device of the present application may include at least one processor and a memory communicatively connected to the at least one processor, wherein the memory stores a computer program that can be executed by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor can perform the steps of the control method of any mobile device provided in the embodiments of the present application.
[0155] In an exemplary embodiment, a storage medium is also provided. When a computer program in the storage medium is executed by a processor of an electronic device, the electronic device can perform any of the above-mentioned methods for controlling a mobile device. Optionally, the storage medium can be a non-transitory computer-readable storage medium, for example, a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.
[0156] In an example embodiment, a computer program product is also provided, which, when executed by an electronic device, enables the electronic device to implement any of the example methods provided herein.
[0157] It should be noted that, although several modules or sub-modules of the apparatus are mentioned in the foregoing detailed description, such a division is merely exemplary and not mandatory. Indeed, according to an implementation of the application, the features and functionalities of two or more modules described above can be embodied in one module. Conversely, the features and functionalities of one module described above can be further divided into modules.
[0158] Moreover, although the operations of the method(s) herein can be described in a particular, sequential order, this order is not meant to be a limitation and is not intended to imply that
[0159] Those of skill in the art would understand that embodiments of the present application can be provided as a method, a system, or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, and the like) embodying computer-readable program code.
[0160] While the preferred embodiments of the application have been described above, it should be understood that many modifications and adaptations of the described embodiments will also be apparent to those skilled in the art. Therefore, the detailed description and representations are to be regarded as illustrative and not restrictive, and it will be apparent that other embodiments falling within the scope of the application will be possible. Accordingly, the appended claims are intended to embrace all such adaptations and modifications of the described embodiments as fall within the scope of the application.
[0161] Obviously, many modifications and variations of this application are possible in light of the above teachings. It is, therefore, to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.
Claims
1. A method for controlling a mobile device, characterized in that: include: When a preset condition is met, evaluating the control effect of the offline map based on control scenario representation data obtained from the mobile device, wherein the control scenario representation data includes data collected by sensors on the mobile device; Based on the control effect evaluation result of the offline map, selecting the offline map or a local map as the target map, wherein the local map is established based on the collected data of the sensor; Based on the target map, the mobile device is controlled to move.
2. The method according to claim 1, wherein Based on the control scenario representation data obtained from the mobile device, the control effect of the offline map is evaluated, including: Based on the local map, evaluating the error of the offline map; Based on the error evaluation result, a control effect evaluation result of the offline map is determined.
3. The method according to claim 2, wherein The control scenario characterization data further includes the speed of the mobile device, and the sensor collected data includes the distances of obstacles around the mobile device; Based on the control scenario representation data obtained from the mobile device, the control effect of the offline map is evaluated, including: evaluating, based on the speed of the mobile device and the distance to the obstacle, a degree of loss of control when controlling the vehicle based on the offline map; Based on the error evaluation result and the out-of-control degree evaluation result, a control effect evaluation result of the offline map is determined.
4. The method according to claim 3, wherein Determining a control effect evaluation result of the offline map based on the error evaluation result and the out-of-control degree evaluation result includes: According to the rule that the error of the offline map is negatively correlated with the control effect, and the degree of loss of control when using the offline map for control is negatively correlated with the control effect, the error evaluation result and the degree of loss of control evaluation result are fused to obtain the control effect evaluation result of the offline map.
5. The method according to any one of claims 1 to 4, characterized in that: If the offline map is selected as the target map, the method further includes: When an abnormality of the offline map is detected or a preset emergency occurs on the moving route of the mobile device, the target map is switched to the latest established local map.
6. The method according to claim 5, wherein When an abnormality of the offline map is detected, the method further includes: When it is determined that the map anomaly type is not a preset anomaly type, recording the location of the mobile device; If the number of times the location is recorded reaches a preset number within a preset time period, information indicating that there is an error in the location of the offline map is reported.
7. The method according to claim 1, wherein The sensors include visual sensors and radar sensors, and the local map is established according to the following steps: Determining a target movement scene of the mobile device based on the acquired movement scene representation data of the mobile device, wherein the movement scene representation data includes movement road information and weather information; Determining the priorities of the visual sensor and the radar sensor in the target movement scene according to pre-established priority information of the visual sensor and the radar sensor in different movement scenes; The local map is established using the collected data of the visual sensor and the radar sensor according to the priorities of the visual sensor and the radar sensor in the target movement scene.
8. An electronic device, characterized in that: include: at least one processor, and a memory communicatively coupled to the at least one processor, wherein: The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can perform the method according to any one of claims 1 to 7.
9. A storage medium, characterized in that: When the computer program in the storage medium is executed by a processor of an electronic device, the electronic device can perform the method according to any one of claims 1 to 7.
10. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 7 when executed by a processor.