Vehicle control method, device and equipment, vehicle, storage medium and program product

Abstract

The embodiment of the invention provides a vehicle control method and device, equipment, a vehicle, a storage medium and a program product. The method comprises the steps of obtaining map information of a scene where a vehicle is located in response to real-time traffic information of the scene where the vehicle is located; matching the road abnormal area change data in the real-time traffic information with the map information; if it is determined that the road abnormal area change data is not matched with the map information, obtaining updated map information from the cloud; wherein the updated map information is obtained by performing map reconstruction processing based on crowdsourcing data of a scene where the vehicle is located; controlling the vehicle to drive to a drivable area according to the updated map information; and secondary scene verification processing is carried out in the vehicle driving process so as to control the vehicle to drive along the driving area. According to the method, the road abnormal area can be recognized in advance in the beyond-visual-range so as to ensure the driving safety.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, and in particular to a vehicle control method, device, equipment, vehicle, storage medium and program product. BACKGROUND

[0002] At present, based on automatic driving technology or assisted driving technology, a vehicle can automatically identify an abnormal situation of a front road, including a road abnormal area where a traffic accident, an obstacle or a construction situation exists, and perform avoidance driving, so that the vehicle can safely pass through the road abnormal area.

[0003] In the prior art, a perception sensor such as a camera / radar in a vehicle is used to collect perception information to identify a road abnormal area to control the vehicle to safely drive.

[0004] However, in the above-mentioned manner, the identification area (200 m range) of the sensor such as the camera / radar is limited, which results in that the road abnormal area cannot be identified in advance in an over-the-horizon range (such as >200 m) and the vehicle cannot be controlled to change lanes, thereby affecting driving safety. SUMMARY

[0005] The vehicle control method, device, equipment, vehicle, storage medium and program product provided by the embodiments of the present application can identify a road abnormal area in advance in an over-the-horizon range to ensure driving safety.

[0006] In a first aspect, the embodiments of the present application provide a vehicle control method, comprising:

[0007] In response to real-time traffic information of a scene where a vehicle is located, map information of the scene where the vehicle is located is acquired, and road abnormal area change data in the real-time traffic information is matched with the map information, wherein the map information includes at least lane-level accuracy geographical information;

[0008] If it is determined that the road abnormal area change data and the map information do not match, updated map information is acquired from a cloud, wherein the updated map information is obtained by performing map reconstruction processing based on crowd-sourced data of the scene where the vehicle is located, and the updated map information includes updated at least lane-level accuracy geographical information;

[0009] The vehicle is controlled to drive to a drivable area according to the updated map information;

[0010] Secondary scene verification processing is performed in a driving process of the vehicle to control the vehicle to drive along the drivable area.

[0011] In a possible implementation manner, the controlling the vehicle to drive to the drivable area according to the updated map information comprises:

[0012] determining relative position information of the vehicle according to the updated map information and the positioning information of the vehicle, wherein the relative position information represents a relative position condition between the vehicle and the abnormal road area ahead;

[0013] controlling the vehicle to drive to the drivable area according to the relative position information of the vehicle.

[0014] In a possible implementation, the determining the relative position information of the vehicle according to the updated map information and the positioning information of the vehicle comprises:

[0015] fusing the updated map information and the real-time traffic information to obtain abnormal road area information;

[0016] determining the relative position information of the vehicle according to the abnormal road area information and the positioning information of the vehicle.

[0017] In a possible implementation, the controlling the vehicle to drive to the drivable area according to the relative position information of the vehicle comprises:

[0018] if it is determined that the relative position information of the vehicle represents that the vehicle is currently in a first preset range of the abnormal road area ahead, updating a target cruise speed of the vehicle, and generating first reminding information, wherein the first reminding information is used to remind a user that there is an abnormal road area ahead;

[0019] controlling the vehicle to drive to the drivable area according to the updated target cruise speed.

[0020] In a possible implementation, the controlling the vehicle to drive to the drivable area according to the updated target cruise speed comprises:

[0021] if it is determined that the vehicle is currently in an abnormally occupied lane, determining a lane-changing condition of the vehicle according to a driving speed of the vehicle and a surrounding traffic state;

[0022] if it is determined that the lane-changing condition of the vehicle represents that the current vehicle meets a lane-changing condition, controlling the vehicle to continue lane-changing driving to the drivable area according to the updated target cruise speed, and generating second reminding information, wherein the second reminding information is used to remind a user that there is an abnormal road area ahead and the current vehicle is currently performing lane-changing driving.

[0023] In a possible implementation, the method further comprises:

[0024] If it is determined that the lane change condition of the vehicle represents that the current vehicle does not meet the lane change condition, a third reminder information is generated after a preset time period; wherein the third reminder information is used to remind the user that there is an abnormal road area in front of the vehicle, and the current vehicle needs to manually select the lane.

[0025] In a possible implementation, the secondary scene verification processing is performed during the driving of the vehicle to control the vehicle to drive along the drivable area, including:

[0026] According to the positioning information of the vehicle, the relative position information of the vehicle is updated; wherein the relative position information represents the relative position condition between the vehicle and the abnormal road area in front of the vehicle;

[0027] If it is determined that the updated relative position information represents that the vehicle is currently in a passable lane within a second preset range of the abnormal road area in front of the vehicle, the target cruise speed of the vehicle is updated; and the vehicle-mounted sensing information of the vehicle is obtained;

[0028] According to the vehicle-mounted sensing information of the vehicle, the drivable area of the vehicle is determined;

[0029] According to the updated target cruise speed, the vehicle is controlled to drive along the drivable area of the vehicle; and a fourth reminder information is generated; wherein the fourth reminder information is used to remind the user to be careful when passing through the abnormal road area.

[0030] In a possible implementation, the updated map information is obtained by image processing the road abnormal area image information in the crowd-sourced data to obtain the lane occupancy information corresponding to the road abnormal area image information; and the updated map information is obtained by updating the map information according to the lane occupancy information.

[0031] In a possible implementation, the image processing the road abnormal area image information in the crowd-sourced data to obtain the lane occupancy information corresponding to the road abnormal area image information includes:

[0032] The road element information in the road abnormal area image information is obtained by performing image recognition processing on the road abnormal area image information;

[0033] According to the road element information in the road abnormal area image information, the road abnormal area image information is processed by semantic segmentation to obtain the lane occupancy information corresponding to the road abnormal area image information.

[0034] In a possible implementation, the updating the map information according to the lane occupancy information includes:

[0035] According to the lane occupancy information and the road abnormal area change data, the base lane information in the map information is associated and updated to generate the updated map information.

[0036] In a possible implementation, the method further includes:

[0037] If it is determined that the real-time traffic information and the map information match, the vehicle is controlled to travel according to the map information.

[0038] In a second aspect, an embodiment of the present application provides a vehicle control device, including:

[0039] The matching module is configured to: in response to real-time traffic information of a scene in which a vehicle is located, acquire map information of the scene in which the vehicle is located; and match road abnormal area change data in the real-time traffic information and the map information; wherein the map information includes at least lane-level-precision geographic information.

[0040] The updating module is configured to: if it is determined that the road abnormal area change data and the map information do not match, acquire updated map information from a cloud; wherein the updated map information is obtained by performing map reconstruction processing based on crowd-sourced data of the scene in which the vehicle is located; and the updated map information includes updated at least lane-level-precision geographic information.

[0041] The first control module is configured to control the vehicle to travel to a drivable area according to the updated map information.

[0042] The second control module is configured to perform secondary scene verification processing during vehicle travel to control the vehicle to travel along the drivable area.

[0043] In a possible implementation, the first control module is specifically configured to: determine relative position information of the vehicle according to the updated map information and positioning information of the vehicle; wherein the relative position information represents a relative position condition between the vehicle and a front road abnormal area; and control the vehicle to travel to the drivable area according to the relative position information of the vehicle.

[0044] In a possible implementation, the first control module is specifically configured to: perform fusion according to the updated map information and the real-time traffic information to obtain road abnormal area information; and determine relative position information of the vehicle according to the road abnormal area information and the positioning information of the vehicle.

[0045] In a possible implementation, the first control module is further configured to: if it is determined that the relative position information of the vehicle represents that the vehicle is currently in a first preset range of the abnormal road area ahead, update a target cruise speed of the vehicle; and generate first reminding information; wherein the first reminding information is used to remind a user that there is an abnormal road area ahead; and control the vehicle to travel to the drivable area according to the updated target cruise speed.

[0046] In a possible implementation, the first control module is further configured to: if it is determined that the vehicle is currently in an abnormally occupied lane, determine a lane changing condition of the vehicle according to a driving speed of the vehicle and a surrounding traffic state; if it is determined that the lane changing condition of the vehicle represents that the current vehicle meets a lane changing condition, control the vehicle to continue to change lanes and travel to the drivable area according to the updated target cruise speed; and generate second reminding information; wherein the second reminding information is used to remind a user that there is an abnormal road area ahead and the current vehicle is changing lanes.

[0047] In a possible implementation, the first control module is further configured to: if it is determined that the lane changing condition of the vehicle represents that the current vehicle does not meet the lane changing condition, generate third reminding information after a preset time period; wherein the third reminding information is used to remind a user that there is an abnormal road area ahead and the current vehicle needs to manually select a lane.

[0048] In a possible implementation, the second control module is configured to: update relative position information of the vehicle according to positioning information of the vehicle; if it is determined that the updated relative position information represents that the vehicle is currently in a drivable lane within a second preset range of the abnormal road area ahead, update a target cruise speed of the vehicle; obtain vehicle-mounted sensing information of the vehicle; determine a drivable area of the vehicle according to the vehicle-mounted sensing information of the vehicle; control the vehicle to travel along the drivable area of the vehicle according to the updated target cruise speed; and generate fourth reminding information; wherein the fourth reminding information is used to remind a user to be careful when passing through the abnormal road area.

[0049] In a possible implementation, the updated map information is obtained by performing image processing on road abnormal area image information in the crowdsourcing data to obtain lane occupancy information corresponding to the road abnormal area image information, and performing update processing on the map information according to the lane occupancy information; wherein the updated map information includes lane occupancy of the abnormal road area ahead of the vehicle.

[0050] In a possible implementation, the updating module is specifically configured to: perform image recognition processing on the road anomaly region image information to obtain road element information in the road anomaly region image information; and perform semantic segmentation processing on the road anomaly region image information according to the road element information in the road anomaly region image information, to obtain lane occupancy information corresponding to the road anomaly region image information.

[0051] In a possible implementation, the updating module is further specifically configured to: perform associated updating processing on basic lane information in the map information according to the lane occupancy information and the road anomaly region change data, to generate the updated map information.

[0052] In a possible implementation, the apparatus is further configured to: if it is determined that the real-time traffic information matches the map information, control the vehicle to travel according to the map information.

[0053] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory, a processor;

[0054] The memory stores computer-executed instructions.

[0055] The processor executes the computer-executed instructions stored in the memory, so that the processor executes the first aspect and / or various possible implementations of the first aspect.

[0056] In a fourth aspect, an embodiment of the present application provides a vehicle, including the electronic device of the third aspect, and the electronic device is configured to execute the first aspect and / or various possible implementations of the first aspect.

[0057] In a fifth aspect, an embodiment of the present application provides a computer-readable storage medium, and the computer-readable storage medium stores computer-executed instructions, and the computer-executed instructions are executed by a processor to implement the first aspect and / or various possible implementations of the first aspect.

[0058] In a sixth aspect, an embodiment of the present application provides a computer program product, including a computer program, and the computer program is executed by a processor to implement the first aspect and / or various possible implementations of the first aspect.

[0059] The vehicle control method, device, equipment, vehicle, storage medium and program product provided by the embodiments of the present application can be used to control the vehicle to safely drive to a drivable area according to updated map information obtained by cloud end map reconstruction processing based on crowd-sourced data of the scene where the vehicle is located, and to control the vehicle to drive along the drivable area through secondary scene verification during the driving of the vehicle. In addition, the cooperative processing of the crowd-sourced data and real-time traffic information can be used to identify the road abnormal area in the over-the-horizon range in advance and control the vehicle to drive, and the effective lane changing and route selection driving can be made through the secondary scene verification, so as to ensure the driving safety. BRIEF DESCRIPTION OF DRAWINGS

[0060] The accompanying drawings, which are incorporated herein and form part of the specification, illustrate embodiments consistent with the present application and, together with the description, further serve to explain the principles of the application.

[0061] Figure 1 A flowchart of a vehicle control method provided by the embodiments of the present application is shown in FIG. 1.

[0062] Figure 2 A flowchart of another vehicle control method provided by the embodiments of the present application is shown in FIG. 2.

[0063] Figure 3 A work flowchart of a construction area preferred lane changing method provided by the embodiments of the present application is shown in FIG. 3.

[0064] Figure 4 An example of a construction area preferred lane changing situation provided by the embodiments of the present application is shown in FIG. 4. Figure 1

[0065] Figure 5 An example of a construction area preferred lane changing situation provided by the embodiments of the present application is shown in FIG. 5. Figure 2

[0066] Figure 6 An example of a construction area preferred lane changing situation provided by the embodiments of the present application is shown in FIG. 6. Figure 3

[0067] Figure 7 A structural diagram of a vehicle control device provided by the embodiments of the present application is shown in FIG. 7.

[0068] Figure 8 A structural diagram of an electronic device provided by the embodiments of the present application is shown in FIG. 8.

[0069] ​​​The specific embodiments of the application have been shown by way of example in the above figures, and will be described in more detail hereafter. These figures and this written description are not intended to limit the scope of the inventive concept in any way, but rather to illustrate the inventive concept to one of ordinary skill in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0070] The exemplary embodiments will be described in detail herein with reference to the attached drawings. The same numbers are used in different drawings to represent the same or similar elements. The following detailed description is not intended to limit the application, as claimed, in any way. Rather, it is described to provide those of ordinary skill in the art with a thorough understanding of the application, to the extent that the application will be claimed, as detailed in the claims.

[0071] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties, and the collection, use, processing, transmission, provision, disclosure and application of related data comply with relevant laws, regulations and standards, necessary security measures are taken, do not violate public order and good customs, and provide corresponding operation portal for user to choose authorization or refusal.

[0072] It should be noted that the present application can be used in the field of vehicle technology, and can also be used in any field other than vehicle technology. The application field of the present application is not limited.

[0073] The perception information is collected by the perception sensor such as camera / radar in the vehicle to identify the abnormal road area to control the safe driving of the vehicle.

[0074] In combination with the above scenario, it can be seen that in the prior art, due to the limitation of the identification distance of the perception sensor, the distance of triggering the early warning or lane changing is short, and the driver cannot respond in time to cope with this abnormal working condition, thereby causing a dangerous collision.

[0075] The vehicle control method provided by the present application can identify the abnormal road area in the over-the-horizon range in advance through the cooperative processing of the crowd-sourced data and real-time traffic information, so as to ensure the driving safety.

[0076] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described again in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0077] Figure 1A flowchart of a vehicle control method provided by an embodiment of the present application is shown in FIG. 1, which includes the following steps. Figure 1

[0078] 201. In response to real-time traffic information of a scene where the vehicle is located, obtain map information of the scene where the vehicle is located, and match road abnormal area change data in the real-time traffic information with the map information, wherein the map information includes at least lane-level geographic information.

[0079] By way of example, the execution subject of the embodiment can be an electronic device in a vehicle, hereinafter referred to as a device. In the scene where the vehicle is driving, the real-time traffic information (RTTI) and map information in front of the scene where the vehicle is located are stored in the cloud. The RTTI data is obtained by real-time collection and uploading by traffic monitoring devices such as traffic cameras, road sensors, roadside units, etc., and can also include road abnormal area change data for providing real-time updates of the current traffic situation. The road abnormal area change data includes construction area dynamic change data, traffic accident area dynamic change data, other obstacle area dynamic change data, and traffic control area dynamic change data, etc., to represent the situation of road condition abnormal area occupying the lane caused by construction, traffic accident or other obstacles. The construction area dynamic change data can include real-time construction information such as timestamp calibration, construction location coordinates, and construction area surrounding traffic state, to provide real-time data sources. The road condition abnormal area includes traffic accidents, road repairs, traffic restrictions, and other areas that are not normally drivable or may cause safety problems.

[0080] The map information can include high-precision map data to represent static traffic and road conditions, such as traffic signals, road signs and markings, lane-level information, precise positioning reference data, static obstacle information, etc. The static obstacle information includes static construction information. The lane-level information includes at least lane-level geographic information, such as the number of lanes of the current road, ground marking lines, entrances and exits, special lanes, etc.

[0081] ​In the driving process, the vehicle can receive the RTTI data of the scene where the vehicle is located issued by the cloud. After the vehicle responds to the RTTI data, the vehicle obtains the map information of the scene where the vehicle is located from the local end of the vehicle, and matches and compares the road abnormal area change data in the RTTI data and the map information to determine whether the recorded conditions in the road abnormal area change data and the map information are consistent. Specifically, taking the positioning information and timestamp of the vehicle-mounted global positioning system (GPS) as the reference, the RTTI data and the map information are first aligned according to time and place. For the road abnormal area indicated by the road abnormal area change data, the map information also has a road abnormal situation in the road abnormal area indicated by the road abnormal area change data, for example, the position in front of the vehicle indicated by the road abnormal area change data in the RTTI data is in a construction area, and the map information has marking information of the construction area in the position in front of the vehicle, and then it is determined that the road abnormal area change data and the map information match. Otherwise, the road abnormal area change data and the map information do not match.

[0082] 202. If it is determined that the road abnormal area change data and the map information do not match, updated map information is obtained from the cloud; wherein the updated map information is obtained by map reconstruction processing based on the crowd-sourced data of the scene where the vehicle is located; and the updated map information includes updated geographical information of at least lane level accuracy.

[0083] Exemplarily, the RTTI data has high accuracy, reliability and data real-time performance. When the device determines that the road abnormal area change data of the scene where the vehicle is located and the corresponding map information do not match, it indicates that the map information has not been updated in time to reflect the actual traffic situation. Therefore, the updated map information can be obtained from the cloud.

[0084] The cloud can receive real-time crowd-sourced data uploaded by crowd-sourced vehicles (such as vehicles that upload real-time road conditions through an Internet platform or a mobile application). The crowd-sourced data includes real-time road condition information reported by vehicle users, such as road conditions, accident reports, and travel habits. After receiving the crowd-sourced data of the scene where the vehicle is located uploaded by the crowd-sourced vehicle, the cloud performs real-time map reconstruction processing based on the crowd-sourced data. Specifically, the road condition information in the current map information can be updated and replaced according to the crowd-sourced data to obtain updated map information. The updated map information includes real-time road condition information, real-time road abnormal area change data, and updated lane level information in the crowd-sourced data. The current map information can be supplemented, the latest road traffic situation can be determined in real time, and the comprehensiveness and real-time performance of the data can be improved. The updated lane level information includes updated geographical information of at least lane level accuracy.

[0085] 203、controlling the vehicle to travel to the drivable area according to the updated map information.

[0086] Exemplarily, after obtaining the updated map information, the device can determine the real-time traffic condition in front of the vehicle in advance according to the road abnormal area change data in the updated map information, including whether there is a road abnormal area in front of the vehicle, and then control the vehicle to continue traveling according to the current driving mode or the current driving route, or to change the current driving mode or the current driving route, so that the vehicle travels to the drivable area, including the non-road abnormal area or the passable lane.

[0087] For example, if it is determined that there is a construction area in front of the vehicle, the vehicle needs to change the current driving route in advance, and control the vehicle to travel to the non-construction area according to the changed driving route in a timely manner, so as to ensure the safe driving of the vehicle through the construction area in a timely manner.

[0088] 204、In the process of vehicle driving, secondary scene verification processing is performed to control the vehicle to travel along the drivable area.

[0089] Exemplarily, after controlling the vehicle to travel for a predetermined distance according to the updated map information, the vehicle has not yet passed through the road abnormal area, and the vehicle can obtain the real-time collected vehicle perception information. Specifically, the vehicle perception information of the vehicle is collected through the vehicle perception device (radar, camera), including the image information of the road area in front of the vehicle, and the vehicle perception information is processed based on the perception algorithm such as image recognition algorithm or target detection algorithm, the road abnormal area in the front range is verified again, that is, the road abnormal area in the front range is identified, and then the drivable area of the vehicle in the front range is determined, and the vehicle is controlled to travel along the drivable area of the vehicle to ensure the safe driving of the vehicle through the road abnormal area.

[0090] In this embodiment, a vehicle control method is provided, which matches the road abnormal area change data in the RTTI data and the map information. If the road abnormal area change data and the map information do not match, the updated map information is reconstructed through the crowd data of the cloud, and is delivered to the vehicle. The vehicle can know the specific traffic matter in a timely manner and control the vehicle; based on the RTTI data and the map information, the data collected in the super-range can realize the super-range response range, the road abnormal area can be identified in the super-range in advance, so as to make effective lane changing and route selection driving, and after driving for a distance, secondary scene verification is performed to identify the road abnormal area, and then the vehicle is controlled to travel along the drivable area to ensure the driving safety.

[0091] Figure 2 Another flowchart of a vehicle control method provided by the embodiment of the application is shown in FIG. 6. Figure 2As shown, the method comprises:

[0092] 301. In response to real-time traffic information of a scene where the vehicle is located, map information of the scene where the vehicle is located is acquired; and road abnormal area change data in the real-time traffic information is matched with the map information; wherein the map information comprises geographical information of at least lane level accuracy.

[0093] By way of example, this step can refer to step 201, which will not be described here again.

[0094] 302. If it is determined that the road abnormal area change data and the map information do not match, updated map information is acquired from the cloud.

[0095] By way of example, when the device determines that the road abnormal area change data corresponding to the scene where the vehicle is located and the corresponding map information do not match, the map information needs to be updated, and therefore, updated map information can be acquired from the cloud.

[0096] The cloud can receive crowd-sourced data uploaded in real time by crowd-sourced vehicles (such as vehicles that upload road conditions in real time through an Internet platform or a mobile application), which comprises road abnormal area image information reported by vehicle users, such as image data or point cloud data of road abnormal areas collected by sensors such as radars or cameras in the vehicles of the users. After receiving the crowd-sourced data of the scene where the vehicle is located uploaded by the crowd-sourced vehicles, the cloud performs image processing on the road abnormal area image information in the crowd-sourced data by using an image recognition algorithm to identify lane occupation information in the road abnormal area image information, including the number of lanes occupied in the road abnormal area and specific lane numbers.

[0097] Specifically, a pre-set deep learning model, such as a convolutional neural network (CNN) that has been trained, can be used to perform lane detection and object recognition on the acquired road abnormal area image information to obtain lane detection results and object recognition results in the corresponding road abnormal area, and the lane detection results and the object recognition results can be combined to analyze whether each lane is occupied by an object and to determine lane occupation in the road abnormal area.

[0098] According to the obtained lane occupation information, the existing map information is updated to obtain updated map information, which comprises real-time road condition information in the crowd-sourced data, specifically, lane occupation in the road abnormal area in front of the vehicle, to supplement the current map information and further improve the comprehensiveness and real-time performance of the data.

[0099] For example, Figure 3 A working flowchart of a construction area preferred lane changing method provided by an embodiment of the present application is shown in FIG. 2.Figure 3 As shown, the RTTI data (i.e., real-time traffic information) corresponding to the scene where the vehicle is located is obtained to obtain construction information, including timestamp calibration, construction location coordinates, and construction area surrounding traffic state. The RTTI data and the high-precision map static construction information in the map information are compared, that is, according to the construction information indicated by the RTTI data, it is determined whether the corresponding region on the high-precision map also has construction information. If it is determined that the corresponding region on the high-precision map does not have construction information, it means that the RTTI data and the high-precision map static construction information are not matched. Then, through the cloud, the crowdsourcing image of the corresponding construction area in the crowdsourcing data is received. After image processing, the lane occupancy of the construction area is obtained, so as to update the high-precision map static construction information. The updated high-precision map static construction information is issued to the vehicle (i.e., the vehicle end) in an incremental form. The vehicle obtains the updated high-precision map static construction information to obtain the temporarily updated lane occupancy of the abnormal road area in front of the vehicle, so as to realize the dynamic update of the lane-level abnormal road area (such as the borrow lane opposite lane scene) in time and avoid the lag of the dynamic road abnormal response.

[0100] In a possible implementation, the step 302 includes: performing image recognition processing on the road abnormal area image information to obtain road element information in the road abnormal area image information; and performing semantic segmentation processing on the road abnormal area image information according to the road element information in the road abnormal area image information to obtain lane occupancy information corresponding to the road abnormal area image information.

[0101] Specifically, the acquired road abnormal area image information can be processed by a preset deep learning model, such as a CNN that has been trained, to recognize elements such as lane lines, cones, water marks, and construction signs in the road abnormal area image, so as to obtain road element information in the road abnormal area image information, including the boundaries and shapes of the lanes and the positions and sizes of the elements such as cones, water marks, and construction signs. In combination with the road element information, a semantic segmentation algorithm, such as a trained FCN, is used to process the image in the road abnormal area image information, to analyze whether there is an object occupying each lane in the image. The lane occupancy can be determined by calculating the overlapping area of the vehicle in the lane area, and the lane occupancy information is obtained. The deep learning model is used for image processing to obtain more accurate lane occupancy of the road abnormal area, so as to improve the data reliability.

[0102] In a possible implementation, the step 302 further includes: performing associated update processing on the basic lane information in the map information according to the lane occupancy information and the road abnormal area change data, to generate updated map information.

[0103] Specifically, the obtained lane occupancy information and the road abnormal area change data in the RTTI data and the basic lane information in the map information are associated and aligned, and the basic lane information in the map information is updated to obtain updated map information, including construction area lane occupancy data attached to the high-precision map, which can specifically include occupancy target lane ID, occupancy type label, and position coordinates and other data, and can further provide lane-level dynamic data support for scenarios such as borrowing lanes and opposite lanes, and is beneficial to improve the accuracy and reliability of subsequent auxiliary control.

[0104] Further, if it is determined that the road abnormal area change data in the RTTI data matches the obtained map information, the real-time traffic condition in front of the vehicle can be determined in advance according to the road abnormal area static data in the map information, including whether there is a road abnormal area in front of the vehicle, and then whether the vehicle continues to drive according to the current driving mode or the current driving route, or changes the current driving mode or the current driving route, is timely controlled.

[0105] Further, based on the GPS positioning information and the timestamp of the vehicle, the device maps the high-precision map data included in the map information, which can provide accurate geographic coordinates (start point\end point\boundary coordinates) of the road abnormal area, lane occupancy, and other static information, to a unified space-time coordinate system, and determines the relative position between the vehicle and the front road abnormal area, i.e. relative position information, including the distance between the front of the vehicle and the boundary of the front road abnormal area. The device can determine whether early warning is needed according to the obtained relative position information of the vehicle, and if the distance in the relative position information is within a preset early warning distance range, dynamically adjust the lane changing strategy (automatic lane changing / human takeover), and thus can timely control the vehicle to safely drive through the front road area.

[0106] For example, in combination with the above-mentioned embodiments, the device can further include the following steps. Figure 3The vehicle receives the construction information at a distance of 500 m in front of the construction position, immediately updates the target cruise speed to 80 km / h, and reminds the driver that "there is a construction area in front"; it is determined whether the vehicle is in the construction lane or in the passable lane, if it is in the construction lane, the construction optimization lane changing function is triggered 500 m in advance, the vehicle is controlled to change into the passable lane, and the driver is reminded that "there is a construction area in front, and the optimization lane changing is being initiated"; if the vehicle is in the construction lane and the lane changing condition is not met (because there is no lane changing space), the driver is reminded after 10 seconds that "there is a construction area in front, and the lane needs to be manually selected"; if the vehicle is in the passable lane, the driver is only reminded that "there is a construction area in front, please keep straight"; when the vehicle enters the passable lane in the construction state interval (200 m in front of the construction sign), the target cruise speed is updated to 60 km / h (or the speed of the speed limit sign recognition vehicle) to safely drive along the drivable area of the construction area, and the driver is reminded to "be careful when passing through the construction area".

[0107] Further, the device maps the road abnormal area change data in the RTTI data and the high-precision map data in the map information to the GPS timestamp coordinate system, aligns the multi-source space-time coordinates, obtains the fused information, and determines the road abnormal area information included in the fused information according to the road abnormal area position marking situation of the road abnormal area change data in the RTTI data, which specifically includes the start point\end point\boundary coordinates of the road abnormal area in front of the vehicle, the lane occupation situation and the like. The road abnormal area information and the positioning information of the vehicle are mapped to the GPS timestamp coordinate system, and the relative position information of the vehicle is calculated according to the coordinates in the road abnormal area information and the coordinates in the positioning information of the vehicle, so as to determine the relative position relationship between the vehicle and the road abnormal area in front of the vehicle.

[0108] 303. Determine the relative position information of the vehicle according to the updated map information and the positioning information of the vehicle; wherein the relative position information represents the relative position situation between the vehicle and the road abnormal area in front.

[0109] Exemplarily, taking the GPS positioning information and the timestamp of the vehicle as the reference, the device updates the high-precision map data in the map information according to the updated map information to obtain the updated high-precision map, which can provide the accurate geographic coordinates (start point\end point\boundary coordinates) of the road abnormal area, the lane occupation situation and the like. The updated high-precision map and the positioning information of the vehicle are mapped to the unified space-time coordinate system, and the relative position situation between the vehicle and the road abnormal area in front, i.e. the relative position information, including the distance between the front of the vehicle and the boundary of the road abnormal area in front, can be determined.

[0110] In a possible implementation, step 303 includes: obtaining road abnormal area information according to fusion of the updated map information and the real-time traffic information; and determining the relative position information of the vehicle according to the road abnormal area information and the positioning information of the vehicle.

[0111] Specifically, the device updates the map information, updates the high-definition map in the map information to obtain an updated high-definition map, and fuses the updated high-definition map and the road abnormal area change data in the obtained RTTI data, for example, maps the road abnormal area change data in the RTTI data and the updated high-definition map to a GPS timestamp coordinate system, performs multi-source space-time coordinate alignment, to obtain fused information, and determines road abnormal area information included in the fused information through road abnormal area position marking of the road abnormal area change data in the RTTI data, and the road abnormal area information specifically includes start point / endpoint / boundary coordinates of a road abnormal area in front of the vehicle, lane occupation, and the like. The road abnormal area information and the positioning information of the vehicle are mapped to the GPS timestamp coordinate system, and the relative position information of the vehicle is calculated according to the coordinates in the road abnormal area information and the coordinates in the positioning information of the vehicle, to determine the relative position relationship between the vehicle and the road abnormal area in front of the vehicle.

[0112] 304. Control the vehicle to travel to a drivable area according to the relative position information of the vehicle.

[0113] Exemplarily, the device can determine whether early warning is needed according to the obtained relative position information of the vehicle, if the distance in the relative position information is within a preset early warning distance range, dynamically adjust the lane changing strategy (automatic lane changing / manual takeover), and further control the vehicle to safely travel to the drivable area, so that the vehicle can safely pass through the road area in front.

[0114] In a possible implementation, step 304 includes: updating the target cruise speed of the vehicle if the relative position information of the vehicle indicates that the vehicle is currently in a first preset range of the road abnormal area in front; and generating first reminding information; wherein the first reminding information is used to remind the user that there is a road abnormal area in front; and controlling the vehicle to travel to the drivable area according to the updated target cruise speed.

[0115] Specifically, according to the distance between the vehicle and the road abnormal area in front of the vehicle in the relative position information of the vehicle, it can be determined whether the vehicle is currently in the first preset range of the road abnormal area in front of the vehicle. If it is determined that the vehicle is currently in the first preset range of the road abnormal area in front of the vehicle, the target cruise speed of the vehicle can be updated to the preset speed, and at the same time, the reminder information is output to the driver through the instrument panel, the head-up display (HUD) and the voice prompt device in the vehicle, so as to remind the driver that there is a road abnormal area in front of the vehicle. The device can control the vehicle to safely drive to the drivable area according to the updated target cruise speed, so that the vehicle can safely pass through the road abnormal area in front of the vehicle. Among them, the instrument panel, the head-up display (HUD) and the voice prompt device in the vehicle are used to feed back the road abnormal area information, the lane changing plan and the speed adjustment state to the driver in real time, so as to improve the driving transparency and trustworthiness. Among them, the first preset range can be designed by the user according to the actual situation, which is not limited here.

[0116] For example, in combination with Figure 3 , the vehicle uses the updated high-new map lane-level information to make decision control at a distance of 500 m in front of the construction position. Specifically, the vehicle receives the construction information at a distance of 500 m in front of the construction, and immediately updates the target cruise speed to 80 km / h according to the fused construction area information combined with the current position and speed of the vehicle. According to the control of 80 km / h, the vehicle drives to the non-construction area, and reminds the driver that there is a construction area in front of the vehicle.

[0117] In one possible implementation, controlling the vehicle to drive according to the updated target cruise speed includes: if it is determined that the vehicle is currently in the abnormal occupied lane, determining the lane changing condition of the vehicle according to the driving speed of the vehicle and the surrounding traffic state; if it is determined that the lane changing condition of the vehicle represents that the current vehicle meets the lane changing condition, controlling the vehicle to continue to change lanes and drive to the drivable area according to the updated target cruise speed; and generating second reminder information; wherein the second reminder information is used to remind the user that there is a road abnormal area in front of the vehicle, and the current vehicle is driving by changing lanes. If it is determined that the lane changing condition of the vehicle represents that the current vehicle does not meet the lane changing condition, the third reminder information is generated after a preset time period; wherein the third reminder information is used to remind the user that there is a road abnormal area in front of the vehicle, and the current vehicle needs to manually select the lane.

[0118] Specifically, after determining that the vehicle is currently in the first preset range of the front road abnormal area, according to the lane occupation of the road abnormal area in the obtained updated map information and the positioning information of the vehicle, it is determined whether the vehicle is in an abnormally occupied lane in the road abnormal area. If it is determined that the vehicle is currently in the abnormally occupied lane, the construction preferred lane changing function is triggered, that is, according to the driving speed of the vehicle and the surrounding traffic state, the lane changing condition of the vehicle is obtained, including whether the vehicle meets the activation condition of lane changing. If it is determined that the current vehicle meets the lane changing condition, the vehicle is controlled to change into a passable lane (multiple lane changes can be initiated), and according to the updated target cruise speed, the vehicle is controlled to safely drive to a drivable area, so that the vehicle safely passes through the front road abnormal area. At the same time, by controlling the instrument panel, head-up display (Head-Up Display, abbreviated as HUD) and voice prompt device in the vehicle, the reminder information is output to the driver, which is used to remind the driver that there is a road abnormal area in front of the vehicle and the current vehicle is driving in lane changing. If it is determined that the current vehicle does not meet the lane changing condition, after a preset time period, the reminder information is output to the driver by controlling the instrument panel, head-up display (Head-Up Display, abbreviated as HUD) and voice prompt device in the vehicle, which is used to remind the driver that there is a road abnormal area in front of the vehicle and the current vehicle needs to manually select a lane. At this time, the user responds in time and performs vehicle control operation, for example, slows down in advance, requests the adjacent vehicle to yield, to ensure the safety of the vehicle.

[0119] For example, in combination with Figure 3 , if the vehicle is in the construction lane, the construction preferred lane changing function can be triggered 500m in advance, the vehicle is controlled to change into a passable lane (multiple lane changes can be initiated), and the driver is reminded that "there is a construction area in front of the vehicle, and the preferred lane changing is being initiated". If the vehicle is in the construction lane and does not meet the activation condition of lane changing (because the vehicle has no lane changing space) for 10s, the driver is reminded that "there is a construction area in front of the vehicle, and the lane needs to be manually selected". Among them, Figure 4 is an example of a construction area preferred lane changing condition provided by an embodiment of the application Figure 1 , Figure 5 is an example of a construction area preferred lane changing condition provided by an embodiment of the application Figure 2 , Figure 6 is an example of a construction area preferred lane changing condition provided by an embodiment of the application Figure 3 , in combination with Figure 4 , 5, 6, according to the lane occupancy of the road abnormal area in the obtained updated map information, and in combination with the current position, speed and surrounding traffic state of the vehicle, the lane changing condition of the vehicle is judged, including that the current vehicle has lane changing space and can be changed into a passable lane; the lane changing condition of the vehicle also includes that the current vehicle has no lane changing space and cannot be changed into a passable lane; the lane changing condition of the vehicle also includes that the current vehicle does not need to change lanes, at this time, the vehicle is in a passable lane, and only the driver is reminded that “there is a construction area in front, please keep straight”. Among them, for Figure 4 、 5 , 6, “Ego” can identify the vehicle, “NPC” can identify other vehicles, and “T0”, “T1” and “T2” can respectively identify the driving positions of the vehicle.

[0120] 305, performing secondary scene verification processing during vehicle driving to control the vehicle to drive along the drivable area.

[0121] By way of example, this step can refer to step 204, which will not be described here.

[0122] In one possible implementation, step 305 includes: updating the relative position information of the vehicle according to the positioning information of the vehicle; if it is determined that the updated relative position information represents that the vehicle is currently in a passable lane within the second preset range of the front road abnormal area, updating the target cruise speed of the vehicle; and obtaining the vehicle-mounted perception information of the vehicle; determining the drivable area of the vehicle according to the vehicle-mounted perception information of the vehicle; controlling the vehicle to drive along the drivable area of the vehicle according to the updated target cruise speed; and generating fourth reminding information; wherein the fourth reminding information is used to remind the user to be careful when passing through the road abnormal area.

[0123] Specifically, after entering the first preset range and controlling the vehicle to travel according to the updated target cruise speed, the relative position information between the vehicle and the road area in front of the vehicle is updated according to the positioning information of the vehicle, and the updated relative position information is monitored in real time, which represents the relative position between the vehicle and the abnormal area in front of the road. According to the updated relative position information, if it is monitored that the vehicle is currently in a passable lane within the second preset range of the abnormal area in front of the road, the updated target cruise speed of the current vehicle is updated again, which can be further updated to another preset speed. At the same time, through the vehicle-mounted sensing device (radar, camera), the vehicle-mounted sensing information of the vehicle is collected, including the image information of the road area in front of the vehicle, and based on the sensing algorithm such as image recognition algorithm or target detection algorithm, the vehicle-mounted sensing information is processed, and the abnormal area in front of the vehicle is checked again, that is, the cone, water, construction card and other obstacles in the abnormal area in front of the road are identified, and the data containing the distribution of each obstacle, the recognition result of the image in the RTTI data and the crowd-sourced data, and the high-precision map data are mapped into a unified space-time coordinate system to generate the drivable area of the vehicle, and guide the vehicle to travel along the safe path. According to the updated target cruise speed, that is, another preset speed, the vehicle can be controlled to travel along the drivable area of the vehicle in advance to safely pass through the abnormal area in front of the road; at the same time, through the control of the instrument panel, head-up display (Head-Up Display, abbreviated as HUD) and voice prompt device in the vehicle, the warning information is output to the driver to remind the driver to be careful when passing through the abnormal area in front of the road. The second preset range in the embodiment and the sensing algorithm applied herein can be designed by the user according to the actual situation, which is not limited herein.

[0124] By relying on the cooperative processing of RTTI data and crowd-sourced image data sources, and combining the sensing algorithm to check the abnormal area in front of the road again, the recognition accuracy of the warning signs and obstacles in the abnormal area of the road can be improved in low light, rain and fog, etc. Environment, to ensure the safety of the vehicle passing through the abnormal area of the road.

[0125] For example, in combination with Figure 3 When the vehicle enters the passable lane of the construction state interval (200m in front of the construction card), the target cruise speed is updated to 60km / h (or the speed identified by the speed limit sign) to safely travel along the drivable area of the construction area, and at the same time, the driver is reminded to "construction area, please be careful when passing through".

[0126] In this embodiment, on the basis of the above embodiment, on the one hand, by unifying RTTI, image and map information in crowd-sourced data to the GPS timestamp coordinate system, information conflicts can be avoided, and data fusion reliability can be improved; on the other hand, by the lane occupancy in the road abnormal area, lane-level road abnormal area dynamic updating is realized, and the dynamic response lag problem caused by insufficient perception distance is avoided, so as to ensure vehicle driving safety.

[0127] Figure 7 A structural schematic diagram of a vehicle control device provided in the embodiment of the application is shown in FIG. 1, which comprises: Figure 7

[0128] The matching module 401 is configured to acquire map information of a scene where the vehicle is located in response to real-time traffic information of the scene, and match road abnormal area change data in the real-time traffic information with the map information, wherein the map information comprises at least lane-level precision geographical information.

[0129] The updating module 402 is configured to acquire updated map information from the cloud if it is determined that the road abnormal area change data and the map information do not match, wherein the updated map information is obtained by map reconstruction processing based on crowd-sourced data of the scene where the vehicle is located, and the updated map information comprises updated at least lane-level precision geographical information.

[0130] The first control module 403 is configured to control the vehicle to drive to a drivable area according to the updated map information.

[0131] The second control module 404 is configured to perform secondary scene verification processing during vehicle driving to control the vehicle to drive along the drivable area.

[0132] In a possible implementation, the first control module 403 is specifically configured to determine relative position information of the vehicle according to the updated map information and positioning information of the vehicle, wherein the relative position information represents a relative position condition between the vehicle and a front road abnormal area, and control the vehicle to drive to the drivable area according to the relative position information of the vehicle.

[0133] In a possible implementation, the first control module 403 is specifically configured to obtain road abnormal area information by fusing the updated map information and the real-time traffic information, and determine the relative position information of the vehicle according to the road abnormal area information and the positioning information of the vehicle.

[0134] ​In a possible implementation, the first control module 403 is further specifically configured to: if it is determined that the relative position information of the vehicle represents that the vehicle is currently in the first preset range of the abnormal road area ahead, update a target cruise speed of the vehicle; and generate first reminding information; wherein the first reminding information is used to remind a user that there is an abnormal road area ahead; and control the vehicle to travel to a drivable area according to the updated target cruise speed.

[0135] In a possible implementation, the first control module 403 is further specifically configured to: if it is determined that the vehicle is currently in an abnormally occupied lane, determine a lane-changing condition of the vehicle according to a driving speed of the vehicle and a surrounding traffic state; if it is determined that the lane-changing condition of the vehicle represents that the current vehicle meets a lane-changing condition, control the vehicle to continue to change lanes and travel to a drivable area according to the updated target cruise speed; and generate second reminding information; wherein the second reminding information is used to remind a user that there is an abnormal road area ahead and the current vehicle is changing lanes.

[0136] In a possible implementation, the first control module 403 is further specifically configured to: if it is determined that the lane-changing condition of the vehicle represents that the current vehicle does not meet the lane-changing condition, generate third reminding information after a preset time period; wherein the third reminding information is used to remind a user that there is an abnormal road area ahead and the current vehicle needs to manually select a lane.

[0137] In a possible implementation, the second control module 404 is specifically configured to: update relative position information of the vehicle according to positioning information of the vehicle; if it is determined that the updated relative position information represents that the vehicle is currently in a drivable lane within a second preset range of the abnormal road area ahead, update a target cruise speed of the vehicle; and obtain vehicle-mounted sensing information of the vehicle; determine a drivable area of the vehicle according to the vehicle-mounted sensing information of the vehicle; control the vehicle to travel along the drivable area of the vehicle according to the updated target cruise speed; and generate fourth reminding information; wherein the fourth reminding information is used to remind a user to be careful when passing through the abnormal road area.

[0138] In a possible implementation, the updated map information is obtained by performing image processing on road abnormal area image information in the crowdsourcing data to obtain lane occupancy information corresponding to the road abnormal area image information; and is obtained by performing update processing on the map information according to the lane occupancy information; wherein the updated map information includes lane occupancy of the abnormal road area ahead of the vehicle.

[0139] In a possible implementation, the update module 402 is specifically configured to: perform image recognition processing on the road abnormal area image information to obtain road element information in the road abnormal area image information; and perform semantic segmentation processing on the road abnormal area image information according to the road element information in the road abnormal area image information to obtain lane occupancy information corresponding to the road abnormal area image information.

[0140] In a possible implementation, the updating module 402 is further configured to perform associated updating processing on the basic lane information in the map information according to the lane occupancy information and the road abnormal area change data, and generate updated map information.

[0141] In a possible implementation, the apparatus is further configured to control the vehicle to travel according to the map information if it is determined that the real-time traffic information matches the map information.

[0142] The apparatus of the embodiment can perform the technical solutions in the above method, and the specific implementation process and technical principles are the same, which will not be repeated here.

[0143] Figure 8 A structural schematic diagram of an electronic device provided by the embodiment of the present application is shown in FIG. 1. Figure 8 As shown in FIG. 1, the electronic device includes a memory 501 and a processor 502. The memory 501 is configured to store the executable instructions of the processor 502.

[0144] The processor 502 is configured to perform the method provided by the above embodiment.

[0145] The electronic device further includes a receiver 503 and a transmitter 504. The receiver 503 is configured to receive the instructions and data sent by other devices, and the transmitter 504 is configured to send the instructions and data to external devices.

[0146] The specific implementation process of the processor can refer to the above method embodiments, and the implementation principles and technical effects are similar, which will not be repeated here.

[0147] In the above embodiments, it should be understood that the processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the disclosed method can be directly embodied as hardware processor execution, or executed by a combination of hardware and software modules in the processor.

[0148] The embodiment of the present application provides a vehicle, and the vehicle includes an electronic device configured to perform various possible embodiments as described above.

[0149] The embodiment of the present application further provides a computer readable storage medium, which stores computer execution instructions. When the computer execution instructions run on a computer, the computer is caused to execute the technical solutions of the above embodiment.

[0150] The readable storage medium can be implemented by any type of volatile or nonvolatile storage devices or a combination thereof, such as static random access memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, programmable read-only memory, read-only memory, magnetic storage, flash memory, magnetic disk or optical disk. The readable storage medium can be any available medium that can be accessed by a general or special purpose computer.

[0151] An exemplary readable storage medium is coupled to the processor, so that the processor can read information from the readable storage medium and write information to the readable storage medium. The readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in a special integrated circuit. The processor and the readable storage medium can also exist as discrete components in the device.

[0152] The embodiment of the present application further provides a computer program product, which includes a computer program stored in a computer readable storage medium. At least one processor can read the computer program from the computer readable storage medium, and the at least one processor executes the computer program to implement the technical solutions in the above embodiment.

[0153] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the present application that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of software products. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk and various media that can store program codes.

[0154] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware. The aforementioned program can be stored in a computer readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes a magnetic disk or an optical disk and various media that can store program codes.

[0155] It should be understood that many of the functional units described herein are merely illustrative of example configurations, and that these configurations and other configurations can be devised that are not specifically described or illustrated herein. Likewise, some configurations can be devised that are more complex than or differ from those described and illustrated herein. Although a particular number of components have been described and / or shown, it will be understood that additional or fewer components can be provided. It should also be understood that the various components can be implemented in hardware, software, firmware, or any combination thereof. It should further be understood that the various components can be implemented in different embodiments.

Claims

1. A vehicle control method characterized by, The method comprises: In response to real-time traffic information of a scene where a vehicle is located, map information of the scene where the vehicle is located is acquired; and road abnormal area change data in the real-time traffic information is matched with the map information; wherein the map information comprises geographical information of at least lane level accuracy; If it is determined that the road abnormal area change data and the map information do not match, updated map information is acquired from the cloud; wherein the updated map information is obtained through map reconstruction processing based on crowd-sourced data of the scene where the vehicle is located; and the updated map information comprises updated geographical information of at least lane level accuracy; The vehicle is controlled to travel to a drivable area according to the updated map information; Secondary scene verification processing is performed during the travel of the vehicle to control the vehicle to travel along the drivable area.

2. The method of claim 1, wherein, The vehicle is controlled to travel to a drivable area according to the updated map information, comprising: Relative position information of the vehicle is determined according to the updated map information and positioning information of the vehicle; wherein the relative position information represents a relative position condition between the vehicle and a front road abnormal area; The vehicle is controlled to travel to the drivable area according to the relative position information of the vehicle.

3. The method of claim 2, wherein, The relative position information of the vehicle is determined according to the updated map information and the positioning information of the vehicle, comprising: Road abnormal area information is obtained through fusion of the updated map information and the real-time traffic information; The relative position information of the vehicle is determined according to the road abnormal area information and the positioning information of the vehicle.

4. The method of claim 2, wherein, The vehicle is controlled to travel to the drivable area according to the relative position information of the vehicle, comprising: If it is determined that the relative position information of the vehicle represents that the vehicle is currently in a first preset range of the front road abnormal area, a target cruise speed of the vehicle is updated; and first reminder information is generated; wherein the first reminder information is used to remind a user that there is a road abnormal area in front; The vehicle is controlled to travel to the drivable area according to the updated target cruise speed.

5. The method of claim 4, wherein, The vehicle is controlled to travel to the drivable area according to the updated target cruise speed, comprising: If it is determined that the vehicle is currently in an abnormally occupied lane, a lane change condition of the vehicle is determined according to a travel speed of the vehicle and a surrounding traffic state; If it is determined that the lane change condition of the vehicle represents that the current vehicle meets a lane change condition, the vehicle is controlled to continue lane changing to travel to the drivable area according to the updated target cruise speed; and second reminder information is generated; wherein the second reminder information is used to remind a user that there is a road abnormal area in front and the current vehicle is lane changing.

6. The method of claim 5, wherein, The method further comprises: If it is determined that the lane change condition of the vehicle represents that the current vehicle does not meet a lane change condition, third reminder information is generated after a preset time period; wherein the third reminder information is used to remind a user that there is a road abnormal area in front and a lane needs to be manually selected at present.

7. The method of claim 1, wherein, The secondary scene verification processing is performed during the travel of the vehicle to control the vehicle to travel along the drivable area, comprising: updating relative position information of the vehicle according to the positioning information of the vehicle, wherein the relative position information represents a relative position condition between the vehicle and the abnormal road area in front of the vehicle; if it is determined that the updated relative position information represents that the vehicle is currently in a passable lane within a second preset range of the abnormal road area in front of the vehicle, updating a target cruise speed of the vehicle; and obtaining vehicle-mounted sensing information of the vehicle; determining a drivable area of the vehicle according to the vehicle-mounted sensing information of the vehicle; controlling the vehicle to travel along the drivable area of the vehicle according to the updated target cruise speed; and generating fourth reminding information, wherein the fourth reminding information is used to remind a user to be careful when passing through the abnormal road area.

8. The method of claim 1, wherein, the updated map information is obtained by image processing the abnormal road area image information in the crowd-sourced data to obtain lane occupancy information corresponding to the abnormal road area image information, and then updating the map information according to the lane occupancy information; wherein the updated map information includes lane occupancy conditions of the abnormal road area in front of the vehicle.

9. The method of claim 8, wherein, the image processing the abnormal road area image information to obtain the lane occupancy information corresponding to the abnormal road area image information includes: performing image recognition processing on the abnormal road area image information to obtain road element information in the abnormal road area image information; performing semantic segmentation processing on the abnormal road area image information according to the road element information in the abnormal road area image information to obtain the lane occupancy information corresponding to the abnormal road area image information.

10. The method of claim 8, wherein, the updating the map information according to the lane occupancy information includes: performing associated updating processing on basic lane information in the map information according to the lane occupancy information and the abnormal road area change data to generate the updated map information.

11. The method according to any one of claims 1-10, characterized in that, the method further includes: if it is determined that the real-time traffic information and the map information match, controlling the vehicle to travel according to the map information.

12. A vehicle control device characterized by comprising: includes: a matching module, configured to obtain map information of a scene in which a vehicle is located in response to real-time traffic information of the scene in which the vehicle is located; and match road abnormal area change data in the real-time traffic information with the map information; wherein the map information includes geographic information of at least lane level accuracy; an updating module, configured to obtain updated map information from a cloud if it is determined that the road abnormal area change data and the map information do not match; wherein the updated map information is obtained by performing map reconstruction processing based on crowd-sourced data of the scene in which the vehicle is located; and the updated map information includes updated geographic information of at least lane level accuracy; a first control module, configured to control the vehicle to travel to a drivable area according to the updated map information; a second control module, configured to perform secondary scene verification processing during travel of the vehicle to control the vehicle to travel along the drivable area.

13. An electronic device, comprising: includes: a memory and a processor; the memory stores computer execution instructions; The processor executes the computer-executed instructions stored in the memory, so that the processor executes the method as claimed in any one of claims 1-11.

14. A vehicle characterized by comprising: The vehicle comprises the electronic device as claimed in claim 13.

15. A computer readable storage medium / computer program product, characterized in that, The computer-readable storage medium stores computer-executed instructions, which, when executed by a processor, are used to implement the method as claimed in any one of claims 1-11; and / or the computer program product comprises a computer program, which, when executed by a processor, implements the method as claimed in any one of claims 1-11.

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