Vehicle control method, vehicle controller and medium

Abstract

The invention provides a vehicle control method, a vehicle controller and a medium. The method comprises the following steps: acquiring road index data of a target road, wherein the road index data is determined according to driving data at a target time point when a vehicle drives on the target road and variation range data representing the driving data at the target time point; and when the vehicle enters the target road, controlling the vehicle according to the road index data of the target road. According to the method, the driving planning effect is improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a vehicle control method, a vehicle controller, and a medium. Background Art

[0002] With the development of automobile electrification and intelligence, intelligent driving technology is becoming increasingly popular, and its application scenarios have expanded from cities and highways to complex road conditions such as rural roads and mountainous areas, demonstrating its potential in improving traffic safety and efficiency.

[0003] At present, existing intelligent driving solutions usually use multiple sensors (such as cameras, millimeter-wave radars, and lidars) for environmental perception, based on which vehicle driving trajectory planning and control instructions are issued to achieve intelligent driving.

[0004] However, existing intelligent driving solutions have the problem of poor driving planning effect when planning the driving conditions of the vehicle. Summary of the invention

[0005] The present application provides a vehicle control method, a vehicle controller, and a medium to solve the problem that the existing intelligent driving solutions have poor driving planning effects when planning the driving conditions of the vehicle.

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

[0007] Acquire road index data of a target road, where the road index data is determined based on driving data of a vehicle at a target time point when the vehicle is driving on the target road, and data representing a range of changes in the driving data at the target time point;

[0008] When the vehicle enters a target road, the vehicle is controlled according to the road index data of the target road.

[0009] In a possible implementation, obtaining road index data of a target road includes:

[0010] Determine the driving data and average driving data of the vehicle at a target time point on the target road;

[0011] According to the driving data of the vehicle at the target time point when driving on the target road and the average driving data, the change degree data representing the change degree of the driving data is obtained;

[0012] Determine the change range data according to the change degree data and the driving data of the vehicle on the target road at the target time point;

[0013] The road index data of the target road is determined based on the driving data and the variation range data of the vehicle at the target time point on the target road.

[0014] In a possible implementation, the variation range data satisfies:

[0015] bounds={(a1-s,a1+s),(a2-s,a2+s),…(a n -s,a n +s)};

[0016] Among them, a n Represents the driving data of the vehicle at the nth target time point when it enters the target road; s represents the degree of change data.

[0017] In a possible implementation manner, when the data type of the driving data is characterized by steering angle data of winding degree or undulation degree data, the undulation degree data represents the difference between the vehicle control acceleration and the chassis acceleration;

[0018] According to the driving data and variation range data of the vehicle at the target time point on the target road, the road index data of the target road is determined, including:

[0019] Determine, based on the driving data of the vehicle at the target time point on the target road, target driving data at the target time point, first target driving data having a first time relationship with the target driving data, and second target driving data having a second time relationship with the target driving data;

[0020] Determine the degree of deviation of the target driving data at the target time point according to the variation range data and the target driving data at the target time point;

[0021] Determine driving planning data at a target time point according to the target driving data, the first target driving data, the second target driving data and the degree of deviation;

[0022] According to the driving planning data at the target time point, the road index data of the target road is determined.

[0023] In a possible implementation, the driving planning data satisfies:

[0024] f(a)=w0∑a i +w1∑a' i +w2∑a″ i +w3∑(a i -0.5×(b u +b l ));

[0025] Among them, w0, w1, w2, and w3 are preset weights respectively; ∑a i Represents the target driving data at target time i, ∑a' i Represents the first target driving data at target time ii, ∑a″i Represents the second target driving data at the target time ii, ∑(a i -0.5×(b u +b l )) represents the degree of deviation at target time i, b u Characterizes the first extreme value in the variation range data, b l Characterizes the second extreme value in the variation range data.

[0026] In a possible implementation manner, when the data type of the driving data is characterized as data of the degree of bumpiness,

[0027] According to the driving data and variation range data of the vehicle at the target time point on the target road, the road index data of the target road is determined, including:

[0028] Determine target driving data at the target time point based on driving data of the vehicle on the target road at the target time point;

[0029] Determine the degree of deviation of the target driving data at the target time point according to the variation range data and the target driving data at the target time point;

[0030] Determine the driving planning data at the target time point based on the target driving data and the degree of deviation;

[0031] According to the driving planning data at the target time point, the road index data of the target road is determined.

[0032] In a possible implementation, the driving planning data satisfies:

[0033] f(a)=w0∑a i +w3∑(a i -0.5×(b u +b l ));

[0034] Among them, w0 and w3 are preset weights respectively; ∑a i Represents the target driving data at target time i, ∑(a i -0.5×(b u +b l )) represents the degree of deviation at the target time ii, b u Characterizes the first extreme value in the variation range data, b l Characterizes the second extreme value in the variation range data.

[0035] In a possible implementation manner, after obtaining the road indicator data of the target road, the method further includes:

[0036] Sending the road indicator data of the target road to the map server, so that the map server updates the map data of the target road according to the road planning data of the target road;

[0037] Receive and display the updated map data of the target road.

[0038] In a second aspect, the present application provides a vehicle control device, comprising:

[0039] An acquisition module, used to acquire road index data of a target road, the road index data being determined based on driving data at a target time point when the vehicle is driving on the target road, and data representing a variation range of the driving data at the target time point;

[0040] The control module is used to control the vehicle according to the road index data of the target road when the vehicle enters the target road.

[0041] In a third aspect, the present application provides a vehicle controller, comprising: a processor, and a memory communicatively connected to the processor;

[0042] Memory stores computer execution quality;

[0043] The processor executes the computer execution quality stored in the memory to implement the method of the embodiment of the present application.

[0044] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer execution quality is stored, and the computer execution quality is used to implement the method of the embodiment of the present application when the processor executes it.

[0045] In a fifth aspect, the present application provides a computer program product, including a computer program, which implements the method of any one of the first aspects when executed by a processor.

[0046] The vehicle control method, vehicle controller, and medium provided in the present application obtain driving data at each target time point when the vehicle is traveling on a target road, and combine the variation range data of the driving data at the corresponding target time point to determine the road index data that makes the vehicle travel more smoothly and comfortably on the target road. When the vehicle enters the target road, the vehicle is controlled according to the road index data of the target road, thereby improving the driving planning effect of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0048] Figure 1 A schematic diagram of a scenario of a vehicle control method provided in an embodiment of the present application;

[0049] Figure 2 Schematic diagram of the process of the vehicle control method provided in the embodiment of the present application Figure 1 ;

[0050] Figure 3 Schematic diagram of the process of the vehicle control method provided in the embodiment of the present application Figure 2 ;

[0051] Figure 4 A flowchart of another message processing method provided in an embodiment of the present application;

[0052] Figure 5 A schematic diagram of the structure of a message processing device provided in an embodiment of the present application;

[0053] Figure 6 A schematic diagram of the structure of a vehicle controller provided in an embodiment of the present application.

[0054] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0055] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0056] First, the terms involved in this application are explained:

[0057] Intelligent Driving, also known as Autonomous Driving or Self-driving, refers to the use of advanced sensors, computer vision, machine learning algorithms, control systems and other technologies to enable vehicles to complete driving tasks autonomously to varying degrees. The goal of intelligent driving is to enable vehicles to complete the driving process from starting point to end point safely and efficiently without the intervention of human drivers.

[0058] The existing intelligent driving control scheme usually obtains the vehicle position and the road information around it through the map and positioning system, and uses the on-board sensors (such as laser radar, camera, millimeter wave radar, etc.) to obtain the current state information of the vehicle, including position, speed, heading angle, etc. On this basis, a path search algorithm (such as A star algorithm, Dijkstra algorithm, etc.) is used to plan a feasible path and optimize it to meet the conditions such as traffic rules, vehicle dynamics constraints and traffic conditions. Finally, the optimized path is converted into a specific trajectory, that is, a series of discrete vehicle state points, including position, speed, acceleration, etc., and the vehicle is ensured to drive safely and smoothly by adjusting the steering, acceleration and braking control instructions of the vehicle in real time. However, the existing scheme mainly relies on perception to obtain the actual road conditions, and lacks effective response to complex conditions such as winding, undulating, potholes, etc. on the road, resulting in the lack of necessary information in the planning algorithm, which affects the driving comfort and safety. Therefore, it is necessary to propose an intelligent driving control design scheme based on special road conditions, which automatically adjusts the planned target speed through control and chassis data to improve driving comfort.

[0059] The vehicle control method, vehicle controller, and medium provided in the present application obtain driving data at each target time point when the vehicle is traveling on a target road, and combine the variation range data of the driving data at the corresponding target time point to determine the road index data that makes the vehicle travel more smoothly and comfortably on the target road. When the vehicle enters the target road, the vehicle is controlled according to the road index data of the target road, thereby improving the driving planning effect of the vehicle.

[0060] Figure 1 A schematic diagram of a scenario of a vehicle control method provided in an embodiment of the present application. Figure 1 As shown, the execution subject of a vehicle control method provided in an embodiment of the present application may be a vehicle control system. The vehicle control system may be a vehicle-mounted computer, a mobile phone, a tablet, or other devices. This embodiment does not impose any particular restrictions on the implementation method of the execution subject, as long as the execution subject can obtain the road index data of the target road, the road index data is determined based on the driving data at the target time point when the vehicle is driving on the target road, and the data representing the range of changes in the driving data at the target time point; when the vehicle enters the target road, the vehicle can be controlled based on the road index data of the target road.

[0061] The technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems are described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0062] Figure 2Schematic diagram of the process of the vehicle control method provided in the embodiment of the present application Figure 1 , the execution subject of this method can be a vehicle control system, such as Figure 2 As shown, the method may include:

[0063] S201. Obtain road index data of a target road, where the road index data is determined based on driving data of a vehicle at a target time point when the vehicle is driving on the target road, and data representing a range of changes in the driving data at the target time point.

[0064] The target road may refer to a road that the vehicle is about to enter.

[0065] Road index data may refer to data on how to control the vehicle to drive smoothly and comfortably on the target road after entering the target road. The road index data may include data from various sensors on the vehicle, such as the steering angle of the vehicle, the suspension height of the wheels or the frame, and other data.

[0066] In the embodiment of the present application, the road index data can be determined based on the driving data of the vehicle at the target time point when the vehicle is driving on the target road and the change range data at the target time point.

[0067] The target time point may refer to the driving time point when the vehicle enters the target road. There may be multiple driving time points, and they may be determined according to preset time intervals. For example, the target time point may be a time point with a time interval of 1 second, that is, the target time point may include 1 second, 2 seconds, 3 seconds, ..., until the target time point when the vehicle exits the target road.

[0068] The driving data at the target time point may be vehicle control data of the vehicle at the target time point, wherein the driving data at the target time point may be historical driving data of the current vehicle or driving data of other vehicles.

[0069] The transformation range data may refer to an adjustable range of the driving data, that is, since the driving data is inevitably affected by the external environment when it is collected, the corresponding transformation range data is set for the driving data at each target time point.

[0070] By using the driving data at each target time point and the corresponding transformation range data, the driving data of the vehicle at each target time point on the target road can be fitted and analyzed, and then the road index data of the target road can be determined. The road index data can be determined by the vehicle based on its own driving data, or by receiving driving data from other vehicles, or by other systems processing the obtained driving data and sending it to the vehicle control system through network transmission or other methods.

[0071] In the embodiment of the present application, obtaining the road index data of the target road includes:

[0072] Determine the driving data and average driving data of the vehicle at a target time point on the target road;

[0073] According to the driving data of the vehicle at the target time point when driving on the target road and the average driving data, the change degree data representing the change degree of the driving data is obtained;

[0074] Determine the change range data according to the change degree data and the driving data of the vehicle on the target road at the target time point;

[0075] The road index data of the target road is determined based on the driving data and the variation range data of the vehicle at the target time point on the target road.

[0076] The average driving data can be obtained by weighted averaging the driving data at each target time point.

[0077] The variation degree data may refer to the degree of dispersion of the driving data at each target time point relative to the average driving data. In the embodiment of the present application, the variation degree data satisfies:

[0078]

[0079] in, is the average driving data, a i is the driving data of each target time point ii, and n is the number of target time points.

[0080] The variation range data may refer to the variation range of the driving data at each target time point determined according to the variation degree data. In the embodiment of the present application, the variation range data satisfies:

[0081] bounds={(a1-s,a1+s),(a2-s,a2+s),…(a n -s,a n +s)};

[0082] Among them, a n Represents the driving data of the vehicle at the nth target time point when it enters the target road; s represents the degree of change data.

[0083] In the embodiment of the present application, when the data type of the driving data is characterized by steering angle data or undulation data of the winding degree, the undulation data represents the difference between the vehicle control acceleration and the chassis acceleration;

[0084] According to the driving data and variation range data of the vehicle at the target time point on the target road, the road index data of the target road is determined, including:

[0085] Determine, based on the driving data of the vehicle at the target time point on the target road, target driving data at the target time point, first target driving data having a first time relationship with the target driving data, and second target driving data having a second time relationship with the target driving data;

[0086] Determine the degree of deviation of the target driving data at the target time point according to the variation range data and the target driving data at the target time point;

[0087] Determine driving planning data at a target time point according to the target driving data, the first target driving data, the second target driving data and the degree of deviation;

[0088] According to the driving planning data at the target time point, the road index data of the target road is determined.

[0089] The steering angle data may refer to the steering angle of the car, which may refer to the deflection angle of the front wheels of the car relative to the longitudinal center line of the car body. The curvature of the path when the car turns, that is, the size of the turning radius, can be determined by the steering angle of the car. The larger the steering angle, the smaller the radius when the car turns, and the smaller the steering angle, the larger the radius when the car turns. The change in the steering angle data can determine the winding degree of the target road.

[0090] Vehicle control acceleration usually refers to the driver's ability to control the acceleration or deceleration of the vehicle through operations such as the accelerator and brake, while chassis acceleration refers to the actual acceleration experienced by the vehicle chassis during driving, including but not limited to linear acceleration and angular acceleration. The difference between vehicle control acceleration and chassis acceleration can represent the degree of undulation of the target road when the vehicle is driving.

[0091] In order to make the vehicle control smoother and more stable when driving on the target road, after determining the steering angle data or undulation data characterizing the vehicle, the minimum data at each target time point is selected, that is, when the driving data is steering angle data, the target driving data is the minimum steering angle data at the corresponding target time point.

[0092] In addition to considering the basic driving data, the corresponding first target driving data and second target driving data can also be determined according to the time relationship, where:

[0093] The first target driving data may be data obtained by derivation of the target driving data according to time. For example, when the target driving data is minimum steering angle data, the first target driving data is steering angle velocity data. When the target driving data is minimum difference data, the first target driving data is difference change velocity data.

[0094] The second target driving data may be data obtained by derivation of the first target driving data according to time. For example, when the target driving data is minimum steering angle data, the first target driving data is steering angle acceleration data. When the target driving data is minimum difference data, the first target driving data is difference change acceleration data.

[0095] The driving planning data may refer to data corresponding to a target time point. According to the driving planning data at multiple target time points on the target road, the road indicator data of the target road may be obtained.

[0096] Among them, in the embodiment of the present application, the driving planning data satisfies:

[0097] f(a)=w0∑a i +w1∑a' i +w2∑a″ i +w3∑(a i -0.5×(b u +b l ));

[0098] Among them, w0, w1, w2, and w3 are preset weights respectively; ∑a i Represents the target driving data at target time i, ∑a' i Represents the first target driving data at target time i, ∑a″ i Represents the second target driving data at the target time ii, ∑(a i -0.5×(b u +b l )) represents the degree of deviation at target time i, b u Characterizes the first extreme value in the variation range data, b l Characterizes the second extreme value in the variation range data.

[0099] Among them, w0, w1, w2, and w3 can be automatically set as needed. For example, in order to ensure smoother steering of the vehicle, when determining ∑a i ,∑a' i ,∑a″ i and∑(a i -0.5×(b u +b l )), the driving planning data at each target time point can be made to change more smoothly by adjusting w0, w1, w2, and w3.

[0100] The first extreme value may be a maximum boundary value in the range change data, and the second extreme value may be a minimum boundary value in the range change data.

[0101] By setting the degree of deviation, the vehicle's speed changes can be ensured to be smoother.

[0102] In this embodiment of the present application, when the data type of the driving data is characterized by data on the degree of bumpiness,

[0103] According to the driving data and variation range data of the vehicle at the target time point on the target road, the road index data of the target road is determined, including:

[0104] Determine target driving data at the target time point based on driving data of the vehicle on the target road at the target time point;

[0105] Determine the degree of deviation of the target driving data at the target time point according to the variation range data and the target driving data at the target time point;

[0106] Determine the driving planning data at the target time point based on the target driving data and the degree of deviation;

[0107] According to the driving planning data at the target time point, the road index data of the target road is determined.

[0108] The driving data characterized by the degree of bumpiness may refer to the suspension height change data, which may refer to the height change of the vehicle chassis relative to the ground or a reference surface. The greater the change in the suspension height change data, the higher the degree of bumpiness of the vehicle.

[0109] For the degree of vehicle bumpiness, only the change in the control suspension height needs to be considered. Therefore, when determining the road index data on the degree of bumpiness, the suspension height change data can be given priority. When the data type of the driving data is characterized by the data on the degree of bumpiness, the target driving data can be the minimum suspension height change data at the target time point.

[0110] Among them, in the embodiment of the present application, the driving planning data satisfies:

[0111] f(a)=w0∑a i +w3∑(a i -0.5×(b u +b l ));

[0112] Among them, w0 and w3 are preset weights respectively; ∑a i Represents the target driving data at target time i, ∑(a i -0.5×(b u +b l )) represents the degree of deviation at the target time ii, b u Characterizes the first extreme value in the variation range data, b l Characterizes the second extreme value in the variation range data.

[0113] Among them, w0 and w3 can be automatically set as needed. For example, in order to ensure smoother steering of the vehicle, when determining ∑a i and∑(a i -0.5×(b u +b l )), the driving planning data at each target time point can be made to change more smoothly by adjusting w0 and w3.

[0114] The first extreme value may be a maximum boundary value in the range change data, and the second extreme value may be a minimum boundary value in the range change data.

[0115] By setting the degree of deviation, the vehicle suspension height can be ensured to change more smoothly.

[0116] In an embodiment of the present application, driving data characterized by the degree of bumpiness may also refer to road condition data of a target road, wherein the road condition data of a target road may be determined based on the conditions of the road scanned by sensors such as a lidar and a camera on the vehicle. For example, the conditions of the scanned road may be first processed by computer vision using an image sensor to identify various objects on the road and obtain features of the road surface. The road surface point cloud of the lidar is then combined with the features of the road surface extracted by the image sensor to establish a road model. By analyzing parameters such as height changes, slope, and curvature of the road model, road condition data characterizing changes in the road conditions may be obtained.

[0117] S202: When the vehicle enters a target road, the vehicle is controlled according to road indicator data of the target road.

[0118] Among them, after the vehicle enters the target road, the vehicle control system of the vehicle can adaptively adjust the vehicle speed and other control parameters according to the data in the road index data, so that the vehicle can pass the target road stably.

[0119] The vehicle control method provided in the embodiment of the present application can improve the description ability of the intelligent driving system in the vehicle control system to the actual environment by evaluating the road index data based on the control and chassis data, provide more basis for path planning, and realize adaptive adjustment of the target vehicle speed, thereby improving the stability and comfort of vehicle driving. At the same time, the vehicle control method provided in the embodiment of the present application can also avoid the limitations of relying solely on sensors to describe the overall road environment, and continuously optimize the map data through real-time vehicle recognition and updating of road indexes, providing more accurate guidance for other intelligent driving vehicles driving on the same road section.

[0120] Figure 3 Schematic diagram of the process of the vehicle control method provided in the embodiment of the present application Figure 2, the execution subject of this method can be a vehicle control system, such as Figure 3 As shown, the method may include:

[0121] S301, obtaining all road index data of the target road;

[0122] S302: Perform weighted averaging processing on all road index data to obtain road index data of the target road.

[0123] Among them, when there are multiple road index data, they can be fused and calculated through smoothing, weighted calculation and other operations to obtain the overall index of the target road section, and then the vehicle can be controlled based on the overall index.

[0124] In the embodiment of the present application, after obtaining the road index data of the target road, the method further includes:

[0125] Sending the road indicator data of the target road to the map server, so that the map server updates the map data of the target road according to the road planning data of the target road;

[0126] Receive and display the updated map data of the target road.

[0127] After obtaining the road index data of the target road, the vehicle control system can also send it to the map server to facilitate the call of the navigation application. After the navigation application is called, the navigation application can also update the map data according to the road index data, and after the update, it is displayed to the driver through display and voice output. At the same time, when the driving data representing the degree of bumpiness in the road index data also includes the road condition data of the target road, the updated map data of the target road can also include the road condition data of the target road. Therefore, when the navigation route of the vehicle or other vehicles includes the target road, the map program corresponding to the map server can prompt the driver of the road condition of the target road, so that the driver can decide whether to drive on the target road, or when the vehicle or other vehicles are already on the target road, the map program corresponding to the map server can also output the road condition of the target road to the driver to assist the driver in driving.

[0128] Figure 4 A flowchart of another message processing method provided by this application is as follows: Figure 4As shown, the method includes: encapsulating the road index, wherein the road index includes a road turning index obtained by the degree of steering change, a road bump index obtained by the degree of suspension data change, and a road undulation index obtained by the control and chassis data difference; sending the encapsulated road index to the map server and the vehicle controller respectively, wherein when the encapsulated road index is sent to the map server, the road index can be matched to the road section in the map according to the location, and then the road index under the same road section can be sorted in chronological order according to time, and the road index within a period of time can be fused to obtain a comprehensive road index and update the map, and finally the fused comprehensive road index is transmitted over the network, so that the vehicle controller controls the vehicle according to the fused comprehensive road index; when the encapsulated road index is sent directly to the vehicle controller, the road index can be fused, and then the vehicle is controlled according to the fused road index.

[0129] Among them, the road index is encapsulated, including recording the time, location (latitude and longitude information) and specific index value of the road index. The time information is used to determine the timeliness of the data, and the location information is used to accurately locate the specific location on the road. The encapsulated road index data is uploaded to the cloud server through the network. Using the location information of the uploaded data, each data point is accurately matched to the corresponding road section. For multiple road indexes of the same section, fusion calculations are performed through smoothing, weighted calculations and other operations to obtain the overall index of the section. Finally, the fused road index data is updated to the map data to ensure that the latest road index information can be called by the map application.

[0130] Figure 5 A schematic diagram of the structure of a message processing device provided in an embodiment of the present application, such as Figure 5 As shown, the message processing device 50 provided in this embodiment includes:

[0131] An acquisition module 501 is used to acquire road index data of a target road, where the road index data is determined based on driving data of a vehicle at a target time point when the vehicle is driving on the target road, and data representing a variation range of the driving data at the target time point;

[0132] The control module 502 is used to control the vehicle according to the road indicator data of the target road when the vehicle enters the target road.

[0133] In a possible implementation, the acquisition module 501 may be specifically used for:

[0134] Determine the driving data and average driving data of the vehicle at a target time point on the target road;

[0135] According to the driving data of the vehicle at the target time point when driving on the target road and the average driving data, the change degree data representing the change degree of the driving data is obtained;

[0136] Determine the change range data according to the change degree data and the driving data of the vehicle on the target road at the target time point;

[0137] The road index data of the target road is determined based on the driving data and the variation range data of the vehicle at the target time point on the target road.

[0138] Among them, the variation range data satisfies:

[0139] bounds={(a1-s,a1+s),(a2-s,a2+s),…(a n -s,a n +s)};

[0140] Among them, a n Represents the driving data of the vehicle at the nth target time point when it enters the target road; s represents the degree of change data.

[0141] In a possible implementation, the acquisition module 501 may be specifically used for:

[0142] According to the driving data and variation range data of the vehicle at the target time point on the target road, the road index data of the target road is determined, including:

[0143] Determine, based on the driving data of the vehicle at the target time point on the target road, target driving data at the target time point, first target driving data having a first time relationship with the target driving data, and second target driving data having a second time relationship with the target driving data;

[0144] Determine the degree of deviation of the target driving data at the target time point according to the variation range data and the target driving data at the target time point;

[0145] Determine driving planning data at a target time point according to the target driving data, the first target driving data, the second target driving data and the degree of deviation;

[0146] According to the driving planning data at the target time point, the road index data of the target road is determined.

[0147] Among them, the driving planning data meets the following requirements:

[0148] f(a)=w0∑a i +w1∑a' i +w2∑a″ i +w3∑(a i -0.5×(b u +bl ));

[0149] Among them, w0, w1, w2, and w3 are preset weights respectively; ∑a i Represents the target driving data at target time i, ∑a' i Represents the first target driving data at target time ii, ∑a″ i Represents the second target driving data at target time i, ∑(a i -0.5×(b u +b l )) represents the degree of deviation at the target time ii, b u Characterizes the first extreme value in the variation range data, b l Characterizes the second extreme value in the variation range data.

[0150] In a possible implementation, when the data type of the driving data is characterized as data on the degree of bumpiness, the acquisition module 501 may be specifically used to:

[0151] Determine target driving data at the target time point based on driving data of the vehicle on the target road at the target time point;

[0152] Determine the degree of deviation of the target driving data at the target time point according to the variation range data and the target driving data at the target time point;

[0153] Determine the driving planning data at the target time point based on the target driving data and the degree of deviation;

[0154] According to the driving planning data at the target time point, the road index data of the target road is determined.

[0155] Among them, the driving planning data meets the following requirements:

[0156] f(a)=w0∑a i +w3∑(a i -0.5×(b u +b l ));

[0157] Among them, w0 and w3 are preset weights respectively; ∑a i Represents the target driving data at target time i, ∑(a i -0.5×(b u +b l )) represents the degree of deviation at the target time ii, b u Characterizes the first extreme value in the variation range data, b l Characterizes the second extreme value in the variation range data.

[0158] In a possible implementation, when there are more than two pieces of road indicator data for the target road, the acquisition module 501 may be specifically used to:

[0159] Obtain all road indicator data of the target road;

[0160] All road index data are weighted and averaged to obtain the road index data of the target road.

[0161] In a possible implementation, the acquisition module 501 may be specifically used for:

[0162] Sending the road indicator data of the target road to the map server, so that the map server updates the map data of the target road according to the road planning data of the target road;

[0163] Receive and display the updated map data of the target road.

[0164] The vehicle control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail in this embodiment.

[0165] Figure 6 This is a schematic diagram of the structure of the vehicle controller provided in the embodiment of the present application. Figure 6 As shown, the vehicle controller 60 provided in this embodiment includes: at least one processor 601 and a memory 602. Optionally, the vehicle controller 60 also includes a communication component 603. The processor 601, the memory 602 and the communication component 603 are connected via a bus 604.

[0166] In a specific implementation process, at least one processor 601 executes the computer execution instructions stored in the memory 602, so that at least one processor 601 executes the above method.

[0167] The specific implementation process of the processor 601 can be found in the above method embodiment, and its implementation principle and technical effect are similar, so this embodiment will not be repeated here.

[0168] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0169] The memory may include a high-speed memory (Random Access Memory, RAM), and may also include a non-volatile memory (Non-volatile Memory, NVM), such as at least one disk memory.

[0170] The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, the bus in the drawings of the present application is not limited to only one bus or one type of bus.

[0171] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0172] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0173] The above-mentioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, 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.

[0174] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0175] The division of units is only a logical function division, and there may be other divisions in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or unit, which can be electrical, mechanical or other forms.

[0176] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0178] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0179] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. 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: ROM, RAM, disk or optical disk and other media that can store program codes.

[0180] Finally, it should be noted that those skilled in the art will readily conceive of other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses or adaptations of the present invention, which follow the general principles of the present invention and include common knowledge or customary technical means in the art not disclosed by the present invention, are not limited to the precise structure described above and shown in the drawings, and may be modified and changed in various ways without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A vehicle control method, characterized in that: The method comprises: Acquire road index data of a target road, wherein the road index data is determined according to driving data of the vehicle at a target time point when the vehicle is traveling on the target road, and data representing a variation range of the driving data at the target time point; When the vehicle enters a target road, the vehicle is controlled according to road index data of the target road.

2. The method according to claim 1, characterized in that The step of obtaining the road index data of the target road includes: Determine the driving data and average driving data of the vehicle at a target time point when the vehicle is traveling on the target road; Obtaining change degree data representing the change degree of the driving data according to the driving data of the vehicle at the target time point and the average driving data when the vehicle is driving on the target road; Determine the change range data according to the change degree data and the driving data of the vehicle driving on the target road at a target time point; The road index data of the target road is determined according to the driving data of the vehicle at a target time point on the target road and the variation range data.

3. The method according to claim 2, characterized in that The variation range data satisfies: bounds={(a1-s,a1+s),(a2-s,a2+s),…(a n -s,a n +s)}; Among them, the a n Represents the driving data of the vehicle at the nth target time point when the vehicle enters the target road; the s represents the change degree data.

4. The method according to claim 2, characterized in that: When the data type of the driving data is characterized by steering angle data of winding degree or undulation degree data, the undulation degree data represents the difference between the vehicle control acceleration and the chassis acceleration; Determining the road index data of the target road according to the driving data of the vehicle at the target time point and the variation range data comprises: Determine, according to the driving data of the vehicle at a target time point when driving on the target road, target driving data at the target time point, first target driving data having a first time relationship with the target driving data, and second target driving data having a second time relationship with the target driving data; Determining a degree of deviation of the target driving data at the target time point according to the variation range data and the target driving data at the target time point; Determining the driving planning data at the target time point according to the target driving data, the first target driving data, the second target driving data and the degree of deviation; The road index data of the target road is determined according to the driving planning data at the target time point.

5. The method according to claim 4, characterized in that The driving planning data meets the following requirements: f(a)=w0∑a i +w1∑a' i +w2∑a” i +w3∑(a i -0.5×(b u +b l )); Wherein, w0, w1, w2, and w3 are respectively preset weights; ∑a i Represents the target driving data at the target time ii, the ∑a' i Characterizes the first target driving data at the target time ii, the ∑a" i Represents the second target driving data at the target time ii, the ∑(a i -0.5×(b u +b l )) characterizes the degree of deviation at the target time ii, wherein b u Characterizes the first extreme value in the variation range data, the b l Characterizes the second extreme value in the variation range data.

6. The method according to claim 2, characterized in that When the data type of the driving data is characterized as data of the degree of bumpiness, Determining the road index data of the target road according to the driving data of the vehicle at the target time point and the variation range data comprises: Determine target driving data at the target time point based on driving data of the vehicle at the target time point on the target road; Determining a degree of deviation of the target driving data at the target time point according to the variation range data and the target driving data at the target time point; Determining the driving planning data at the target time point according to the target driving data and the degree of deviation; The road index data of the target road is determined according to the driving planning data at the target time point.

7. The method according to claim 6, characterized in that The driving planning data meets the following requirements: f(a)=w0∑a i +w3∑(a i -0.5×(b u +b l )); Wherein, w0 and w3 are respectively preset weights; ∑a i represents the target driving data at the target time i, the ∑(a i -0.5×(b u +b l )) characterizes the degree of deviation at the target time i, and the b u Characterizes the first extreme value in the variation range data, the b l Characterizes the second extreme value in the variation range data.

8. The method according to claim 1, characterized in that After obtaining the road index data of the target road, the method further includes: Sending the road indicator data of the target road to a map server, so that the map server updates the map data of the target road according to the road planning data of the target road; Receive and display updated map data of the target road.

9. A vehicle controller, characterized in that: include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 8 when executed by a processor.

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