Vehicle control method and device, vehicle and storage medium

Abstract

The invention provides a vehicle control method and device, a vehicle and a storage medium, and relates to the technical field of driving, and the scheme comprises the following steps: obtaining navigation data of the vehicle under the condition of detecting that the vehicle starts an auxiliary driving function; wherein the navigation data comprises data of a plurality of passing points in the driving path; the data of the passing points are used for reflecting driving conditions of the vehicle at the passing points; detecting whether abnormal data exists in the data of the plurality of passing points or not; if the abnormal data exist, performing moving average processing on the abnormal data to obtain updated data of the plurality of passing points; and controlling the vehicle to execute an auxiliary driving function according to the updated data of the plurality of passing points. According to the scheme, stable running of the vehicle can be achieved in the auxiliary driving process.

Description

Technical Field

[0001] The present application relates to the field of driving technology, and more specifically, to a vehicle control method, device, vehicle and storage medium in the field of driving technology. Background Art

[0002] At present, vehicles can usually use navigation data provided by navigation service platforms (e.g., Electronic Horizon Provider (EHP)) for assisted driving (assisting users to control the vehicle to drive automatically). However, due to regulatory restrictions, untimely updates of navigation information, etc., the data of a certain waypoint in the navigation data provided by the Electronic Horizon Provider (e.g., the historical average driving speed when passing a certain waypoint) contains some abnormal data such as distortion and mutation, which may cause the vehicle to make wrong decisions during driving, resulting in abnormal driving of the vehicle; for example, the vehicle may suddenly accelerate, decelerate, or drive at an unreasonable speed; this will affect the user's riding experience and even cause traffic accidents.

[0003] Therefore, how to achieve smooth driving of the vehicle during assisted driving is a technical problem that urgently needs to be solved. Summary of the invention

[0004] The present application provides a vehicle control method, device, vehicle and storage medium, wherein the method is used to achieve smooth driving of the vehicle during assisted driving.

[0005] In a first aspect, a vehicle control method is provided, the method comprising: obtaining navigation data of the vehicle when detecting that an assisted driving function is turned on for the vehicle; wherein the navigation data comprises data of a plurality of waypoints in a driving route; the data of the waypoints are used to reflect the driving condition of the vehicle at the waypoints; detecting whether there is abnormal data in the data of the plurality of waypoints; if there is abnormal data, obtaining updated data of the plurality of waypoints by performing sliding average processing on the abnormal data; and controlling the vehicle to perform the assisted driving function according to the updated data of the plurality of waypoints.

[0006] In an embodiment of the present application, when data of multiple waypoints in a driving path are obtained, the driving condition of the vehicle at each waypoint on the driving path can be preliminarily obtained; by detecting whether there is abnormal data in the data of multiple waypoints, it is possible to detect whether there is distorted or mutated waypoint data in the navigation data; if there is abnormal data, the abnormal data is processed by sliding average to obtain updated data of multiple waypoints; and based on the updated data of multiple waypoints, the vehicle is controlled to perform an assisted driving function, thereby reducing the current situation where the vehicle makes decisions incorrectly during driving, resulting in abnormal vehicle driving; based on this, the embodiment of the present application can achieve smooth driving of the vehicle during assisted driving.

[0007] In combination with the first aspect, in certain implementations of the first aspect, detecting whether there is abnormal data in the data of multiple waypoints includes: obtaining a first difference between the data of two adjacent waypoints among the multiple waypoints; determining whether there is a second difference in the first difference; wherein the second difference is greater than the first difference threshold; if the second difference exists, treating the data of the second waypoint among the two adjacent waypoints corresponding to the second difference as abnormal data.

[0008] In an embodiment of the present application, abnormal data is detected by using the difference between data of two adjacent waypoints, which can improve the accuracy of detecting abnormal data and enable subsequent processing of the abnormal data.

[0009] In combination with the first aspect and the above-mentioned implementation methods, in some implementation methods of the first aspect, the data of multiple updated waypoints are obtained by performing sliding average processing on the abnormal data, including: determining the type of abnormal data based on the distance between two adjacent waypoints corresponding to the second difference; and performing sliding average processing on the abnormal data based on the type of the abnormal data to obtain the data of multiple updated waypoints.

[0010] In the embodiment of the present application, the abnormal data can be processed in different ways according to the type of abnormal data, so that the abnormal data can be effectively processed to achieve smooth driving of the vehicle.

[0011] In combination with the first aspect and the above-mentioned implementation manner, in some implementation manners of the first aspect, the type of abnormal data is determined according to the distance between two adjacent waypoints corresponding to the second difference, including: if the distance between two adjacent waypoints corresponding to the second difference is greater than the second difference threshold, the type of abnormal data is determined to be a steady-state data type; if the distance between two adjacent waypoints corresponding to the second difference is less than or equal to the second difference threshold, the type of abnormal data is determined to be a noise data type; according to the type of abnormal data, sliding average processing is performed on the abnormal data to obtain updated data of multiple waypoints, including: if the type of abnormal data is a steady-state data type, weighted average processing is performed on the abnormal data; if the type of abnormal data is a noise data type, equal-weight average processing is performed on the abnormal data.

[0012] In an embodiment of the present application, the type of abnormal data is determined by the distance between the waypoints, so that the accuracy of data judgment can be improved, and the abnormal data can be processed in different ways, thereby being able to effectively process the abnormal data to achieve smooth driving of the vehicle.

[0013] In combination with the first aspect and the above-mentioned implementation methods, in some implementation methods of the first aspect, the data of multiple waypoints after update are obtained by performing sliding average processing on the abnormal data, including: obtaining a preset sliding window value for the abnormal data; according to the preset sliding window value, obtaining a first waypoint that meets a preset number from multiple waypoints; wherein the data of the first waypoint is located around the abnormal data; according to the data of the preset number of first waypoints and the abnormal data, performing sliding average processing on the abnormal data to obtain the data of multiple waypoints after update.

[0014] In an embodiment of the present application, by performing sliding average processing on abnormal data, smoothing and stabilization of data of multiple waypoints can be achieved.

[0015] In combination with the first aspect and the above-mentioned implementation methods, in some implementation methods of the first aspect, obtaining the navigation data of the vehicle includes: obtaining initial navigation data and a preset length of the vehicle; wherein the initial navigation data includes data of multiple initial waypoints in the driving path; according to the preset length, obtaining multiple waypoints from the multiple initial waypoints to obtain navigation data; wherein the distance between two adjacent waypoints from the multiple waypoints is the preset length.

[0016] In an embodiment of the present application, multiple waypoints are obtained from the initial navigation data according to a preset length, thereby reducing the amount of data to be processed and thereby reducing memory usage.

[0017] In combination with the first aspect and the above-mentioned implementation methods, in certain implementation methods of the first aspect, the data of the waypoint includes: a first driving speed of the vehicle when passing through the waypoint; a second driving speed of the vehicle when passing through the waypoint; at least one of the road slope when the vehicle passes through the waypoint; the first driving speed is the average speed of other vehicles when passing through the waypoint within a historical preset time period; the second driving speed is the average speed of other vehicles when passing through the waypoint at a historical preset time.

[0018] In the embodiments of the present application, since the data of the waypoints may include at least one type of data, the accuracy of understanding the road conditions of the driving path can be improved, so that the vehicle can be controlled to travel stably and safely on different types of driving paths.

[0019] In a second aspect, a vehicle control device is provided, which includes: an acquisition module, which is used to acquire navigation data of the vehicle when it is detected that the vehicle has turned on an assisted driving function; wherein the navigation data includes data of multiple waypoints in the driving path; the data of the waypoints are used to reflect the driving conditions of the vehicle at the waypoints; a detection module, which is used to detect whether there is abnormal data in the data of multiple waypoints; a processing module, which is used to obtain updated data of multiple waypoints by performing sliding average processing on the abnormal data if there is abnormal data; and a control module, which is used to control the vehicle to perform the assisted driving function according to the updated data of multiple waypoints.

[0020] In a possible implementation, the detection module is further used to obtain a first difference between data of two adjacent waypoints among multiple waypoints; determine whether a second difference exists in the first difference; wherein the second difference is greater than a first difference threshold; if a second difference exists, the data of the second waypoint among the two adjacent waypoints corresponding to the second difference is taken as abnormal data.

[0021] In a possible implementation, the processing module is further used to determine the type of abnormal data based on the distance between two adjacent waypoints corresponding to the second difference; and perform sliding average processing on the abnormal data according to the type of the abnormal data to obtain updated data of multiple waypoints.

[0022] In a possible implementation, the detection module is further configured to determine that the type of the abnormal data is a steady-state data type if the distance between two adjacent waypoints corresponding to the second difference is greater than a second difference threshold; and determine that the type of the abnormal data is a noise data type if the distance between two adjacent waypoints corresponding to the second difference is less than or equal to the second difference threshold;

[0023] In a possible implementation, the processing module is further configured to perform weighted averaging processing on the abnormal data if the type of the abnormal data is a steady-state data type; and perform equal-weighted averaging processing on the abnormal data if the type of the abnormal data is a noise data type.

[0024] In a possible implementation, the processing module is further used to obtain a preset sliding window value for abnormal data; according to the preset sliding window value, obtain a first waypoint that meets a preset number from multiple waypoints; wherein the data of the first waypoint is located around the abnormal data; based on the data of the preset number of first waypoints and the abnormal data, perform sliding average processing on the abnormal data to obtain updated data of multiple waypoints.

[0025] In a possible implementation, the acquisition module is also used to acquire initial navigation data and a preset length of the vehicle; wherein the initial navigation data includes data of multiple initial waypoints in the driving path; according to the preset length, multiple waypoints are acquired from the multiple initial waypoints to obtain navigation data; wherein the distance between two adjacent waypoints from the multiple waypoints is the preset length.

[0026] In a third aspect, a vehicle is provided, comprising a memory and a processor. The memory is used to store executable program code, and the processor is used to call and run the executable program code from the memory, so that the vehicle executes the method in the first aspect or any possible implementation of the first aspect.

[0027] In a fourth aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any possible implementation of the first aspect.

[0028] In a fifth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation manner of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a schematic diagram of a vehicle driving scenario provided by an embodiment of the present application;

[0030] Figure 2 is a flow chart of a vehicle control method provided in an embodiment of the present application;

[0031] Figure 3 is a schematic diagram of a vehicle driving scenario provided by an embodiment of the present application;

[0032] Figure 4is a schematic diagram of another vehicle driving scenario provided in an embodiment of the present application;

[0033] Figure 5 is a flow chart of another vehicle control method provided in an embodiment of the present application;

[0034] Figure 6 It is a structural schematic diagram of a vehicle control method device provided in an embodiment of the present application;

[0035] Figure 7 It is a structural schematic diagram of a vehicle provided in an embodiment of the present application. DETAILED DESCRIPTION

[0036] The technical solution in the present application will be described clearly and in detail below in conjunction with the accompanying drawings. In the description of the embodiments of the present application, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, "multiple" means two or more than two.

[0037] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0038] Before introducing the embodiments of the present application, the relevant terms involved in the present application are first explained as follows:

[0039] (1) Electronic horizon provider: plays an important role in autonomous driving and advanced driver assistance systems. It can provide vehicles with real-time, high-precision navigation data, such as each waypoint on the driving path and data of each waypoint (e.g., road conditions, historical average speed, etc.).

[0040] (2) Sliding average processing: A common smoothing technique in data processing that smoothes the original data sequence by calculating the average value of data points in a fixed-size window (sliding window). This window slides along the data sequence step by step, and each time the average value of the data in the current window is calculated and output as the smoothed data point. Sliding average processing can reduce random noise and short-term fluctuations in the data while retaining long-term trends. Sliding average algorithms include weighted average algorithm and equal weight average algorithm.

[0041] A. Weighted average algorithm: In the weighted average algorithm, each data point is assigned a different weight according to its importance or credibility. Data points with higher weights contribute more to the average, while data points with lower weights contribute less. When there are significant differences in the data or some data points are more reliable, the weighted average algorithm can more flexibly reflect the actual distribution of the data.

[0042] B. Equal Weighted Average Algorithm: In the equal weighted average algorithm, all data points are given the same weight when calculating the average. That is, each data point contributes equally to the average. The algorithm is simple to implement and does not require additional weight information. However, since all data points are treated equally, this method may not effectively reflect the true differences or noise levels in the data.

[0043] (3) Traffic flow speed: also known as dynamic traffic speed; refers to the speed of vehicles traveling on a certain road section or a certain point on the road section under real-time or near real-time traffic flow. This speed information is usually provided by a real-time traffic data collection system (such as global positioning system tracking, road sensors, etc.), which can reflect the impact of current traffic conditions on vehicle speed.

[0044] (4) Road attribute information: a series of data used to describe road characteristics, such as driving speed, road slope, road curvature, traffic signals, etc.

[0045] (5) Offset: It is used to indicate the specific position of a vehicle or map entity on the navigation path. It refers to the distance from the starting point of the navigation path to the current position of the vehicle or map entity along the path. It is a measurement method on the path, which is used to accurately indicate the position of the vehicle or map entity on the path. In actual application scenarios, the fitting accuracy of the unit offset defined by EHP is 128m. This means that each offset unit represents approximately 128 meters of path length.

[0046] Currently, vehicles can usually use navigation data provided by a navigation service platform (eg, an electronic horizon provider) for assisted driving (assisting users in controlling the vehicle to drive automatically).

[0047] For example, Figure 1 As shown, Figure 11 is a schematic diagram of a vehicle driving scene. For example, scene 100 includes vehicle 110 and driving path 120; vehicle 110 can drive on driving path 120 using navigation data provided by a navigation service platform. For example, if the navigation data includes the historical average speed of each waypoint of vehicle 110 on driving path 120, vehicle 110 can drive on driving path 120 according to the historical average speed of each waypoint. For example, the driving speed of vehicle 110 at waypoint 121 is 40 kilometers per hour (km / h).

[0048] It should be understood that in order to ensure that the vehicle 110 travels smoothly on the driving path 120, in the navigation data provided by the navigation service platform, there is usually not a large difference in the historical average speed between two adjacent waypoints.

[0049] However, due to regulatory restrictions, untimely updates of navigation information and other reasons, the data of a certain waypoint in the navigation data provided by the electronic horizon provider (for example, the historical average driving speed when passing a certain waypoint) contains partial distortion, mutation and other abnormal data, which may cause the vehicle to make wrong decisions during driving, resulting in abnormal vehicle driving; the vehicle may suddenly accelerate, decelerate, or drive at an unreasonable speed; this will affect the user's riding experience and even cause traffic accidents.

[0050] For example, based on the above Figure 1 Related description: There may be an abnormal historical average speed of a certain waypoint (for example, waypoint 122) in the navigation data; the historical average speed of the waypoint 122 (for example, 80km / h) is significantly different from the historical average speed of the previous waypoint (waypoint 123) (for example, 40km / h). This will cause the vehicle to travel at the historical average speed of 40km / h at waypoint 123, but make a decision error when passing through waypoint 122, and adjust the driving speed to 80km / h; due to the sudden change in driving speed, the vehicle suddenly accelerates, affecting the user's riding experience; it may even collide with the vehicle in front, causing a traffic accident.

[0051] Therefore, how to achieve smooth driving of the vehicle during assisted driving has become a technical problem that urgently needs to be solved.

[0052] In view of this, the present application provides a vehicle control method, device, vehicle and storage medium, which detects whether there is abnormal data in the data of multiple waypoints in the driving path in the navigation data; if there is abnormal data, the abnormal data is processed by sliding average to obtain updated data of multiple waypoints; based on the updated data of multiple waypoints, the vehicle is controlled to perform an assisted driving function. The method is used to achieve smooth driving of the vehicle during the assisted driving process.

[0053] In order to illustrate the technical solution described in this application, the following is a description through specific embodiments. Figures 2 to 5 The vehicle control method provided in the embodiment of the present application is described in detail.

[0054] It should be understood that the embodiments of the present application do not particularly limit the execution subject of the vehicle control method, as long as the program that records the code of the vehicle control method of the embodiments of the present application can be run to communicate according to the vehicle control method of the embodiments of the present application. For example, the execution subject of the vehicle control method provided in the embodiments of the present application can be a vehicle, or a vehicle control device used in a vehicle, such as a chip.

[0055] Figure 2 It is a flow chart of a vehicle control method provided in an embodiment of the present application.

[0056] S201. When it is detected that the vehicle has activated the assisted driving function, obtain the navigation data of the vehicle.

[0057] The navigation data includes data of multiple waypoints in the driving route; the data of the waypoints are used to reflect the driving conditions of the vehicle at the waypoints.

[0058] For example, during the driving process of the vehicle, the driving assistance function of the vehicle can be used to control the vehicle. For example, the vehicle can be controlled according to the data of multiple waypoints in the navigation data (for example, the driving speed); for details, please refer to the following embodiments.

[0059] Exemplarily, the navigation data may be composed of a series of waypoints; these waypoints may be arranged in the order of the vehicle's travel, thereby obtaining a travel path. Optionally, the last waypoint in a series of waypoints may be the end point of the travel path, or a certain position point on the travel path, and the embodiment of the present application does not impose specific restrictions on this. Each waypoint may include a plurality of data related to the driving condition; for example: the position of the waypoint on the travel path, the slope of the waypoint, the historical average speed, the road type, the traffic condition, etc. Among them, the position of the waypoint on the travel path may be expressed in coordinate form. The historical average speed may include: the average speed of other vehicles passing through the waypoint within the previous historical preset time (for example, one month or one hour); the average speed of other vehicles passing through the waypoint at the historical preset time (for example, 12:00:00). Road types may include: urban main roads, urban branch roads, highways, tunnels, rural roads, etc. Traffic conditions may refer to the road congestion, traffic volume, whether a traffic accident has occurred, whether there is road construction, etc. at the waypoint.

[0060] As an example, the vehicle can obtain navigation data in the driving section when it is started, wherein the first waypoint in the navigation data can be the starting point of the driving route.

[0061] For example, Figure 3 As shown, Figure 3 A schematic diagram of a vehicle driving scenario provided for an embodiment of the present application. Take the case where the navigation data includes the historical average speed of each waypoint of the vehicle on the driving path as an example: the driving path includes multiple waypoints, such as waypoint A to waypoint D. It should be explained that there are also multiple waypoints between waypoint A and waypoint B, between waypoint B and waypoint C, and between waypoint C and waypoint D. Waypoint A can be the starting point of the driving path. Optionally, waypoint D can be the end point of the driving path, or it can be a certain position point on the driving path. The historical average speed corresponding to waypoint A is 45km / h; the historical average speed corresponding to waypoint B is 100km / h; the historical average speed corresponding to waypoint C is 70km / h; the historical average speed corresponding to waypoint D is 10km / h. Among them, the historical average speed corresponding to the waypoints between waypoints A and B (excluding waypoint B) is 45km / h; the historical average speed corresponding to the waypoints between waypoints B and C (excluding waypoint C) is 100km / h; the historical average speed corresponding to the waypoints between waypoints C and D (excluding waypoint D) is 70km / h. It can be understood that the section 1 corresponding to the section between waypoints A and B, the section 2 corresponding to the section between waypoints B and C, and the section 3 corresponding to the section between waypoints C and D may be sections of different road types; for example, section 1 is a rural road; section 2 is a highway; and section 3 is a tunnel.

[0062] As another example, the vehicle may also obtain navigation data while traveling on the driving section. The first waypoint in the navigation data may be the current position of the vehicle on the driving section.

[0063] For example, Figure 4 As shown, Figure 4Another schematic diagram of a vehicle driving scenario provided for an embodiment of the present application. Take the example of navigation data including the historical average speed of each waypoint of the vehicle on the driving path: the driving path includes multiple waypoints, such as waypoint B to waypoint E. It should be explained that there are also multiple waypoints between waypoint B and waypoint C, between waypoint C and waypoint D, and between waypoint D and waypoint E. Waypoint B can be the current position of the vehicle on the driving section. Optionally, waypoint E can be the end point of the driving path, or it can be a certain position point on the driving path. The historical average speed corresponding to waypoint B is 100km / h; the historical average speed corresponding to waypoint C is 70km / h; the historical average speed corresponding to waypoint D is 10km / h; the historical average speed corresponding to waypoint E is 25km / h; among them, the historical average speed corresponding to the waypoints between waypoint B and waypoint C (excluding waypoint C) is 100km / h; the historical average speed corresponding to the waypoints between waypoint C and waypoint D (excluding waypoint D) is 70km / h; the historical average speed corresponding to the waypoints between waypoint D and waypoint E (excluding waypoint E) is 10km / h. It can be understood that Section 2 corresponding to the section between waypoint B and waypoint C, Section 3 corresponding to the section between waypoint C and waypoint D, and Section 4 corresponding to the section between waypoint D and waypoint E may be sections of different road types; for example, Section 2 is a highway; Section 3 is a tunnel; Section 4 is a city street, and there is road congestion in Section 4.

[0064] For example, the embodiments of the present application do not limit how the vehicle obtains navigation data. For example, the vehicle can obtain navigation data from a navigation service platform (e.g., EHP); the vehicle can also obtain navigation data from other devices (e.g., a mobile phone that communicates with the vehicle).

[0065] S202. Detect whether there is abnormal data in the data of multiple waypoints.

[0066] It is understandable that there may be abnormal data such as data distortion and mutation in the data of multiple waypoints.

[0067] For example, based on the above Figure 3 Related description: The historical average speed of the waypoints between waypoints A and B (excluding waypoint B) is 45km / h; if there is a waypoint F between waypoints A and B, the historical average speed is 70km / h; when the vehicle is traveling at a speed of 45km / h on section 1, when passing through waypoint F, the vehicle is adjusted to a speed of 70km / h, causing a sudden change in the vehicle's speed. It can be concluded that the historical average speed of waypoint F is abnormal data.

[0068] For example, based on the above Figure 3 Related description: The historical average speed of the waypoints between waypoint B and waypoint C (excluding waypoint C) is 100km / h; the historical average speed of the waypoints between waypoint C and waypoint D (excluding waypoint D) is 70km / h. When the vehicle is traveling at a speed of 45km / h on section 1 and passes through waypoint B, since the historical average speed of waypoint B is 100km / h, the vehicle will adjust to a speed of 100km / h when passing through waypoint B, causing a sudden change in the vehicle's speed. Similarly, when the vehicle is traveling at a speed of 100km / h on section 2 and passes through waypoint C, since the historical average speed of waypoint C is 70km / h, the vehicle will adjust to a speed of 70km / h when passing through waypoint C, causing a sudden change in the vehicle's speed. When the vehicle is traveling at a speed of 70 km / h on road section 3 and passes through point D, since the historical average speed of point D is 10 km / h, the vehicle will adjust to a speed of 10 km / h when passing through point D, causing a sudden change in the vehicle's speed. In summary, the historical average speeds of point B, point C, and point D are abnormal data.

[0069] For example, based on the above Figure 4 Related description: The historical average speed of the waypoints between waypoints D and E (excluding waypoint E) is 10km / h. If there is a waypoint G between waypoints D and E, the historical average speed is 20km / h; when the vehicle is traveling at a speed of 10km / h on section 4, when passing through waypoint G, the vehicle is adjusted to a speed of 20km / h, causing a sudden change in the vehicle's speed. It can be concluded that the historical average speed of waypoint G is abnormal data.

[0070] For example, based on the above Figure 4 Related description: The historical average speed of the waypoints between waypoint D and waypoint E (excluding waypoint E) is 10km / h. When the vehicle is traveling at a speed of 10km / h on section 4 and passes through waypoint E, since the historical average speed corresponding to waypoint E is 25km / h, the vehicle will adjust to a speed of 25km / h when passing through waypoint E, causing a sudden change in the vehicle's speed. It can be concluded that the historical average speed of waypoint E can be abnormal data.

[0071] For a detailed description of S202, please refer to the following embodiments, which will not be described again here.

[0072] S203. If there is abnormal data, obtain updated data of multiple waypoints by performing sliding average processing on the abnormal data.

[0073] Exemplarily, the abnormal data may have different types; for example, a steady-state data type and a noise data type. For a specific description of the abnormal data, please refer to the following embodiments.

[0074] Exemplarily, the sliding average processing method may include weighted average processing and equal weighted average processing. The embodiment of the present application may select a corresponding sliding average processing method according to the type of abnormal data. For example, when the type of abnormal data is a steady-state data type, the abnormal data may be processed by weighted average; when the type of abnormal data is a noise data type, the abnormal data may be processed by equal weighted average; for details, please refer to the following embodiments.

[0075] For a detailed description of S203, please refer to the following embodiments, which will not be described again here.

[0076] S204. Control the vehicle to perform the assisted driving function based on the updated data of the multiple waypoints.

[0077] For example, when the vehicle turns on the assisted driving function, the vehicle controls the vehicle to travel on the driving path based on the data of multiple updated waypoints; since the data change trend of the updated multiple waypoints after sliding average processing is stable, the vehicle can travel smoothly on the driving path; at the same time, the data change trend on the driving path is obvious, which can also provide more road information for the back-end energy management.

[0078] In an embodiment of the present application, when data of multiple waypoints in a driving path are obtained, the driving condition of the vehicle at each waypoint on the driving path can be preliminarily obtained; by detecting whether there is abnormal data in the data of multiple waypoints, it is possible to detect whether there is distorted or mutated waypoint data in the navigation data; if there is abnormal data, the abnormal data is processed by sliding average to obtain updated data of multiple waypoints; and based on the updated data of multiple waypoints, the vehicle is controlled to perform an assisted driving function, thereby reducing the current situation in which the vehicle makes decisions incorrectly during driving, resulting in abnormal vehicle driving; based on this, the embodiment of the present application can achieve smooth driving of the vehicle during assisted driving.

[0079] In a possible embodiment of the present application, the data of the waypoint includes: a first driving speed of the vehicle when passing the waypoint; a second driving speed of the vehicle when passing the waypoint; and at least one of the road slope when the vehicle passes the waypoint.

[0080] The first driving speed is the average speed of other vehicles passing the waypoint during a preset historical period; the second driving speed is the average speed of other vehicles passing the waypoint at a preset historical moment.

[0081] Exemplarily, the historical preset time period and the historical preset time may be values ​​preconfigured for the vehicle or manually set values, and the embodiments of the present application do not impose any specific restrictions on this.

[0082] In actual application scenarios, the data of the waypoints can be road attribute information. It is understandable that when controlling a vehicle to travel on a driving path, it often relies on multiple types of road attribute information, such as driving speed, road slope, traffic conditions, etc. When at least one type of waypoint data exists, the robustness of controlling the vehicle's travel can be improved; it helps the vehicle to travel stably and safely on different types of driving paths, improving the user experience.

[0083] For example, the first driving speed may be an average speed of multiple vehicles traveling at the waypoint in the past period of time (historical preset period). The average speed is derived from a large number of historical driving records and can be used to represent the average speed level of vehicles traveling through the waypoint.

[0084] For example, the second driving speed may be the average speed of multiple vehicles traveling at the waypoint at a certain time in the past (historical preset time). The historical preset time may be a time within the previous hour. In actual application scenarios, the second driving speed may be the traffic flow speed.

[0085] Exemplarily, the road slope may be the inclination angle between the road surface of the waypoint and the horizontal plane. It is understandable that, in the case where the waypoint data is the road slope when the vehicle passes the waypoint, by performing sliding average processing on the road slopes of multiple waypoints, the driving strategy of the vehicle when driving on the driving path can be adjusted in time; for example, the vehicle can be decelerated appropriately before going uphill; and the vehicle can maintain its driving speed when going downhill. In addition, by performing sliding average processing on the road slopes of multiple waypoints, the energy management needs can be adjusted to be accurately predicted; for example, when the vehicle goes uphill, the engine is usually required to output a higher power; and when the vehicle goes downhill, the engine output power requirement is lower; therefore, by processing the road slopes of multiple waypoints, energy can also be reasonably distributed, thereby reducing energy consumption.

[0086] In the embodiments of the present application, since the data of the waypoints may include at least one type of data, the accuracy of understanding the road conditions of the driving path can be improved, so that the vehicle can be controlled to travel stably and safely on different types of driving paths.

[0087] In a possible embodiment of the present application, the above S202 includes: obtaining a first difference between data of two adjacent waypoints among multiple waypoints; determining whether there is a second difference in the first difference; if there is a second difference, taking the data of the second waypoint among the two adjacent waypoints corresponding to the second difference as abnormal data.

[0088] The second difference is greater than the first difference threshold.

[0089] It can be understood that by determining the difference between the data of two adjacent waypoints before and after the data change, the nature of the data change of the waypoints can be determined more accurately; for example, if the absolute value of the difference between the data of the waypoints before and after the data change is very small, then this may just be a normal speed fluctuation and no special processing is required; if the absolute value of the difference between the data of the waypoints before and after the data change reaches the first difference threshold, such as a sudden increase in the historical average speed, then the data of the waypoint after the change can be considered to be a significant change point (i.e., abnormal data).

[0090] Exemplarily, the first difference threshold may be a difference threshold preconfigured for the vehicle, or may be a difference threshold set manually, and the embodiments of the present application do not impose any specific limitation on this.

[0091] As an example, the first difference threshold can be determined by the data size of the waypoints; when the data of multiple waypoints in the road section is large, the first difference threshold can be set to a larger value; when the data of multiple waypoints in the road section is small, the first difference threshold can be set to a smaller value. For example, taking the data of the waypoints as the historical average speed as an example: the historical driving speed can be divided into multiple speed stages (low-speed stage, medium-speed stage and high-speed stage); the first difference threshold corresponding to the low-speed stage (for example, 0<V≤30km / h) is the first difference threshold 1 (for example, 5km / h), the first difference threshold corresponding to the medium-speed stage (for example, 30km / h<V≤80km / h) is the first difference threshold 2 (for example, 15km / h), and the first difference threshold corresponding to the high-speed stage (for example, 80km / h<V) is the first difference threshold 3 (for example, 25km / h).

[0092] For example: Figure 3 and Figure 4 As shown: the historical average speed corresponding to the waypoints between waypoint A and waypoint B (excluding waypoint B) can be regarded as the medium-speed stage; the historical average speed corresponding to the waypoints between waypoint B and waypoint C (excluding waypoint C) can be regarded as the high-speed stage; the historical average speed corresponding to the waypoints between waypoint C and waypoint D (excluding waypoint D) can be regarded as the medium-speed stage; the historical average speed corresponding to the waypoints between waypoint D and waypoint E (excluding waypoint E) can be regarded as the low-speed stage.

[0093] For example, the historical average speed corresponding to the waypoints between waypoints A and B (excluding waypoint B) is 45km / h; if there is a waypoint F among the waypoints between waypoints A and B, the historical average speed corresponding to it is 70km / h; the historical average speed of waypoint H before and adjacent to waypoint F is 45km / h; the first difference between the historical average speed of waypoint F and the historical average speed of waypoint H is 25km / h. Since the first difference (25km / h) is greater than the first difference threshold 2 (15km / h), the first difference is the second difference. In other words, the historical average speed of waypoint F is abnormal data. The historical average speed corresponding to the waypoints between waypoints D and E (excluding waypoint E) is 10km / h. If there is a waypoint G among the waypoints between waypoint D and waypoint E, the historical average speed corresponding to the waypoint G is 20km / h; the historical average speed of the waypoint I before and adjacent to the waypoint G is 10km / h. Since the first difference (10km / h) between the historical average speed of the waypoint G and the historical average speed of the waypoint I is greater than the first difference threshold 1 (5km / h), the historical average speed of the waypoint G is abnormal data.

[0094] For example, Figure 3 and Figure 4As shown: the first difference between the historical average speed (100km / h) of waypoint B and the historical average speed (45km / h) of waypoint A' before and adjacent to waypoint B is 55km / h, and the first difference is greater than the first difference threshold 2 (15km / h), so the first difference is the second difference. In other words, the historical average speed of waypoint B is abnormal data. The first difference between the historical average speed (70km / h) of waypoint C and the historical average speed (100km / h) of waypoint B' before and adjacent to waypoint C is 30km / h, and the first difference is greater than the first difference threshold 3 (25km / h), so the first difference is the second difference. In other words, the historical average speed of waypoint C is abnormal data. The first difference between the historical average speed (10 km / h) of waypoint D and the historical average speed (70 km / h) of waypoint C' before and adjacent to waypoint D is 60 km / h, and the first difference is greater than the first difference threshold 2 (15 km / h), so the first difference is the second difference. In other words, the historical average speed of waypoint D is abnormal data. The first difference between the historical average speed (25 km / h) of waypoint E and the historical average speed (10 km / h) of waypoint D' before and adjacent to waypoint E is 15 km / h, and the first difference is greater than the first difference threshold 1 (5 km / h), so the first difference is the second difference. In other words, the historical average speed of waypoint E is abnormal data.

[0095] In summary, it can be determined that the historical average speed of waypoint B, the historical average speed of waypoint C, the historical average speed of waypoint D, the historical average speed of waypoint E, the historical average speed of waypoint F and the historical average speed of waypoint G are abnormal data.

[0096] Optionally, in a possible implementation, the first difference threshold may also be a fixed value; no matter how much data the vehicle has at a certain waypoint, when the data of the vehicle at the waypoint differs from the data of the previous waypoint by a first difference threshold with a fixed value, the data at the waypoint is regarded as abnormal data.

[0097] For example: As above Figure 3 As shown, taking the data of the waypoints as the historical average speed as an example: the first difference threshold is set to 15km / h: if the historical driving speed of the vehicle changes during driving, and the change value is greater than 15km / h (for example, the difference between the historical driving speed of waypoint F and the historical driving speed of waypoint H is 25km / h), the historical driving speed of the waypoint where the historical driving speed changes (for example, the historical driving speed of waypoint F) is taken as abnormal data.

[0098] For example, based on the above Figure 3 and Figure 4 For the relevant description, take the first difference threshold of 15km / h as an example: the historical average speed corresponding to the waypoints between waypoints A and B (excluding waypoint B) is 45km / h; if there is a waypoint F among the waypoints between waypoints A and B, the historical average speed corresponding to it is 70km / h; the historical average speed of waypoint H before and adjacent to waypoint F is 45km / h; the first difference between waypoint F and waypoint H is 25km / h. Since the first difference between waypoint F and waypoint H (25km / h) is greater than the first difference threshold (15km / h), the first difference between waypoint F and waypoint H is the second difference. In other words, the historical average speed of waypoint F (the second waypoint of two adjacent waypoints) is abnormal data.

[0099] For example, based on the above Figure 3 and Figure 4For example, when a vehicle reaches waypoint B at a speed of 45 km / h on road section 1, the historical average speed of waypoint B is 100 km / h, while the historical average speed of waypoint A' before and adjacent to waypoint B is 45 km / h; the first difference between the historical average speed of waypoint B and the historical average speed of waypoint A' is 55 km / h, which is greater than the first difference threshold (15 km / h). Therefore, the first difference is the second difference. In other words, the historical average speed of waypoint B is abnormal data. When the vehicle is traveling at a speed of 100 km / h on road section 2 and arrives at waypoint C, the historical average speed of waypoint C is 70 km / h, while the historical average speed of waypoint B' before and adjacent to waypoint C is 100 km / h; the first difference between the historical average speeds of waypoint C and the historical average speeds of waypoint B' is 30 km / h, and the first difference is greater than the first difference threshold (15 km / h), so the first difference is the second difference. In other words, the historical average speed of waypoint C is abnormal data. When the vehicle is traveling at a speed of 70 km / h on road section 3 and arrives at waypoint D, the historical average speed of waypoint D is 10 km / h, while the historical average speed of waypoint C' before and adjacent to waypoint D is 70 km / h; the first difference between the historical average speeds of waypoint D and the historical average speeds of waypoint C' is 60 km / h, which is greater than the first difference threshold (15 km / h), so the first difference is the second difference. In other words, the historical average speed of waypoint D is abnormal data. When the vehicle reaches waypoint E while traveling at a speed of 10 km / h on road section 4, the historical average speed of waypoint E is 25 km / h, while the historical average speed of waypoint D' before and adjacent to waypoint E is 10 km / h; the first difference between the historical average speeds of waypoint E and the historical average speeds of waypoint D' is 15 km / h, and the first difference is equal to the first difference threshold (15 km / h), so the first difference is not the second difference, in other words, the historical average speed of waypoint E is not abnormal data.

[0100] In summary, it can be determined that the historical average speed of waypoint B, the historical average speed of waypoint C, the historical average speed of waypoint D, and the historical average speed of waypoint F are abnormal data.

[0101] Regarding the setting method of the first difference threshold, the first embodiment of the present application does not impose any specific restrictions on this. The first difference threshold can be set according to actual conditions.

[0102] In an embodiment of the present application, abnormal data is detected by using the difference between data of two adjacent waypoints, which can improve the accuracy of detecting abnormal data and enable subsequent processing of the abnormal data.

[0103] In a possible embodiment of the present application, the data of multiple waypoints after update are obtained by performing sliding average processing on the abnormal data, including: determining the type of abnormal data according to the distance between two adjacent waypoints corresponding to the second difference; and performing sliding average processing on the abnormal data according to the type of the abnormal data to obtain the data of multiple waypoints after update.

[0104] Exemplarily, the types of abnormal data may include a variety of different types, such as steady-state data types and noise data types. Steady-state data types can be used to reflect more significant and lasting changes on the driving path; abnormal data of the steady-state data type are usually caused by long-term changes in road conditions on the driving path, or traffic flow (for example, morning and evening rush hours). Abnormal data of the noise data type are usually interference data caused by some unstable factors (such as sensor errors, short-term traffic fluctuations), etc., and these abnormal data are usually irregular. Therefore, the abnormal data can be processed according to the type of abnormal data; please refer to the following embodiments for details, which will not be repeated here.

[0105] In the embodiment of the present application, the abnormal data can be processed in different ways according to the type of abnormal data, so that the abnormal data can be effectively processed to achieve smooth driving of the vehicle.

[0106] In a possible embodiment of the present application, the type of abnormal data is determined according to the distance between two adjacent waypoints corresponding to the second difference, including: if the distance between two adjacent waypoints corresponding to the second difference is greater than the second difference threshold, the type of abnormal data is determined to be a steady-state data type; if the distance between two adjacent waypoints corresponding to the second difference is less than or equal to the second difference threshold, the type of abnormal data is determined to be a noise data type. According to the type of abnormal data, the abnormal data is subjected to sliding average processing to obtain updated data of multiple waypoints, including: if the type of abnormal data is a steady-state data type, the abnormal data is subjected to weighted average processing; if the type of abnormal data is a noise data type, the abnormal data is subjected to equal weighted average processing.

[0107] Exemplarily, the second difference threshold may be a difference threshold preconfigured for the vehicle, or may be a difference threshold set manually, and the embodiments of the present application do not impose any specific limitation on this.

[0108] As an example, the second difference threshold can be determined by the difference range of the change between the abnormal data and the data before the change. In other words, there is a correspondence between the first difference before and after the data change and the second difference threshold. When the difference before and after the change is large, the second difference threshold can be set to a larger value; when the difference before and after the change is small, the second difference threshold can be set to a smaller value. For example, as shown in Table 1, Table 1 is a correspondence table of a first difference range and a second difference threshold provided in an embodiment of the present application. Of course, the following table only provides an exemplary description of the second difference threshold, which can be determined by the actual scenario.

[0109] Table 1

[0110] The first difference range before and after the data changes Second difference threshold (0,30] D1 (30,60] D2 (60,90] D3 (90,120] D4

[0111] Exemplarily, the first difference range before and after the data change is (0,30], and the second difference threshold can be set to D1; the first difference range before and after the data change is (30,60], and the second difference threshold can be set to D2; the first difference range before and after the data change is (60,90], and the second difference threshold can be set to D3; the first difference range before and after the data change is (90,120], and the second difference threshold can be set to D4. Exemplarily, D1 to D4 in the second difference threshold can be different values. As the value in the first difference range before and after the data change increases, the second difference threshold also increases accordingly; in other words, D1<D2<D3<D4.

[0112] For example: As above Figure 3 and Figure 4As shown, taking the data of the waypoints as the historical average speed as an example: the historical average speeds of waypoints B, C, D, E, F and G are abnormal data. The first difference between the historical average speed of waypoint B and the historical average speed of waypoint A' is 55km / h, and the second difference threshold can be determined to be D2; since the distance between waypoint B and waypoint A' is greater than D2, the historical average speed of waypoint B is a steady-state data type. The first difference between the historical average speed of waypoint C and the historical average speed of waypoint B' is 30km / h, and the second difference threshold can be determined to be D1; since the distance between waypoint C and waypoint B' is greater than D1, the historical average speed of waypoint C is a steady-state data type. The first difference between the historical average speed of waypoint D and the historical average speed of waypoint C' is 60km / h, and the second difference threshold can be determined to be D2; since the distance between waypoint D and waypoint C' is greater than D2, the historical average speed of waypoint D is a steady-state data type. The first difference between the historical average speed of waypoint E and the historical average speed of waypoint D' (10km / h) is 15km / h, and the second difference threshold can be determined to be D1; since the distance between waypoint E and waypoint D' is greater than D1, the historical average speed of waypoint E is a steady-state data type. The first difference between waypoint F and waypoint H is 25km / h, and the second difference threshold can be determined to be D1; since the distance between waypoint F and waypoint H is less than D1, the historical average speed of waypoint F is a noise data type. The first difference between the historical average speed of waypoint G and the historical average speed of waypoint I is 10 km / h, and the second difference threshold can be determined to be D1; since the distance between waypoint G and waypoint I is less than D1, the historical average speed of waypoint G is a noise data type.

[0113] In summary, the historical average speed of the waypoint B, the historical average speed of the waypoint C, the historical average speed of the waypoint D, and the historical average speed of the waypoint E are abnormal data of the steady-state data type; the historical average speed of the waypoint F and the historical average speed of the waypoint G are abnormal data of the noise data type.

[0114] It should be explained that, since the steady-state data type can reflect more significant and lasting changes in the driving path, the abnormal data of the steady-state data type can be processed by weighted averaging, so that the abnormal data and the data of other adjacent waypoints can be smoothly transitioned, and the accuracy of the abnormal data in reflecting the actual situation will not be affected. Correspondingly, since the abnormal data of the noise data type is usually interference data caused by some unstable factors, the abnormal data of the noise data type can be processed by equal weighted averaging, which can eliminate or reduce the impact of the abnormal data of the noise data type on the entire data, making the change trend of the entire data smooth and stable.

[0115] In an embodiment of the present application, the type of abnormal data is determined by the distance between the waypoints, so that the accuracy of data judgment can be improved, and the abnormal data can be processed in different ways, thereby being able to effectively process the abnormal data to achieve smooth driving of the vehicle.

[0116] In a possible embodiment of the present application, the above S203 includes: obtaining a preset sliding window value for abnormal data; according to the preset sliding window value, obtaining a first waypoint that meets a preset number from multiple waypoints; based on the data of the preset number of first waypoints and the abnormal data, performing sliding average processing on the abnormal data to obtain updated data of multiple waypoints.

[0117] Among them, the data of the first waypoint is located around the abnormal data.

[0118] Exemplarily, the preset window sliding value may be determined by the vehicle's single processing capability; optionally, the preset window sliding value may also be data pre-configured by the vehicle, or a manually set value, which is not specifically limited in the present embodiment of the application. For example, the preset sliding window value may be 25.

[0119] As another example, in the above Figure 3 In the relevant description, the historical average speed of the waypoint F is taken as an example: assuming that the preset sliding window value can be 25; when the historical average speed of the waypoint F is detected as abnormal data of the noise data type, the historical average speed of the 24 waypoints before the waypoint F can be obtained (that is, the data of the first waypoint of the preset number); the historical average speeds of the 24 waypoints before the waypoint F are all 45km / h, and the historical average speed of the waypoint F is 70km / h; therefore, the historical average speed of the waypoint F is processed with equal weight averaging, specifically: the historical average speed of the waypoint F after processing = (45×24+70) / 25 = 46km / h.

[0120] As an example, the above Figure 3As an example, assume that the preset sliding window value can be 25; when the historical average speed of the waypoint B is detected as abnormal data of the steady-state data type, the historical average speed of the 24 waypoints before the waypoint B (that is, the data of the first waypoint of the preset number) can be obtained; the historical average speeds of the 23 waypoints before the waypoint B are all 45 km / h and the historical average speed of the waypoint F after processing is 46 km / h, and the historical average speed of the waypoint B is 100 km / h; assume that the weight of the historical average speed of the 24 waypoints before the waypoint B is 1, and the weight of the historical average speed of the waypoint B is 20; therefore, the historical average speed of the waypoint B is weighted averaged, specifically: the historical average speed of the waypoint B after processing = (45×23+46+100×20) / (24×1+20) = 70.02 km / h.

[0121] Of course, optionally, when the historical average speed of the processed waypoint B is 53.14 km / h, since the first difference between the historical average speed of the processed waypoint B and the historical average speed of the waypoint B1 after the processed waypoint B and adjacent to the waypoint B is 45.86 km / h, the first difference is greater than the first difference threshold (25 km / h); and since the historical average speed of the waypoint B1 is abnormal data of the steady-state data type, the historical average speed of the waypoint B1 is weighted averaged; the historical average speeds of the 24 waypoints before the waypoint B1 include: the historical average speeds of the 22 waypoints are all 45 km / h, the historical average speed of the processed waypoint F is 46 km / h, and the historical average speed of the processed waypoint B is 70.02 km / h. Assume that the weight of the historical average speed of the 24 waypoints before the waypoint B1 is 1, and the weight of the historical average speed of the waypoint B1 is 20; therefore, the historical average speed of the waypoint B1 is weighted averaged, specifically: the historical average speed of the waypoint B1 after processing = (45×22+46+70.02+100×20) / (24×1+20) = 70.59km / h. The processing method of the historical average speed of subsequent waypoints can refer to the above processing steps, which will not be repeated here.

[0122] For the specific principles of equal-weighted average processing and weighted average processing, reference may be made to the prior art and will not be repeated here.

[0123] In an embodiment of the present application, by performing sliding average processing on abnormal data, smoothing and stabilization of data of multiple waypoints can be achieved.

[0124] In a possible embodiment of the present application, the above S201 includes: obtaining initial navigation data and a preset length of the vehicle; and obtaining multiple waypoints from multiple initial waypoints according to the preset length to obtain navigation data.

[0125] The initial navigation data includes data of a plurality of initial waypoints in the driving route; and the distance between two adjacent waypoints in the plurality of waypoints is a preset length.

[0126] Exemplarily, the initial navigation data includes a plurality of initial waypoints; the number of the plurality of initial waypoints may be greater than or equal to the number of the plurality of waypoints in the navigation data; the distance between two adjacent initial waypoints may be the initial length. Waypoints may be obtained at equal distances among the plurality of initial waypoints according to a preset length. In other words, the preset length may be greater than or equal to the initial length and may be a multiple of the initial length. In this way, the amount of data storage and the amount of data processing in the navigation data may be reduced.

[0127] Exemplarily, the preset length may be a value pre-configured by the vehicle or a manually set value, and the embodiments of the present application do not impose specific restrictions on this. For example, the preset length may be 128 meters, or 256 meters, etc. It is understandable that the smaller the preset length, the more the number of multiple waypoints obtained from the multiple initial waypoints of the initial navigation data, and thus the higher the accuracy of the navigation data. The larger the preset length, the fewer the number of multiple waypoints obtained from the multiple initial waypoints of the initial navigation data, and thus the lower the accuracy of the navigation data.

[0128] Example: Continuing with the above text Figure 3Take the relevant description as an example to illustrate: the multiple initial waypoints in the initial navigation data include: waypoint A to waypoint E, and multiple waypoints between waypoint A and waypoint B, between waypoint B and waypoint C, and between waypoint C and waypoint D. There are 499 waypoints between waypoint A and waypoint B (e.g., waypoint A1, waypoint A2, waypoint A3, ..., waypoint A499); there are 499 waypoints between waypoint B and waypoint C (e.g., waypoint B1, waypoint B2, waypoint B3, ..., waypoint B499); there are 457 waypoints between waypoint C and waypoint D (e.g., waypoint C1, waypoint C2, waypoint C3, ..., waypoint C457); there are 457 waypoints between waypoint D and waypoint E (e.g., waypoint D1, waypoint D2, waypoint D3, ..., waypoint D457). The distance between two adjacent waypoints can be 128 meters (i.e., the initial length). When the preset length is 256 meters, waypoints can be obtained starting from waypoint A among the multiple initial waypoints, and the obtained navigation data includes: waypoint A, waypoint A2, waypoint A4, waypoint A6, ..., waypoint A498, waypoint B, waypoint B2, waypoint B4, ..., waypoint B498, waypoint C, waypoint C2, waypoint C4, ..., waypoint C456, waypoint D, waypoint D2, waypoint D4, ..., waypoint D456, waypoint E.

[0129] Of course, the preset length of each stage can also be determined according to the number of multiple initial waypoints in the initial navigation data. It is understandable that in order to reduce memory usage, the number of waypoints in the navigation data can be dynamically adjusted according to the distance between each waypoint in the navigation data and the starting point in the driving path. Exemplarily, if the distance between the waypoint and the starting point in the driving path is close, more waypoints can be obtained from the initial navigation data to improve the accuracy of the navigation data; if the distance between the waypoint and the starting point in the driving path is far, fewer waypoints can be obtained from the initial navigation data to save memory.

[0130] For example: the initial navigation data includes 3931 waypoints; the distance between adjacent waypoints is 128 meters; in actual application scenarios, each waypoint is used to represent an offset; for example, the 0th waypoint represents an offset of 0 from the driving path. Among them, according to the preset length a (128 meters), the waypoints in the navigation data 1 obtained from the 0th waypoint to the 561th waypoint include: the 1st waypoint, the 2nd waypoint, the 3rd waypoint, the 4th waypoint, ..., the 561st waypoint; correspondingly, according to the preset length b (256 meters), the waypoints in the navigation data 1 obtained from the 562th waypoint to the 1684th waypoint include: the 56th waypoint 2 waypoints, the 564th waypoint, the 566th waypoint, the 568th waypoint, ..., the 1684th waypoint; according to the preset length c (512 meters), the waypoints in the navigation data 1 obtained from the 1686th waypoint to the 3930th waypoint include: the 1686th waypoint, the 1690th waypoint, the 1694th waypoint, the 1698th waypoint, ..., the 3930th waypoint. In summary, as shown in Table 2, Table 2 is used to represent the waypoints in the navigation data 1. Exemplarily, Table 2 uses the form of a two-dimensional matrix to store the waypoints in the navigation data 1 for exemplary explanation. Of course, other forms can also be used to store the waypoints in the navigation data 1, and the embodiments of the present application do not impose specific restrictions on this.

[0131] Table 2

[0132]

[0133] In addition, while the vehicle is traveling on the driving path, the initial navigation data can be re-acquired, and then the navigation data can be re-acquired from the initial navigation data. For example, the initial navigation data includes 4231 waypoints, including: waypoint 300 to waypoint 4230. The distance between adjacent waypoints is 128 meters; according to the preset length a (128 meters), the waypoints in the navigation data 2 obtained from the 300th waypoint to the 562nd waypoint include: the 300th waypoint, the 301st waypoint, the 302nd waypoint, the 303rd waypoint, ..., the 861st waypoint; correspondingly, according to the preset length b (256 meters), the navigation data 2 obtained from the 862nd waypoint to the 1984th waypoint The waypoints in navigation data 2 include: the 862nd waypoint, the 864th waypoint, the 866th waypoint, the 868th waypoint, ..., the 1984th waypoint; the waypoints in navigation data 2 obtained from the 1986th waypoint to the 4230th waypoint according to the preset length c (512 meters) include: the 1986th waypoint, the 1990th waypoint, the 1994th waypoint, the 1998th waypoint, ..., the 4230th waypoint. In summary, as shown in Table 3, Table 3 is used to represent the waypoints in navigation data 2. Exemplarily, Table 3 uses the form of a two-dimensional matrix to store the waypoints in navigation data 2 for exemplary purposes. Of course, other forms may also be used to store the waypoints in navigation data 2, and the embodiments of the present application do not impose specific restrictions on this.

[0134] Table 3

[0135]

[0136] For example, when the vehicle deviates from the driving path indicated by the navigation, or when the network is disconnected and reconnected, or when the navigation is exited, the navigation data stored in the vehicle can be cleared; then the initial navigation data can be reacquired, and navigation data can be obtained from the initial navigation data.

[0137] Exemplarily, when the waypoint corresponding to the end point on the driving route is obtained, it can be determined that the complete data of the driving route is obtained.

[0138] In an embodiment of the present application, multiple waypoints are obtained from the initial navigation data according to a preset length, thereby reducing the amount of data to be processed and thereby reducing memory usage.

[0139] Figure 5 It is a schematic flowchart of another method for controlling a vehicle provided in an embodiment of the present application.

[0140] For example, Figure 5 The vehicle control method shown may be executed by the vehicle, or may be executed by a vehicle control device in the vehicle, such as a chip.

[0141] like Figure 5 As shown, the method for controlling a vehicle includes S501 to S509, and S501 to S509 are described in detail below.

[0142] S501. Determine whether navigation is turned on in the vehicle; if so, execute S502.

[0143] Optionally, if navigation is not turned on for the vehicle, the road attribute information on the driving path cannot be fitted, and thus the map information cannot be reconstructed.

[0144] S502. Obtain a road attribute information sequence in the driving path.

[0145] Exemplarily, the road attribute information sequence includes a plurality of road attribute information. Adjacent road attributes are separated by a unit offset. The fitting accuracy of the unit offset may be 125 meters.

[0146] Exemplarily, the road attribute information sequence may be obtained from the EHP.

[0147] S503. Determine whether the road attribute information is of speed type; if so, execute S504.

[0148] Exemplarily, when the road attribute information is of speed type, the road attribute information can be used to represent the historical driving speed.

[0149] S504. Fill the road attribute information sequence into the vector container.

[0150] Exemplarily, the vector container may store multiple pieces of road attribute information in the road attribute information sequence in order.

[0151] S505. Determine whether the amount of road attribute information in the vector container meets the preset processing capacity; if so, execute S506.

[0152] Exemplarily, the preset processing amount may be a preset number of unit offsets; for example, 25 unit offsets may be set as the preset processing amount. If the preset processing amount is met, it can be indicated that the road attribute information in the vector container can be subjected to sliding average processing.

[0153] Optionally, if the amount of road attribute information in the vector container does not meet the preset processing amount, the road attribute information in the road attribute information sequence continues to be filled into the vector container.

[0154] S506. Determine whether there is abnormal road attribute information with a front-to-back change difference greater than a first preset value; if so, execute S507.

[0155] Exemplarily, if the difference between the previous and the next changes is greater than the first preset value of abnormal road attribute information, it can be said that the abnormal road attribute information is abnormal data in the road attribute information sequence; a sliding average process can be performed on it to smooth the road attribute information sequence.

[0156] S507. Determine whether the target road length is greater than a second preset value; if so, execute S508; if not, execute 509.

[0157] Exemplarily, the target road length is the distance between the abnormal road attribute information and the adjacent road attribute information.

[0158] S508. Perform weighted averaging processing on abnormal road attributes to obtain a processed road attribute information sequence.

[0159] Exemplarily, if the road length between the abnormal road attribute information and the adjacent road attribute information is greater than a second preset value, this indicates that the abnormal road attribute information is of a steady-state type; the abnormal road attribute information may be subjected to weighted averaging processing.

[0160] S509. Perform equal-weighted averaging processing on abnormal road attributes to obtain a processed road attribute information sequence.

[0161] Exemplarily, if the road length between the abnormal road attribute information and the adjacent road attribute information is less than or equal to a second preset value, this indicates that the abnormal road attribute information is of a noise type; the abnormal road attribute information may be processed with equal weight averaging to eliminate noise.

[0162] Optionally, in a possible implementation, S502 to S509 may also be performed when the vehicle does not turn on navigation. For example, when the vehicle does not turn on navigation, the vehicle may also obtain the road attribute information sequence in the driving path from other devices (e.g., a mobile phone).

[0163] For detailed description of S501 to S509, please refer to Figures 2 to 4 The relevant descriptions will not be repeated here.

[0164] In an embodiment of the present application, abnormal data in a road attribute information sequence can be preliminarily detected by determining whether there is abnormal road attribute information in the road attribute information sequence whose difference between the previous and the next road attribute information is greater than a first preset value; then, the abnormal type of the abnormal road attribute information is determined by judging whether the road length between the abnormal road attribute information and the adjacent road attribute information is greater than a second preset value, and the abnormal road attribute information is processed according to the abnormal type, so that the processed road attribute information sequence is smooth and stable, and the change trend of the road attribute information sequence is obvious.

[0165] Combination of the above Figures 2 to 5 The vehicle control method provided by the embodiment of the present application is described in detail; Figure 6 and Figure 7 The device embodiments of the present application are described in detail. It should be understood that the device in the embodiments of the present application can execute the various methods of the aforementioned embodiments of the present application, that is, the specific working processes of the following various products can refer to the corresponding processes in the aforementioned method embodiments.

[0166] Combine the following Figure 6 The vehicle control device provided in the embodiment of the present application is described in detail.

[0167] like Figure 6 As shown, Figure 6 It is a structural schematic diagram of a vehicle control device provided in an embodiment of the present application.

[0168] For example, Figure 6 As shown, the device comprises:

[0169] The acquisition module 610 is used to acquire the navigation data of the vehicle when it is detected that the vehicle turns on the assisted driving function; wherein the navigation data includes data of multiple waypoints in the driving path; the data of the waypoints is used to reflect the driving condition of the vehicle at the waypoints;

[0170] A detection module 620, used to detect whether there is abnormal data in the data of multiple waypoints;

[0171] A processing module 630 is used to obtain updated data of multiple waypoints by performing sliding average processing on the abnormal data if there is abnormal data;

[0172] The control module 640 is used to control the vehicle to perform the assisted driving function according to the updated data of the multiple waypoints.

[0173] In a possible implementation, the detection module 620 is further used to obtain a first difference between data of two adjacent waypoints among multiple waypoints; determine whether a second difference exists in the first difference; wherein the second difference is greater than the first difference threshold; if the second difference exists, the data of the second waypoint among the two adjacent waypoints corresponding to the second difference is taken as abnormal data.

[0174] In a possible implementation, the processing module 630 is further used to determine the type of abnormal data based on the distance between two adjacent waypoints corresponding to the second difference; and perform sliding average processing on the abnormal data according to the type of the abnormal data to obtain updated data of multiple waypoints.

[0175] In a possible implementation, the detection module 620 is further configured to determine that the type of the abnormal data is a steady-state data type if the distance between two adjacent waypoints corresponding to the second difference is greater than a second difference threshold; and determine that the type of the abnormal data is a noise data type if the distance between two adjacent waypoints corresponding to the second difference is less than or equal to the second difference threshold.

[0176] In a possible implementation, the processing module 640 is further configured to perform weighted averaging processing on the abnormal data if the type of the abnormal data is a steady-state data type; and perform equal-weighted averaging processing on the abnormal data if the type of the abnormal data is a noise data type.

[0177] In one possible implementation, the processing module 630 is further used to obtain a preset sliding window value for abnormal data; according to the preset sliding window value, obtain a first waypoint that meets a preset number from multiple waypoints; wherein the data of the first waypoint is located around the abnormal data; based on the data of the preset number of first waypoints and the abnormal data, perform sliding average processing on the abnormal data to obtain updated data of multiple waypoints.

[0178] In a possible implementation, the acquisition module 610 is also used to obtain the initial navigation data and preset length of the vehicle; wherein the initial navigation data includes data of multiple initial waypoints in the driving path; according to the preset length, multiple waypoints are obtained from the multiple initial waypoints to obtain navigation data; wherein the distance between two adjacent waypoints in the multiple waypoints is the preset length.

[0179] It should be noted that the vehicle control device provided in the above embodiment only uses the division of the above-mentioned functional modules as an example when executing the vehicle control method. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the equipment is divided into different functional modules to complete all or part of the functions described above.

[0180] In addition, the vehicle control device and vehicle control method embodiments provided in the above embodiments belong to the same concept. Therefore, for details not disclosed in the device embodiments of this specification, please refer to the vehicle control method embodiments described above in this specification, and no further details will be given here.

[0181] Figure 7 It is a structural schematic diagram of a vehicle provided in an embodiment of the present application.

[0182] For example, Figure 7 As shown, the vehicle 700 includes: a memory 701 and a processor 702, wherein the memory 701 stores an executable program code 7011, and the processor 702 is used to call and execute the executable program code 7011 to perform a vehicle control method.

[0183] In addition, an embodiment of the present application also protects a device, which may include a memory and a processor, wherein the memory stores an executable program code, and the processor is used to call and execute the executable program code to execute a vehicle control method provided by an embodiment of the present application.

[0184] In this embodiment, the functional modules of the device can be divided according to the above method example. For example, each functional module can be corresponded, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0185] In the case of dividing each functional module according to each function, the device may also include an acquisition module, a detection module, a processing module, a control module, etc. It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, which will not be repeated here.

[0186] It should be understood that the device provided in this embodiment is used to execute the above-mentioned vehicle control method, and thus can achieve the same effect as the above-mentioned implementation method.

[0187] In the case of an integrated unit, the device may include a processing module and a storage module. When the device is applied to a vehicle, the processing module may be used to control and manage the actions of the vehicle. The storage module may be used to support the vehicle in executing related program codes, etc.

[0188] The processing module may be a processor or a controller, which may implement or execute various exemplary logic blocks, modules and circuits shown in conjunction with the disclosure of the present application. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module may be a memory.

[0189] In addition, the device provided in the embodiments of the present application may specifically be a chip, component or module, and the chip may include a connected processor and memory; wherein the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute a vehicle control method provided in the above embodiments.

[0190] This embodiment also provides a computer-readable storage medium, in which a computer program code is stored. When the computer program code is run on a computer, the computer executes the above-mentioned related method steps to implement a vehicle control method provided by the above-mentioned embodiment.

[0191] This embodiment also provides a computer program product. When the computer program product is run on a computer, the computer is caused to execute the above-mentioned related steps to implement a vehicle control method provided by the above-mentioned embodiment.

[0192] Among them, the device, computer-readable storage medium, computer program product or chip provided in this embodiment is used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects in the corresponding method provided above, and will not be repeated here.

[0193] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0194] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, 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 through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0195] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A vehicle control method, characterized in that: The method comprises: When it is detected that the vehicle has activated the assisted driving function, obtaining navigation data of the vehicle; wherein the navigation data includes data of a plurality of waypoints in the driving path; and the data of the waypoints is used to reflect the driving condition of the vehicle at the waypoints; Detecting whether there is abnormal data in the data of the plurality of waypoints; If the abnormal data exists, performing sliding average processing on the abnormal data to obtain updated data of the plurality of waypoints; Based on the updated data of the multiple waypoints, the vehicle is controlled to perform the assisted driving function.

2. The method according to claim 1, characterized in that The detecting whether there is abnormal data in the data of the plurality of waypoints comprises: Obtaining a first difference between data of two adjacent waypoints among the plurality of waypoints; Determine whether there is a second difference in the first difference; wherein the second difference is greater than the first difference threshold; If the second difference exists, data of the second waypoint of two adjacent waypoints corresponding to the second difference is taken as abnormal data.

3. The method according to claim 2, characterized in that The step of performing sliding average processing on the abnormal data to obtain updated data of the plurality of waypoints includes: determining the type of the abnormal data according to the distance between two adjacent waypoints corresponding to the second difference; According to the type of the abnormal data, the sliding average processing is performed on the abnormal data to obtain the updated data of the multiple waypoints.

4. The method according to claim 3, characterized in that The determining the type of the abnormal data according to the distance between two adjacent waypoints corresponding to the second difference includes: If the distance between two adjacent waypoints corresponding to the second difference is greater than a second difference threshold, determining that the type of the abnormal data is a steady-state data type; If the distance between two adjacent waypoints corresponding to the second difference is less than or equal to the second difference threshold, determining that the type of the abnormal data is a noise data type; The performing the sliding average processing on the abnormal data according to the type of the abnormal data to obtain the updated data of the plurality of waypoints includes: If the type of the abnormal data is the type of the steady-state data, performing weighted averaging processing on the abnormal data; If the type of the abnormal data is the noise data type, equal-weighted averaging is performed on the abnormal data.

5. The method according to claim 1, characterized in that: The step of performing sliding average processing on the abnormal data to obtain updated data of the plurality of waypoints includes: Obtaining a preset sliding window value for the abnormal data; According to the preset sliding window value, obtaining a first waypoint that meets a preset number from the plurality of waypoints; wherein the data of the first waypoint is located around the abnormal data; According to the data of the preset number of first waypoints and the abnormal data, sliding average processing is performed on the abnormal data to obtain the updated data of the plurality of waypoints.

6. The method according to any one of claims 1 to 5, characterized in that The obtaining of navigation data of the vehicle comprises: Acquiring initial navigation data and a preset length of the vehicle; wherein the initial navigation data includes data of a plurality of initial waypoints in the driving path; According to the preset length, a plurality of the waypoints are obtained from the plurality of the initial waypoints to obtain the navigation data; wherein the distance between two adjacent waypoints from the plurality of the waypoints is the preset length.

7. The method according to any one of claims 1 to 5, characterized in that The data of the waypoints include: at least one of a first driving speed of the vehicle when passing the waypoint; a second driving speed of the vehicle when passing the waypoint; and a road slope of the vehicle when passing the waypoint; The first driving speed is the average speed of other vehicles passing the waypoint during a historical preset time period; the second driving speed is the average speed of other vehicles passing the waypoint at a historical preset time.

8. A vehicle control device, characterized in that: The device comprises: An acquisition module, configured to acquire navigation data of the vehicle when detecting that the vehicle has activated the assisted driving function; wherein the navigation data includes data of a plurality of waypoints in the driving path; and the data of the waypoints is used to reflect the driving condition of the vehicle at the waypoints; A detection module, used to detect whether there is abnormal data in the data of the plurality of waypoints; A processing module, configured to obtain updated data of the plurality of waypoints by performing sliding average processing on the abnormal data if the abnormal data exists; A control module is used to control the vehicle to perform the assisted driving function according to the updated data of the multiple waypoints.

9. A vehicle, characterized in that: The vehicle comprises: A memory for storing executable program codes; A processor, configured to call and run the executable program code from the memory so that the vehicle executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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