Vehicle control method and vehicle

Abstract

The application provides a vehicle control method and a vehicle, and relates to the technical field of vehicle control. The method comprises the following steps: constructing a driving state information set according to different driving state information of different road sections uploaded by different vehicles; receiving target driving state information of a target road section uploaded by a target vehicle; determining an energy consumption prediction parameter according to a target driving state information set matched with the target driving state information in all driving state information sets; and issuing the energy consumption prediction parameter to the target vehicle, wherein the energy consumption prediction parameter is used for predicting energy consumption data of the target road section by the target vehicle and determining an energy consumption control strategy of the target road section based on the energy consumption data. The application solves the problem that a vehicle consumes a large amount of energy when passing through a front road section.

Description

Technical Field

[0001] This application relates to the field of vehicle control technology, and more particularly to a vehicle control method and a vehicle. Background Technology

[0002] In existing technologies, when a vehicle is in motion, the energy consumption control strategy for the road ahead can be adjusted by predicting the energy consumed on the road ahead. This allows the vehicle to be controlled according to the energy consumption control strategy on the road ahead, thereby achieving energy conservation.

[0003] In predicting the energy consumed on the road ahead, preset prediction parameters are typically used. However, due to the diverse nature of road sections in different scenarios, using preset prediction parameters to predict the energy consumed on the road ahead is inaccurate, further leading to inaccurate energy consumption control strategies and resulting in vehicles consuming more energy on the road ahead. Summary of the Invention

[0004] This application provides a vehicle control method and a vehicle. In this method, the cloud constructs a driving status information set based on the driving status information of different vehicles on different road segments. Thus, for each road segment, the driving status information of different vehicles is obtained. Therefore, for that road segment, the relationship between vehicle status and energy consumption in the driving status information can be constructed, and accurate energy consumption prediction parameters matching that road segment can be determined. In this way, the vehicle obtains the energy consumption prediction parameters corresponding to the target road segment from the cloud, and can use these parameters to predict accurate energy consumption data on the target road segment. Therefore, based on accurate energy consumption data, targeted energy consumption control strategies can be formulated to reduce the energy consumption of the vehicle on the target road segment and solve the problem of excessive energy consumption when the vehicle passes through the previous road segment.

[0005] In a first aspect, this application provides a vehicle control system, comprising: a vehicle-side unit, configured to acquire target driving state information of the vehicle on a target road segment; and to send the target driving state information to a cloud; the cloud, configured to construct a driving state information set based on driving state information of different road segments uploaded by different vehicles; to determine energy consumption prediction parameters based on the target driving state information set matched with the target driving state information; and to send the energy consumption prediction parameters to the vehicle-side unit; the vehicle-side unit is further configured to predict energy consumption data of passing through the target road segment based on the energy consumption prediction parameters; and to determine an energy consumption control strategy for the target road segment based on the energy consumption data, and to control the vehicle on the target road segment according to the energy consumption control strategy.

[0006] Secondly, this application provides a vehicle, comprising: an acquisition module for acquiring target driving state information of the vehicle on a target road segment; a transmission module for transmitting the target driving state information to a cloud; and receiving energy consumption prediction parameters determined by the cloud based on a target driving state information set matching the target driving state information, wherein the target driving state information set includes driving state information of different road segments uploaded by different vehicles; a prediction module for predicting the energy consumption demand of the target road segment based on the energy consumption prediction parameters; a determination module for determining an energy consumption control strategy for the target road segment based on the energy consumption demand; and a control module for controlling the vehicle on the target road segment according to the energy consumption control strategy.

[0007] Thirdly, this application provides an electronic device, comprising: at least one communication interface; at least one bus connected to the at least one communication interface; at least one processor connected to the at least one bus; and at least one memory connected to the at least one bus.

[0008] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements any of the methods described above.

[0009] Fifthly, this application provides a vehicle control method, comprising: constructing a driving state information set based on driving state information of different road segments uploaded by different vehicles; receiving target driving state information of a target road segment uploaded by a target vehicle; determining energy consumption prediction parameters based on a target driving state information set that matches the target driving state information from all driving state information sets; and sending the energy consumption prediction parameters to the target vehicle, wherein the energy consumption prediction parameters are used by the target vehicle to predict energy consumption data passing through the target road segment and to determine an energy consumption control strategy for the target road segment based on the energy consumption data.

[0010] Sixthly, this application provides a vehicle, including: a memory for storing a computer program; and a processor for calling and running the computer program from the memory, causing the vehicle to perform any of the methods described above.

[0011] The technical solutions provided in this application have the following advantages compared with the prior art: In this application, the cloud constructs a driving status information set based on the driving status and energy consumption information of different vehicles on different road segments. Thus, for each road segment, the cloud obtains the driving status and energy consumption information of different vehicles. Therefore, for that road segment, the relationship between vehicle status and energy consumption in the driving status information can be constructed, and accurate energy consumption prediction parameters matching that road segment can be determined. In this way, the vehicle obtains the energy consumption prediction parameters corresponding to the target road segment from the cloud, and can use these parameters to predict accurate energy consumption data on the target road segment. Therefore, based on accurate energy consumption data, targeted energy consumption control strategies can be formulated to reduce the energy consumption of the vehicle on the target road segment and solve the problem of excessive energy consumption when the vehicle passes through the previous road segment. Attached Figure Description

[0012] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0013] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0015] Figure 1 A schematic diagram illustrating the interaction between the vehicle and the cloud, provided in an embodiment of this application; Figure 2 A flowchart of a vehicle control method provided in an embodiment of this application; Figure 3 A flowchart of another vehicle control method provided in the embodiments of this application; Figure 4 A flowchart of yet another vehicle control method provided in this application embodiment; Figure 5 A flowchart of yet another vehicle control method provided in this application embodiment; Figure 6 This is a schematic diagram of a vehicle control system provided in an embodiment of this application; Figure 7 A schematic diagram of the structure of a vehicle provided in an embodiment of this application; Figure 8 This is a schematic diagram of another vehicle structure provided in an embodiment of this application. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] The following disclosure provides numerous different embodiments or examples for implementing various structures of this application. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the scope of this application. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed.

[0018] The following will describe in detail a vehicle control method provided in the embodiments of this application, with reference to specific implementation methods. Figure 1 This is a schematic diagram illustrating the interaction between the vehicle and the cloud in this application. Figure 2 This is a flowchart of the vehicle control method of this application. The method includes: S202, construct a driving status information set based on the driving status information of different road segments uploaded by different vehicles; S204, Receive target driving status information of the target road segment uploaded by the target vehicle; S206, Determine energy consumption prediction parameters based on the target driving state information set that matches the target driving state information from all driving state information sets; S208, the energy consumption prediction parameters are sent to the target vehicle, wherein the energy consumption prediction parameters are used by the target vehicle to predict the energy consumption data of passing through the target road segment and to determine the energy consumption control strategy of the target road segment based on the energy consumption data.

[0019] The vehicles covered by this application may be gasoline-powered vehicles, pure electric vehicles, plug-in hybrid vehicles, or range-extended electric vehicles, but are not limited to these.

[0020] The method described in this application can be applied in different scenarios.

[0021] For example, in the scenario of optimizing energy consumption in trunk logistics, the vehicle acquires the target driving status information of the target road segment (such as a long downhill section of a highway) and uploads it to the cloud; the cloud aggregates the driving status information and energy consumption information of hundreds or thousands of vehicles on this road segment, constructs a target driving status information set, and fits the energy consumption prediction parameters for this specific road segment and sends them out; the vehicle uses these parameters to predict the optimal kinetic energy recovery level for this long downhill road segment, formulates energy consumption control strategies, and thus maximizes the recovery of electrical energy and extends the driving range while ensuring safety.

[0022] For example, this can be applied to long-distance global path energy consumption planning in autonomous driving. Autonomous driving requires globally optimal energy consumption planning. The vehicle acquires target driving status information such as terrain and slope for the next tens of kilometers and uploads it to the cloud; the cloud combines the driving status information of the target road segment uploaded by a large number of vehicles to accurately calculate the energy consumption prediction parameters including undulating terrain and distributes them; the vehicle predicts the energy consumption curve of the entire long-distance road segment based on these parameters, and then formulates a global energy consumption control strategy to avoid the extra energy consumption caused by frequent acceleration and deceleration.

[0023] The target road segment in this application can have different scenarios. For example, it can be a road segment that the vehicle has not yet reached, i.e., a road segment that the vehicle will soon pass through, such as the road segment 0-500 meters ahead of the vehicle, or the road segment 500-1500 meters ahead. Alternatively, it can be a segmented road segment where the vehicle is currently located or a road segment that the vehicle has not yet passed through. For example, by dividing the vehicle's path into multiple segments according to a division method of 100 meters, 200 meters, 500 meters, 10 seconds, or 30 seconds, the road segment where the vehicle is currently located can be used as the target road segment, or the road segment that the vehicle has not yet passed through can be used as the target road segment.

[0024] When the current road segment is used as the target road segment, the vehicle's sensors can initially collect target driving status information after entering the segment, such as collecting it within the first 10 or 20 meters of a 100-meter segment. This information is then uploaded to the cloud to obtain corresponding energy consumption prediction parameters. Furthermore, the vehicle can continue collecting target driving status information after the first 10 or 20 meters to correct any previously collected data.

[0025] For example, a vehicle enters a 100-meter target road segment, travels 10 meters, and collects target driving status information for the first 10 meters. This target driving status information is uploaded to the cloud and compared with a set of driving status information in the cloud to determine the target driving status information set. Then, the energy consumption prediction parameters corresponding to this target driving status information set are obtained from the cloud to determine the vehicle's energy consumption control strategy for the next 90 meters. Since the vehicle continues to move forward, when it has traveled 20 meters, additional target driving status information for the next 10-20 meters is collected. At this time, the vehicle uploads the target driving status information for the 0-20 meters to the cloud. If the determined energy consumption prediction parameters are the same, there is no need to send them down, or after sending them down, the vehicle does not need to re-determine the energy consumption control strategy. However, if the energy consumption prediction parameters determined from the target driving status information for the 0-20 meters are different from those for the 0-10 meters, the new energy consumption prediction parameters can be sent down to the vehicle, and the vehicle uses the new parameters to determine the energy consumption control strategy for the next 80 meters.

[0026] When a target road segment is a road segment that the vehicle has not yet reached, its own sensors cannot collect target driving status information because the vehicle has not yet arrived at that segment. Instead, map data and the vehicle's cruise status can be used to obtain the target driving status information when the vehicle passes through that segment in the future. For example, by combining map data of the target road segment ahead, the vehicle can determine its predicted speed and acceleration when passing through that segment, thus obtaining the target driving status information. The vehicle can then upload this target driving status information in advance to obtain corresponding energy consumption prediction parameters, predicting the vehicle's energy consumption data for that segment and further determining the energy consumption control strategy. When the vehicle arrives at the target segment, it can then be controlled according to this energy consumption control strategy. Alternatively, if the vehicle has already arrived at the target segment, it can collect and report the target driving status information in real time, obtaining more accurate energy consumption prediction parameters to update the energy consumption control strategy in real time.

[0027] With this control method, the vehicle will always use the optimal energy consumption control strategy to pass through each road segment.

[0028] In this application, to obtain accurate energy consumption prediction parameters, the vehicle needs to acquire target driving state information on the target road segment and upload it to the cloud for comparison. The target driving state information acquired by the vehicle in this application can include various types of information, such as slope information, vehicle speed information, acceleration information, and battery information. If the target driving state information is collected by vehicle sensors, it can also include sampling information.

[0029] Slope information can include the mean slope, standard deviation of slope, and altitude difference of the target road segment. When determining the mean slope, the first 10% of extreme values ​​in the instantaneous slope data collected from the sampling points can be removed before calculating the mean. Removing extreme values ​​aims to suppress transient noise. Altitude difference refers to the complementary filtered altitude difference between the beginning and end of the target road segment, i.e., the change in altitude between the start and end positions of the target road segment. Vehicle speed information can include the mean vehicle speed and standard deviation of vehicle speed. Acceleration information can include the mean longitudinal acceleration, the maximum longitudinal acceleration, and the standard deviation of the first-order difference of acceleration. The standard deviation of the first-order difference of acceleration can be used to represent the severity of the road surface in the target segment. Battery information can include the mean motor torque and the change in battery state of charge. Sampling information can include the number of valid sampling points, which refers to the number of remaining sampling points after removing samples collected during sensor malfunctions.

[0030] Because other vehicles can collect driving status information from various road segments and upload it to the cloud in this application, in addition to the 11 data items mentioned above, other vehicles can also upload three other data items: vehicle identification, road segment sequence identification, and the confidence level of the driving status information. The vehicle identification is used to mark which uploaded driving status information belongs to the same vehicle. It should be noted that this vehicle identification is only used to distinguish vehicles and does not contain privacy information about the vehicle, user, or road segment. The road segment sequence identification is used to mark which road segment the currently uploaded driving status information belongs to for a particular vehicle. The confidence level is used to indicate the validity or accuracy of the driving status information uploaded by the vehicle. The current vehicle can collect and upload these three data items in addition to the 11 data items mentioned above after passing through the target road segment. Furthermore, this application can also upload information such as vehicle type and load capacity, similarly filtering out privacy information about the vehicle, user, and road segment.

[0031] The driving status information in this application may include one or more of the 11 and 3 data items mentioned above, combined with information such as vehicle type and load, totaling 16 data items. For example, the average gradient, average vehicle speed, average longitudinal acceleration, and change in battery state of charge can be uploaded as driving status information; or the average gradient, average vehicle speed, standard deviation of vehicle speed, and change in battery state of charge can be uploaded as driving status information; or all 16 data items can be uploaded as driving status information.

[0032] After acquiring the driving status information uploaded by each vehicle, the cloud can construct a driving status information set. The purpose of constructing this set is to categorize road segments. By using data uploaded by different vehicles, the energy consumption of different vehicles under a specific gradient on a given road segment can be calculated, and based on this relationship, energy consumption prediction parameters for different states on that road segment can be determined.

[0033] The cloud can treat a single upload of driving status information for a specific road segment by a single vehicle as a single data point, and categorize multiple data points from different vehicles on different road segments. During categorization, multiple data points can be set up based on one or more of the driving status information. For example, if categorized by the average gradient, intervals of the average gradient can be defined, and multiple data points with average gradients within the same interval can be grouped into one driving status information set. Similarly, if categorized by both average gradient and average speed, intervals of both average gradient and average speed can be defined, and multiple data points with average gradients and average speeds within the same interval can be grouped into one driving status information set.

[0034] In this way, the cloud divides the multiple driving status information data for each road segment into multiple driving status information sets. If the division is based on the average gradient, then different driving status information sets store the energy consumption corresponding to the vehicle status at different gradients. If the driving status information sets are divided based on the average vehicle speed, then different driving status information sets store the energy consumption corresponding to the vehicle status at different speeds.

[0035] Given the aforementioned correspondence, the cloud can solve for this relationship. For example, by substituting each data point from a driving status information set into a nonlinear function and obtaining the parameters of the nonlinear function through numerical approximation, these parameters can be sent to the vehicle as energy consumption prediction parameters.

[0036] After the vehicle obtains the energy consumption prediction parameters, it can predict the energy consumption data for the target road segment based on these parameters and the nonlinear function. If the energy consumption is too high, the vehicle speed or acceleration can be adjusted to predict energy consumption data under different driving conditions, thus obtaining the scenario with the minimum energy consumption. By generating an energy consumption control strategy, the vehicle passes through the target road segment with the minimum energy consumption.

[0037] In this system, the cloud constructs a driving status information set based on the driving status and energy consumption information of different vehicles on different road segments. Thus, for each road segment, the cloud obtains the driving status and energy consumption information of different vehicles. Therefore, it can construct the relationship between vehicle status and energy consumption in the driving status information for that road segment, and determine the energy consumption prediction parameters that match and are accurate for that road segment. In this way, when a vehicle obtains the energy consumption prediction parameters corresponding to the target road segment from the cloud, it can use these parameters to predict the accurate energy consumption data for the target road segment. Therefore, based on the accurate energy consumption data, targeted energy consumption control strategies can be formulated to reduce the energy consumption of the vehicle on the target road segment and solve the problem of excessive energy consumption when the vehicle passes through the previous road segment.

[0038] As an optional implementation method, such as Figure 3 As shown, based on the driving status information uploaded by different vehicles for different road segments, a driving status information set is constructed, including: S302 receives driving status information for different road sections uploaded by different vehicles; S304, extract multi-dimensional road condition and vehicle condition feature parameters from driving status information; S306, based on multi-dimensional road condition and vehicle condition characteristic parameters, divides the driving status information of different road sections into different driving status information sets.

[0039] In this application, after obtaining driving status information of different vehicles and different road segments uploaded by different vehicles in the cloud, when constructing the driving status information set, one or more pieces of information can be selected from the driving status information as multi-dimensional road condition and vehicle condition feature parameters to divide the driving status information set. If the driving status information includes the above 16 data items, there are multiple ways to select multi-dimensional road condition and vehicle condition feature parameters.

[0040] For example, if the average gradient is chosen as a multidimensional road and vehicle condition feature parameter, driving status information sets with different average gradient intervals can be constructed. The driving status information uploaded by different vehicles, after being divided, yields driving status information sets with different average gradient intervals. These different driving status information sets retain energy consumption information for road segments with different gradients.

[0041] If we choose the average vehicle speed as a multi-dimensional road and vehicle condition feature parameter, we can construct driving state information sets for different average vehicle speed ranges. These different driving state information sets then retain energy consumption information at different vehicle speeds.

[0042] If longitudinal acceleration is chosen as a multi-dimensional road and vehicle condition characteristic parameter, then driving state information sets for different longitudinal acceleration ranges can be constructed. These different driving state information sets then retain energy consumption information under different longitudinal accelerations.

[0043] Therefore, if we want to determine energy consumption information under different dimensions, the cloud can select different parameters from the driving status information as multi-dimensional road condition and vehicle condition characteristic parameters, and then construct the relationship between different parameters and energy consumption.

[0044] For example, taking driving status information including vehicle type, load, average vehicle speed, average gradient, average longitudinal acceleration, and average motor torque, and using the vehicle type, load, average vehicle speed, and average gradient as multi-dimensional road and vehicle condition feature parameters, different driving status information sets are obtained by classifying the situations of different vehicle types, loads, average vehicle speeds, and average gradients. These different driving status information sets record the energy consumption of a vehicle with a certain load and vehicle type when passing through the average gradient at the average vehicle speed, under different average longitudinal accelerations and different average motor torques.

[0045] For example, 10,000 data points are retrieved from the cloud, covering different vehicle models, loads, average vehicle speeds, average gradients, average longitudinal accelerations, and average motor torques. Using vehicle model, load, average speed, and average gradient as multi-dimensional road and vehicle condition feature parameters, the data is divided into two sets: Model A, with a 400kg load, 80km / h speed, and a 10-degree uphill slope, contains 400 data points, covering different average longitudinal accelerations and average motor torques. Model B, with an 800kg load, 60km / h speed, and a 5-degree uphill slope, contains 200 data points, also covering different average longitudinal accelerations and average motor torques. Therefore, each driving status data set retains energy consumption data for a specific vehicle model, load, speed, and gradient, with different average longitudinal accelerations and average motor torques.

[0046] In this application, since different multi-dimensional road condition and vehicle condition feature parameters can be selected to construct the driving status information set when constructing the driving status information set, a driving status information set recording the correspondence between different parameters and energy consumption can be constructed according to actual needs. Compared with constructing a fixed relationship between fixed parameters and energy consumption, the solution of this application improves the flexibility of constructing the driving status information set in the cloud and adapts to different scenarios. It only needs to determine which multi-dimensional road condition and vehicle condition feature parameters are available in different scenarios, which greatly improves the flexibility of constructing the driving status information set.

[0047] As an optional implementation method, the multi-dimensional road condition and vehicle condition characteristic parameters include the average gradient, average vehicle speed, and average longitudinal acceleration. Based on these parameters, the driving state information for different road segments is divided into different driving state information sets, including: Divide the vehicle into multiple gradient ranges, multiple speed ranges, and multiple acceleration ranges. Driving status information that has the same average gradient, average vehicle speed, and average longitudinal acceleration within the same gradient range is grouped into a single driving status information set.

[0048] This application proposes an example of multidimensional road and vehicle condition feature parameters including average slope, average vehicle speed, and average longitudinal acceleration. Under this example, different driving state information sets are constructed based on the different average slope, average vehicle speed, and average longitudinal acceleration.

[0049] Because the average values ​​of gradient, average vehicle speed, and average longitudinal acceleration vary greatly—for example, the average vehicle speed may vary between 0-120 km / h—this application proposes a method of dividing the driving state information set into multiple gradient intervals, multiple vehicle speed intervals, and multiple acceleration intervals to avoid creating too many intervals. In this method, the possible average gradient of a road segment is divided into different gradient intervals, the possible average vehicle speed is divided into multiple speed intervals, and the common average longitudinal acceleration of a vehicle is divided into different acceleration intervals. For example, a common gradient angle is [-10%, +10%], and if the step size is 0.5%, 40 gradient intervals are obtained. The average vehicle speed is divided into 12 speed intervals, 0-10 km / h, ..., 110-120 km / h, using a step size of 10 km / h. For the mean longitudinal acceleration, the acceleration range [-3, +3 m / s²] can be divided into 6 acceleration intervals with a step size of 1 m / s². This results in a total of 40 * 12 * 6, or 2880, driving status information sets. Therefore, the driving status information of all vehicles acquired from the cloud can be assigned to a specific driving status information set based on the range of mean vehicle speed, mean gradient, and mean longitudinal acceleration.

[0050] For example, taking driving status information including average gradient, average vehicle speed, average longitudinal acceleration, average motor torque, vehicle speed standard deviation, and battery state of charge change as an example, after dividing according to the above average vehicle speed, average gradient, and average longitudinal acceleration, 2880 driving status information sets are obtained. A certain driving status information set includes multiple driving status information points with average gradient within [0%, +0.5%], average speed within 0-10 km / h, average longitudinal acceleration within [0, +1 m / s²], and no restrictions on average motor torque, vehicle speed standard deviation, or battery state of charge change. Based on this driving status information set, the energy consumption of the vehicle when the average gradient is within [0%, +0.5%], the average speed is within 0-10 km / h, and the average longitudinal acceleration is within [0, +1 m / s²] can be summarized. If we take vehicle type, load, average gradient, average speed, average longitudinal acceleration, and average motor torque as multi-dimensional road and vehicle condition characteristic parameters, we can statistically analyze the energy consumption under different vehicle types, loads, average gradients, average speeds, average longitudinal accelerations, and average motor torques.

[0051] This application constructs a driving state information set based on the average slope, average vehicle speed, and average longitudinal acceleration. This allows for the construction of vehicle energy consumption data under different average slope, average vehicle speed, and average longitudinal acceleration. It can determine the correspondence between different average slope, average vehicle speed, and average longitudinal acceleration and vehicle energy consumption. Based on this relationship, energy consumption prediction parameters are calculated for predicting energy consumption data of the vehicle on the target road segment and controlling energy consumption strategies. This enables the construction of accurate control strategies for different average slope, average vehicle speed, and average longitudinal acceleration, thereby controlling the vehicle to reduce energy consumption.

[0052] As an optional implementation method, such as Figure 4 As shown, based on the driving state information set, the energy consumption prediction parameters are determined by the target driving state information set that matches the target driving state information set, including: S402, fuse different driving state information in the target driving state information set to obtain multiple fused driving state information; S404, determine the weighted variance of each fused driving status information; S406, Ridge regression is performed on all fused driving status information with fitting weights determined by their respective weighted variances to obtain energy consumption prediction parameters.

[0053] In this application, a set of energy consumption prediction parameters must be determined for each set of driving status information. Before determining the energy consumption prediction parameters, the driving status information in the driving status information set can be fused. The purpose of fusion is to remove outliers and retain normal values ​​to participate in the determination of energy consumption prediction parameters. The fusion operation involves merging multiple driving status information from a driving status information set into multiple fused driving status information. For example, if the driving status information set contains 1000 driving status information entries, and every 10 entries are merged into one fused driving status information entry, then 100 fused driving status information entries are obtained. The specific fusion method is described below.

[0054] The fused driving state information obtained after fusion can be used to determine energy consumption prediction parameters. This application introduces a parameter called the weighted variance of the fused driving state information. This parameter represents the degree of dispersion of the multiple driving state information used to obtain the fused driving state information. For example, for three driving state information points, the fused driving state information corresponds to a weighted variance of the three driving state information points. The larger the weighted variance, the greater the dispersion of the three driving state information points, and the less reliable the fused driving state information is. Therefore, when determining energy consumption prediction parameters, the weighted variance is used to determine the fitting weights, and the weighted variance is inversely proportional to the fitting weights. For fused driving state information with a larger weighted variance, the fitting weight in the process of determining energy consumption prediction parameters is smaller.

[0055] After determining the fitting weights, ridge regression can be performed on the fused driving status information according to the fitting weights to obtain energy consumption prediction parameters.

[0056] In this application, by fusing driving state information from a driving state information set, outliers can be removed. Then, the fused driving state information is fitted using the reciprocal of the weighted variance as the fitting weight. This allows for setting smaller fitting weights for fused driving state information with higher dispersion (lower accuracy or reliability), thus improving fitting accuracy. Finally, ridge regression fitting is used, by introducing... The regularization term suppresses the interference caused by highly correlated data in the fused driving state information. A situation of drastic fluctuations.

[0057] As an optional implementation method, such as Figure 5 As shown, different driving state information in the target driving state information set are fused to obtain multiple fused driving state information, including: S502, for each piece of driving status information in the target driving status set, perform the following operations to calculate its own comprehensive weight: determine the time decay coefficient based on the time elapsed since the upload; determine the comprehensive weight based on its own confidence level and the time decay coefficient; S504, the driving status information uploaded at the same time in the target driving status information set or each preset number of driving status information are fused according to their respective comprehensive weights to obtain the above-mentioned multiple fused driving status information.

[0058] This application proposes a method for fusing driving status information from any set of driving status information. The fusing process involves two main aspects: first, determining which driving status information to fuse; and second, determining the overall weight of each driving status information during fusing. Once these two aspects are determined, the corresponding driving status information can be fused according to the overall weight.

[0059] Regarding the first aspect, this application presents different integration methods.

[0060] For example, based on the upload time period, the system determines which driving status information should be merged into a single merged driving status information. This method allows setting a time window (e.g., the most recent 10 minutes, the most recent 1 hour, the most recent 1 day, or the most recent 7 days). The system will group all data within this time window into batches. Driving status information from the same batch can be merged to obtain a single merged driving status information. Therefore, multiple merged driving status information sets can be obtained from the same set.

[0061] Road conditions and traffic flow change dynamically with seasons, weather, and even road repairs. Therefore, fusing data according to time windows ensures that the fused data reflects the latest status under the current season / road conditions.

[0062] For example, a preset number of driving status information entries can be used to determine which entries are merged into a single merged driving status information entry. This method allows setting a preset number, such as 3, 5, or 10, but is not limited to these. For each preset number of driving status information entries in the set, they are merged into one merged driving status information entry. Taking a preset number of 5 entries as an example, a set of 1000 driving status information entries would be merged into 200 merged driving status information entries.

[0063] The preset quantity in this application is determined based on the number of driving status information in the driving status information set. The more driving status information in the driving status information set, the larger the preset quantity. This ensures that the difference in the number of fused driving status information obtained from the fusion of various driving status information sets is not significant, which facilitates ridge regression fitting to determine energy consumption prediction parameters.

[0064] Regarding the second aspect, the overall weight of each piece of driving status information during fusion is determined by two parts. The first part is the confidence level of the driving status information itself. This confidence level is uploaded when the driving status information is uploaded to the vehicle, and the method for determining the confidence level on the vehicle side will be explained below. The other part is the time decay coefficient. The time decay coefficient is determined based on the time elapsed since the upload. The longer the time elapsed since the upload, the smaller the time decay coefficient, indicating that data uploaded more recently has a smaller impact on the fused driving status information.

[0065] After obtaining the confidence level and time decay coefficient of the driving status information itself, the comprehensive weight can be calculated using Formula 1.

[0066] w_i = confidence_i · exp(-Δt_i / τ) (1) Where confidence_i is the confidence level of the i-th data, Δt_i is the time (in days) since the i-th record was uploaded, and τ=30 days is the half-life scale to ensure that new data has high weight and old data gradually fades out.

[0067] After calculating the comprehensive weight, the following formula 2 can be used to fuse multiple driving status information to obtain fused driving status information.

[0068] x = (Σ_i w_i · x_i) / (Σ_i w_i) (2) x_i represents the driving status information. To integrate driving status information.

[0069] In this application, during the fusion of driving status information, the driving status information is fused according to the comprehensive weight of each driving status information. Since the comprehensive weight is determined based on the confidence level and time decay coefficient of the driving status information itself, the impact of data accuracy and time decay is fully considered when fusing driving status information, thereby improving the accuracy of the fused driving status information.

[0070] As an optional implementation, after constructing a driving status information set based on the driving status information of different road segments uploaded by different vehicles, the method further includes: For the current driving status information set, determine the median and median absolute deviation of the driving parameters of the driving status information; determine the filtering threshold based on the median absolute deviation; delete driving status information whose driving parameters differ from the median by more than the filtering threshold from the current driving status information set; or... For the current driving status information set, determine the geometric elevation difference of the driving status information. The geometric elevation difference is determined based on the average slope of the driving status information and the horizontal driving distance. If the relative difference between the geometric elevation difference and the actual elevation difference in the driving status information is greater than the difference threshold, the corresponding driving status information is deleted from the current driving status information set.

[0071] After constructing the driving status information set, this application can fuse the driving status information in the set through the above process to obtain fused driving status information, and further determine the energy consumption prediction parameters. Before fusing the driving status information, this application can also filter the driving status information in the set, deleting driving status information with large errors or high accuracy.

[0072] This application provides several methods for filtering driving status information from a set of driving status information.

[0073] The first method is the median constraint method. In this method, the median of each driving parameter (such as average vehicle speed, average gradient, etc.) in the driving status information set is calculated. Then, the absolute distance of each driving parameter to the median is calculated, and the median of these absolute distances is calculated to obtain the median absolute deviation. A filtering threshold is obtained by multiplying the median absolute deviation by 3-5 times. This 3-5 times factor is determined based on historical experience. If the difference between a driving parameter and the median is greater than the filtering threshold, the corresponding driving status information is filtered out. This method uses the median as the core condition for filtering driving status information. This approach can eliminate the problem of inaccuracies when using the mean or standard deviation to filter data due to the significant impact of extremely large or small individual data points on the average.

[0074] The second method is based on elevation difference comparison. In this method, the average slope and horizontal travel distance from the driving status information can be used to calculate the vehicle's geometric elevation difference in the vertical direction. If the calculated elevation difference differs significantly from the actual elevation difference in the driving status information, it indicates that the driving status information is inaccurate, and therefore, that driving status information is removed from the driving status information set.

[0075] This application filters out inaccurate driving status information by filtering the driving status information in the driving status information set. Moreover, by employing cross-validation using both median constraints and elevation difference comparison, it can accurately filter out erroneous driving status information in the driving status information set.

[0076] As an optional implementation method, receiving the target driving status information of the target road segment uploaded by the target vehicle includes: Receive the target driving status information determined by the vehicle's sensor signals when the vehicle is located within the target road segment; or, When the vehicle has not entered the target road segment, the target driving status information is determined by the acquired map data.

[0077] In this application, the method of acquiring target driving status information for a target road segment varies depending on the circumstances. If the vehicle has already entered the target road segment, it can use its own sensors to collect target driving status information. For example, target driving status information can be collected in the first 10 or 20 meters of a 100-meter road segment and then uploaded to the cloud to obtain corresponding energy consumption prediction parameters. As the vehicle continues to move forward, target driving status information is continuously collected until the vehicle has traversed the entire target road segment. If the vehicle has traversed the entire target road segment, the collected target driving status information best represents that target road segment. The collected target driving status information for the target road segment can be uploaded to the cloud, where it is categorized into a driving status information set.

[0078] For situations where the vehicle has not yet reached the target road segment, since the vehicle's own sensors cannot collect target driving status information, map data and the vehicle's cruise status can be used to obtain target driving status information when the vehicle passes through that segment in the future. For example, static map data can be acquired, which may include the slope information of the target road segment. Additionally, traffic rules and traffic control events for the target road segment can be included. Then, the vehicle's current status, such as speed, acceleration, motor torque, and battery status, can be obtained. Next, based on the traffic rules and traffic control events of the target road segment, the vehicle's speed, acceleration, motor torque, and battery status on the target road segment can be calculated, thus obtaining the driving status information for that segment.

[0079] In this application, regardless of whether the vehicle enters the target road segment or not, the driving status information of the target road segment can be obtained to determine the energy consumption prediction parameters of the vehicle in the target road segment. Therefore, the vehicle can pre-plan the energy consumption prediction parameters and energy consumption control strategies for future roads. The cloud can send the corresponding energy consumption prediction parameters to the vehicle, so even if it enters a weak network or no network environment, it can formulate an accurate energy consumption control strategy to control the vehicle.

[0080] As an optional implementation, in this application, the energy consumption prediction parameters are used to simulate different torques and energy recovery coefficients for the vehicle, causing the vehicle's speed and acceleration to change within the speed and acceleration ranges corresponding to the target road segment. Based on the energy consumption prediction parameters, the energy consumption of the vehicle passing through the target road segment at different speeds and accelerations is determined, resulting in energy consumption data, which is then used to determine the energy consumption control strategy. In other words, the energy consumption data predicted by the vehicle based on the energy consumption prediction parameters for passing through the target road segment in this application can include: The vehicle's speed and acceleration are simulated to vary within the speed and acceleration ranges corresponding to the target road segment by simulating different torques and energy recovery coefficients. Determine the energy consumption of a vehicle traveling through a target road segment at different speeds and with different accelerations to obtain energy consumption data.

[0081] In this application, after the vehicle obtains the energy consumption prediction parameters, the energy consumption data of the vehicle on the target road segment can be predicted based on the parameters.

[0082] In this application, the following formula 3 can be used to predict energy consumption data.

[0083] E_est = α·slope_mean + β·v_mean + γ·a_x_mean + δ·Δt + ε(3) Where α, β, γ, δ, ε are the energy consumption prediction parameters sent from the cloud, E_est is the energy consumption data, ε is the static energy consumption data, used to represent the energy consumption lost due to wheel set, wind resistance, etc. slope_mean is the average slope of the target road segment, v_mean is the average vehicle speed of the target road segment, a_x_mean is the average longitudinal acceleration of the target road segment, and Δt is the estimated time to pass through the target road segment.

[0084] Since the torque and energy recovery coefficient of a vehicle can vary, affecting the vehicle's speed and acceleration, we can simulate different torques and energy recovery coefficients to calculate the energy consumption data of a vehicle passing through a target road segment under different average vehicle speeds and different average longitudinal accelerations, thereby obtaining the combination of torque and energy recovery coefficients that minimizes energy consumption data.

[0085] In this application, different torques and energy recovery coefficients are simulated, and the energy consumption data of the vehicle under different speeds and longitudinal accelerations is calculated based on the simulated torques and energy recovery coefficients. Thus, the optimal combination of torque and energy recovery coefficients can be selected to control the vehicle to pass through the target road segment with the minimum energy consumption.

[0086] As an optional implementation, the above method further includes: receiving the driving status information and total confidence score of the target road segment uploaded by the vehicle, wherein the driving status information is information obtained by the vehicle after performing privacy filtering on the collected driving data; the total confidence score is calculated based on the effective sampling rate, sensor health, and standard deviation of the slope within the target road segment; wherein driving status information with a total confidence score less than or equal to a first upload threshold is not uploaded.

[0087] In this application, after controlling the vehicle to pass through the target road segment, the vehicle can perform privacy filtering on the driving data collected when the vehicle passes through the target road segment to obtain the driving status information of the target road segment; based on the effective sampling rate of the target road segment, the sensor health status, and the standard deviation of the slope within the segment, the total confidence of the driving status information of the target road segment is calculated; if the total confidence is less than or equal to the first upload threshold, the upload of the driving status information of the target road segment is prohibited; if the total confidence is greater than the first upload threshold, the driving status information of the target road segment and the total confidence are uploaded.

[0088] In this application, after the vehicle passes through the target road segment, various information collected by the vehicle's sensors can be obtained. At this point, this application can filter the various information to obtain driving status information.

[0089] The method used in this application to filter information is essentially a scheme for extracting driving status information from various information collected by sensors. For example, the various information collected by sensors includes privacy information about users, vehicles, and road segments, as well as driving status information. What this application aims to do is to extract only driving status information from these various data sources, without acquiring privacy data. This includes extracting average vehicle speed, average longitudinal acceleration, average road slope, energy consumption data, etc., as driving status information and uploading them to the cloud. If the extracted information is identified as privacy information through tag comparison, it will not be uploaded. The extracted driving status information can be uploaded to the cloud for the cloud to generate a driving status information set.

[0090] Furthermore, this application includes an additional judgment step to determine whether the driving status information can be uploaded to the cloud, and whether total confidence data is required during upload. This judgment step can be applied before any vehicle uploads driving status information for any road segment.

[0091] The core logic of this judgment step lies in calculating a total confidence level based on the effective sampling rate, sensor health, and standard deviation of the slope within the target road segment when the vehicle passes through it. This total confidence level represents the accuracy or reliability of the driving status information for the target road segment. This total confidence level can be calculated using the following formula: confidence = w1· (n / N_expected) + w2· exp(-σ_slope / σ_0) + w3· H_sensor (4) Here, w1, w2, and w3 are weights that can be preset. (n / N_expected) is the effective sampling rate, where n is the number of effective sampling points and N_expected is the total number of sampling points. σ_slope is the standard deviation of the slope within the segment (σ_0 is the normalized scaling constant, taken as 0.5°). H_sensor∈[0,1] represents the sensor health, indicating whether the sensor is in normal or abnormal condition; the larger the value, the better the sensor condition.

[0092] The higher the calculated total confidence score, the better the driving status information of the target road segment. This application allows setting different upload thresholds, such as a first upload threshold and a second upload threshold, where the first upload threshold is lower than the second upload threshold. If the total confidence score of the target road segment is lower than the first upload threshold, the driving status information is considered of poor quality, and therefore, it is not uploaded. If the total confidence score is between the first and second upload thresholds, the driving status information can be uploaded. During upload, it should be marked that the confidence score is between the first and second upload thresholds. Conversely, if the total confidence score is greater than the second upload threshold, the driving status information is considered of high quality and can be uploaded. In this case, it should also be marked that the total confidence score is greater than the second upload threshold.

[0093] After acquiring different driving status information, the cloud needs to normalize the total confidence of driving status information whose total confidence falls between the first and second upload thresholds. The purpose of normalization is to align it with the total confidence of driving status information whose total confidence is greater than the second upload threshold.

[0094] The normalization method involves summing the total confidence scores of different driving status information points between the first and second upload thresholds to obtain a median sum. Then, the total confidence score of each driving status information point is divided by this median sum to obtain the normalized total confidence score for each driving status information point. After the cloud obtains the driving status information and the total confidence score (or the normalized total confidence score), the total confidence score (or the normalized total confidence score) can be used as the confidence score of the driving status information for calculating the comprehensive weight.

[0095] In this application, by setting an upload threshold, information with low accuracy is filtered out when vehicles upload driving status information, ensuring the accuracy of the driving status information set constructed in the cloud. Furthermore, the cloud normalizes the total confidence of driving status information with a total confidence level between the first and second upload thresholds, thus avoiding the amplification or reduction of the fused driving status information due to inconsistent weights during the fusion process, thereby improving the accuracy of the fused driving status information. This further enhances the accuracy of constructing the driving status information set.

[0096] This application also provides a vehicle control system, such as Figure 6 As shown, it includes: Vehicle-side 602 is used to obtain the target driving status information of the vehicle on the target road segment; and to send the target driving status information to the cloud. The cloud-based 604 function is used to construct a driving status information set based on driving status information uploaded by different vehicles on different road segments; determine energy consumption prediction parameters based on the target driving status information set that matches the target driving status information; and send the energy consumption prediction parameters to the vehicle. The vehicle-side 602 is also used to predict energy consumption data for passing through the target road segment based on energy consumption prediction parameters; based on the energy consumption data, it determines the energy consumption control strategy for the target road segment, and controls the vehicle in accordance with the energy consumption control strategy in the target road segment.

[0097] Please refer to the examples above for examples of this application, which will not be repeated here.

[0098] This application also provides a vehicle, such as Figure 7 As shown, the vehicle includes: The acquisition module 702 is used to acquire the target driving status information of the vehicle on the target road segment; The transmission module 704 is used to send the target driving status information to the cloud; and to receive the energy consumption prediction parameters determined by the cloud based on the target driving status information set that matches the target driving status information, wherein the target driving status information set includes driving status information of different road segments uploaded by different vehicles; Prediction module 706 is used to predict the energy consumption demand of the target road segment based on energy consumption prediction parameters; The determination module 708 is used to determine the energy consumption control strategy for the target road segment based on energy consumption demand. The control module 710 is used to control vehicles according to the energy consumption control strategy on the target road segment.

[0099] The vehicles covered by this application may be gasoline-powered vehicles, pure electric vehicles, plug-in hybrid vehicles, or range-extended electric vehicles, but are not limited to these.

[0100] The vehicle described in this application can reduce energy consumption in different scenarios.

[0101] For example, in the scenario of optimizing energy consumption in trunk logistics, the vehicle acquires target driving status information of the target road segment (such as a long downhill section of a highway) and uploads it to the cloud; the cloud aggregates driving status information and energy consumption information of hundreds or thousands of vehicles on this road segment, constructs a target driving status information set, and fits energy consumption prediction parameters for this specific road segment and sends them out; the vehicle uses these parameters to predict the optimal kinetic energy recovery intensity of the long downhill section, formulates energy consumption control strategies, and thus maximizes the recovery of electrical energy and extends the driving range while ensuring safety.

[0102] For example, in the scenario of long-distance global path energy consumption planning for autonomous driving, autonomous driving requires globally optimal energy consumption planning. The vehicle acquires target driving status information such as terrain and slope for the next tens of kilometers of the target road segment and uploads it to the cloud; the cloud combines the driving status information of the road segment uploaded by a large number of vehicles to accurately calculate the energy consumption prediction parameters including undulating terrain and distributes them; the vehicle predicts the energy consumption curve of the entire long-distance road segment based on these parameters, and then formulates a global energy consumption control strategy to avoid the extra energy consumption caused by frequent acceleration and deceleration.

[0103] In this application, since the cloud constructs a driving status information set based on the driving status information and energy consumption information of different vehicles on different road segments, it obtains the driving status information and energy consumption information of different vehicles for each road segment. Therefore, it can construct the relationship between vehicle status and energy consumption in the driving status information for that road segment, and determine the energy consumption prediction parameters that match and are accurate for that road segment. In this way, the vehicle can obtain the energy consumption prediction parameters corresponding to the target road segment from the cloud, and use these parameters to predict the accurate energy consumption data on the target road segment. Therefore, it can formulate targeted energy consumption control strategies based on accurate energy consumption data, reduce the energy consumption of the vehicle on the target road segment, and solve the problem of the vehicle consuming too much energy when passing through the previous road segment.

[0104] Other examples of this application can be found in the examples above, and will not be repeated here.

[0105] like Figure 8 As shown, this application embodiment provides a vehicle, including a processor 111, a communication interface 112, a memory 113, and a communication bus 114, wherein the processor 111, the communication interface 112, and the memory 113 communicate with each other through the communication bus 114.

[0106] Memory 113 is used to store computer programs.

[0107] In one embodiment of this application, the processor 111, when executing a program stored in the memory 113, implements the method provided in any of the foregoing method embodiments.

[0108] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method provided in any of the foregoing method embodiments.

[0109] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0110] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented using software plus a general-purpose hardware platform, or of course, using hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0111] It should be understood that the terminology used herein is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting. Unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “described” as used herein may also include the plural forms. The terms “comprising,” “including,” “containing,” and “having” are inclusive and therefore indicate the presence of the stated features, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, elements, components, and / or combinations thereof. The method steps, processes, and operations described herein are not construed as requiring them to be performed in a particular order described or illustrated unless the order of performance is explicitly indicated. It should also be understood that additional or alternative steps may be used.

[0112] The above description is merely a specific embodiment of this application, enabling those skilled in the art to understand or implement this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A vehicle control method, characterized in that, include: Based on the driving status information uploaded by different vehicles on different road segments, a driving status information set is constructed; Receive target driving status information of the target road segment uploaded by the target vehicle; Based on all driving status information sets, the energy consumption prediction parameters are determined from the target driving status information set that matches the target driving status information. The energy consumption prediction parameters are sent to the target vehicle. The energy consumption prediction parameters are used by the target vehicle to predict the energy consumption data when passing through the target road segment and to determine the energy consumption control strategy of the target road segment based on the energy consumption data.

2. The method according to claim 1, characterized in that, Based on the driving status information uploaded by different vehicles for different road segments, a driving status information set is constructed, including: Receive driving status information for different road sections uploaded by different vehicles; Extract multi-dimensional road condition and vehicle condition feature parameters from the driving status information; Based on the multidimensional road condition and vehicle condition feature parameters, the driving status information of different road segments is divided into different driving status information sets.

3. The method according to claim 2, characterized in that, The multi-dimensional road condition and vehicle condition feature parameters include average gradient, average vehicle speed, and average longitudinal acceleration. Based on these multi-dimensional road condition and vehicle condition feature parameters, the driving state information of different road segments is divided into different driving state information sets, including: Divide the vehicle into multiple gradient ranges, multiple speed ranges, and multiple acceleration ranges. The driving status information that the average slope value belongs to the same slope range, the average vehicle speed value belongs to the same vehicle speed range, and the average longitudinal acceleration value belongs to the same acceleration range is grouped into a driving status information set.

4. The method according to claim 1, characterized in that, The energy consumption prediction parameters are determined based on the target driving state information set that matches the target driving state information from all driving state information sets, including: The different driving state information in the target driving state information set are fused to obtain multiple fused driving state information; Determine the weighted variance for each fused driving status information; Ridge regression is performed on all the fused driving status information with fitting weights determined by their respective weighted variances to obtain the energy consumption prediction parameters.

5. The method according to claim 4, characterized in that, By fusing different driving state information from the target driving state information set, multiple fused driving state information are obtained, including: For each piece of driving status information in the target driving status set, the following operations are performed to calculate its own comprehensive weight: determine the time decay coefficient based on the time elapsed since the upload; determine the comprehensive weight based on its own confidence level and the time decay coefficient; The driving status information uploaded at the same time in the target driving status information set, or each preset number of driving status information, is fused according to their respective comprehensive weights to obtain the multiple fused driving status information.

6. The method according to claim 1, characterized in that, After constructing a driving status information set based on the driving status information uploaded by different vehicles for different road segments, it also includes: For the current driving status information set, determine the median and median absolute deviation of the driving parameters of the driving status information; determine a filtering threshold based on the median absolute deviation; delete driving status information whose driving parameters differ from the median by more than the filtering threshold from the current driving status information set; or... For the current driving status information set, determine the geometric elevation difference of the driving status information. The geometric elevation difference is determined based on the average slope of the driving status information and the horizontal driving distance. If the relative difference between the geometric elevation difference and the actual elevation difference in the driving status information is greater than the difference threshold, then the corresponding driving status information is deleted from the current driving status information set.

7. The method according to any one of claims 1 to 6, characterized in that, The target driving status information of the target road segment uploaded by the target vehicle includes: Receive the target driving status information determined by the vehicle's sensor signals when the vehicle is located within the target road segment; or, When the vehicle has not entered the target road segment, the target driving status information is determined by the acquired map data.

8. The method according to any one of claims 1 to 6, characterized in that, The energy consumption prediction parameters are used to simulate different torques and energy recovery coefficients of the vehicle, so that the vehicle speed and acceleration change within the speed range and acceleration range corresponding to the target road segment. Based on the energy consumption prediction parameters, the energy consumption of the vehicle passing through the target road segment at different speeds and different accelerations is determined, and the energy consumption data is obtained to determine the energy consumption control strategy.

9. The method according to any one of claims 1 to 6, characterized in that, Also includes: The system receives the driving status information and total confidence score of the target road segment uploaded by the vehicle. The driving status information is obtained by the vehicle after performing privacy filtering on the collected driving data. The total confidence score is calculated based on the effective sampling rate, sensor health, and standard deviation of the slope within the target road segment. Driving status information with a total confidence level less than or equal to the first upload threshold was not uploaded.

10. A vehicle, characterized in that, include: Memory, used to store computer programs; as well as A processor for calling and running the computer program from the memory, causing the vehicle to perform the method as described in any one of claims 1 to 9.

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