Method, apparatus, device and storage medium for generating driving suggestions

Abstract

A method for generating driving advice is provided, including: obtaining N driving records, where the N driving records are from at least two vehicles, each driving record includes a mapping relationship between a driving period and an acceleration value, and N is an integer greater than 1 (310); determining a plurality of acceleration metric values based on the acceleration values in the N driving records, where each vehicle corresponds to at least one acceleration metric value, and the acceleration metric values are positively correlated with the acceleration values (320); determining a metric threshold according to the plurality of acceleration metric values (330); and generating driving advice based on the metric threshold and the driving record corresponding to any one of the at least two vehicles (340). An apparatus, a device and a computer readable storage medium for generating driving advice are also provided.

Description

Methods, apparatus, devices, and storage media for generating driving recommendations Technical Field

[0001] This application generally relates to the automotive field, and specifically to a method, apparatus, device, and computer-readable storage medium for generating driving suggestions. Background Technology

[0002] With the increasing popularity of new energy vehicles, energy consumption has become a more significant issue due to driver distance anxiety. Driver skills and behavior are crucial factors influencing vehicle energy consumption.

[0003] Drivers typically receive advice on driving skills and behavior based on subjective opinions, such as guidance from instructors or recommendations from vehicle salespeople.

[0004] However, these tips only provide general principles and are difficult to tailor to the specific circumstances of individual drivers. Drivers need to improve their driving skills and behaviors based on their own driving experience to maintain their current vehicle mileage. Summary of the Invention

[0005] According to embodiments of this application, a method, apparatus, and storage medium for generating driving suggestions are provided. The technical solution is as follows:

[0006] According to the first aspect of this application, a method for generating driving suggestions is provided.

[0007] Obtain N driving records, wherein the N driving records are from at least two vehicles, and each driving record includes a mapping relationship between driving period and acceleration value, where N is an integer greater than 1;

[0008] Multiple acceleration metrics are determined based on the acceleration values ​​in the N driving records, wherein each vehicle corresponds to at least one acceleration metric, and the acceleration metric is positively correlated with the acceleration value;

[0009] A measurement threshold is determined based on the plurality of acceleration metrics; and

[0010] Driving suggestions are generated based on the metric threshold and the driving records corresponding to any one of the at least two vehicles.

[0011] According to a second aspect of this application, a device for generating driving suggestions is provided, comprising:

[0012] The record acquisition module is configured to acquire N driving records, wherein the N driving records are from at least two vehicles, and each driving record includes a mapping relationship between driving period and acceleration value, wherein N is an integer greater than 1;

[0013] The metric determination module is configured to determine multiple acceleration metric values ​​based on the acceleration values ​​in the N driving records, wherein each vehicle corresponds to at least one acceleration metric value, and the acceleration metric value is positively correlated with the acceleration value.

[0014] The threshold determination module is configured to determine a measurement threshold based on the plurality of acceleration measurement values.

[0015] The suggestion generation module is configured to generate driving suggestions based on the metric threshold and the driving records corresponding to any one of the at least two vehicles.

[0016] According to a third aspect of the embodiments of this application, an electronic device is provided, comprising: a memory for storing computer-readable instructions; and a processor for reading the computer-readable instructions stored in the memory to perform the above-described method for generating driving suggestions.

[0017] According to a fourth aspect of the embodiments of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, performs the above-described method for generating driving suggestions.

[0018] By using the above-described method to generate targeted driving suggestions based on the driver's own driving data, the driving suggestions can be tailored to the driver's specific situation, improving their accuracy and thus helping to verify the vehicle's mileage. It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this disclosure. Attached Figure Description

[0019] The accompanying drawings are incorporated in and form a part of this specification. The drawings illustrate embodiments consistent with this application and, together with the specification, serve to explain the principles of this application. Clearly, the drawings described below are only some embodiments of this application. Those skilled in the art can derive other drawings based on these drawings without any inventive effort.

[0020] Figure 1 shows the structure of a system for driving advice according to an exemplary embodiment.

[0021] Figure 2 is a schematic diagram of the data flow of the system used for driving advice in Figure 1.

[0022] Figure 3 is a flowchart of a method for generating driving suggestions in an embodiment of this application.

[0023] Figure 4 is an example distribution of longitudinal acceleration measurement according to an exemplary embodiment.

[0024] Figure 5 is an example distribution of the lateral acceleration metric according to an exemplary embodiment.

[0025] Figure 6 is a block diagram illustrating a device for generating driving suggestions according to an exemplary embodiment.

[0026] Figure 7 is a schematic diagram of an electronic device in one embodiment of this application. Detailed Implementation

[0027] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, exemplary embodiments may be implemented in various forms and should not be construed as limited to the examples described herein; rather, these exemplary embodiments are provided to make the description of this application more complete and comprehensive, and to fully convey the concepts of the exemplary embodiments to those skilled in the art. The drawings are merely schematic diagrams of this application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted.

[0028] Furthermore, the described features, structures, or characteristics may be incorporated into one or more example embodiments in any suitable manner. Numerous specific details are provided in the following description to fully understand the exemplary embodiments of this application. However, those skilled in the art will recognize that one or more specific details may be omitted, or other methods, components, steps, etc., may be employed by implementing the technical solutions of this application. In other instances, well-known structures, methods, implementations, or operations have not been shown or described in detail to avoid obscuring aspects of this disclosure.

[0029] Some of the block diagrams shown are functional entities, which do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, or in the form of one or more hardware modules or integrated circuits, or in the form of different network and / or processor devices and / or microcontroller devices.

[0030] First, the general approach is to develop a system that provides suggestions to a driver based on vehicle maneuvers. For clarity, refer to Figure 1. Figure 1 shows the structure of a system for driving suggestions according to an exemplary embodiment. Vehicle 101's mobility data is transmitted to a business server 102 and stored in a database. The business server 102 calculates a weighted average of metrics related to the collected mobility data over a predefined time period (e.g., one month or one year). Driving suggestions are generated for each vehicle based on the weighted average and are accessible by a user terminal 103. For example, the driver can view the driving suggestions on a website via computer or have them sent to the driver's mobile phone via SMS.

[0031] It is understood that the system architecture in Figure 1 is an exemplary architecture, and the actual system architecture may be different and more complex. The server shown in Figure 1 can actually be a single server, a cluster of multiple servers, or a cloud server. Those skilled in the art can adopt a suitable system architecture according to actual needs, and this application does not limit this.

[0032] Please now refer to Figure 2. Figure 2 is a schematic diagram of the data flow of the system for driving recommendations in Figure 1 according to an exemplary embodiment. Vehicle control data may come from the vehicle itself, such as an in-vehicle system, or from a mobile device carried by the driver, such as a mobile phone. The collected data can be stored in a database on a server and analyzed to generate driving recommendations for each vehicle. The driver can then access the generated driving recommendations via an API provided by the server, such as through a web browser, text message, email, and mobile application.

[0033] Figure 3 is a flowchart of a method for generating driving suggestions according to an exemplary embodiment. Referring to Figure 3, Figure 3 is a flowchart of a method for generating driving suggestions in one embodiment of this application, the method including the following steps.

[0034] In step 310, N driving records are obtained, wherein the N driving records are from at least two vehicles, and each driving record includes a mapping relationship between driving period and acceleration value, wherein N is an integer greater than 1;

[0035] In this embodiment, the method can be executed by a server. The server retrieves N driving records from a database. The server can perform this step periodically or in response to a user's command. The driving records can be sent by vehicles communicating with the server and stored in the database. The N driving records retrieved can be all driving records received within an appropriate time period (e.g., one year). In one embodiment, the user can specify filtering conditions for the driving records, and the server will generate a query based on the filtering conditions and retrieve driving records from the database that meet the filtering conditions. The filtering conditions can include vehicle brand, model, and recording time. For example, the server can retrieve driving records of a Trumpchi GS8 recorded last year.

[0036] Driving records are managed on a daily basis. Typically, a vehicle sends its driving record to the server before the engine is turned off. Therefore, a day's driving record usually includes data from at least one trip. A trip includes the process between starting and stopping the vehicle's engine. During a trip, the vehicle typically starts and stops multiple times. A driving period refers to the process between when the vehicle begins to move and when it stops. That is, a driving record includes at least one trip, and one trip includes at least one driving period. Driving records include acceleration values ​​corresponding to each driving period. In this embodiment, the acceleration value refers to acceleration per minute. However, in other embodiments, the acceleration value can be acceleration per second, acceleration over 5 minutes, or other suitable values. Since acceleration values ​​are counted in minutes, the number of acceleration values ​​in each driving record is determined based on the duration of the driving period. For example, if a driving period lasts 10 minutes and 50 seconds, then the driving record for that driving period should contain 11 acceleration values.

[0037] In step 320, multiple acceleration metrics are determined based on the acceleration values ​​in the N driving records, wherein each vehicle corresponds to at least one acceleration metric, and the acceleration metric is positively correlated with the acceleration value.

[0038] In this embodiment, acceleration metrics are calculated relative to driving periods. Specifically, in N driving records, each vehicle typically has multiple driving periods, and acceleration metrics are calculated for each driving period. For example, if 20 driving records contain 200 driving periods, 200 acceleration metrics will be determined. Acceleration metrics are positively correlated with acceleration values, meaning that the larger the acceleration value, the larger the acceleration metric. Acceleration metrics are used to measure the total acceleration of the vehicle during driving. Larger acceleration metric values ​​indicate relatively rapid changes in vehicle speed. Conversely, smaller acceleration metric values ​​indicate relatively smooth changes in vehicle speed.

[0039] In step 330, a measurement threshold is determined based on the plurality of acceleration measurement values.

[0040] In this embodiment, the measurement threshold can be selected from multiple acceleration measures, or it can be calculated based on multiple acceleration measures. For example, the measurement threshold can be the average or median value of multiple acceleration measures. Mathematical modeling or function fitting can also be performed based on multiple acceleration measures to obtain a distribution or distribution function. Then, based on the distribution, a value that helps distinguish specific cases from a few special cases is determined as the measurement threshold.

[0041] In step 340, driving suggestions are generated based on the metric threshold and the driving records corresponding to any of the at least two vehicles.

[0042] In this embodiment, driving suggestions can consist of several driving suggestion items, which can be preset and stored in the server. When generating driving suggestions, the server analyzes the vehicle's driving record and determines the vehicle's acceleration metric. Based on the vehicle's acceleration metric and a metric threshold, the server selects appropriate driving suggestion items and combines them to generate driving suggestions. For example, driving suggestion items may include: A. Please lightly press the accelerator pedal; C. Please turn more smoothly; D. Please lightly press the brake pedal. If the server believes that the vehicle accelerates and decelerates too quickly, the driving suggestion will include items A and C.

[0043] In the technical solution of this application embodiment, a metric threshold for generating driving suggestions is dynamically generated each time a driving suggestion is generated, thereby improving the accuracy of the driving suggestions. Furthermore, driving records are collected from multiple vehicles; therefore, when judging a driver's driving behavior, the driving behavior and skills of other drivers are considered, making the driving suggestions more reasonable, because the judgment of driving behavior is based on the actual driving behavior of all drivers, rather than purely static information.

[0044] In one embodiment of this application, step 320, which determines multiple acceleration metrics based on acceleration values ​​in N drive records, may further include the following steps:

[0045] Step 321: For each driving record, calculate the acceleration weight value based on the acceleration value and the preset basic acceleration value;

[0046] Step 322: Perform an integral calculation based on the acceleration value, the duration of the driving period, and the acceleration weight value to obtain the acceleration metric value.

[0047] In this embodiment, an acceleration weight value is calculated based on the acceleration value and a preset basic acceleration value. Specifically, the preset basic acceleration value represents a normal acceleration value determined based on the behavior of most drivers. This value may differ for different types or models of vehicles. For example, the acceleration value of a car is typically greater than that of a truck or bus, so the preset basic acceleration value for a car may be greater than that for a truck. The preset basic acceleration value can be used to normalize acceleration values. For example, the preset basic acceleration value is set as a scaling factor to scale acceleration values ​​to the same size to assess driver skill across different types of vehicles.

[0048] The method for calculating acceleration metrics is as follows:

[0049]

[0050] Where T is the driving duration in minutes, a(t) is the acceleration value, and f(a(t)) is the acceleration weight value. It can be seen that the acceleration weight value is the integral of the weighted acceleration value over the entire driving period, and then normalized by the driving duration, which reflects the driving behavior throughout the entire driving period.

[0051] In one embodiment of this application, for each driving record, step 321 calculates an acceleration weight value based on the acceleration value and a preset basic acceleration value:

[0052] The difference is obtained by subtracting the preset basic acceleration value from the acceleration value.

[0053] If the difference is greater than zero, the acceleration weight value is calculated based on the difference and the preset slope coefficient;

[0054] If the difference is less than or equal to zero, the acceleration weight value is set to zero.

[0055] An acceleration weight value is used to penalize larger acceleration values. Specifically, the acceleration weight value is calculated as follows:

[0056]

[0057] Where x is the acceleration value per minute during the driving period, a 0 This is the preset basic acceleration value, which is the slope coefficient used to determine the rate of increase in fines. It is a configurable parameter, typically set to 1.5. The purpose of the preset basic acceleration value is to ignore smaller acceleration values. As can be seen from the calculation method of the acceleration weight, the difference is calculated using x - a0, 0, and the larger value between the difference and 0 is used to calculate the acceleration weight. Therefore, if the difference is greater than zero, the acceleration weight value is calculated based on the difference and the preset slope coefficient; if the acceleration value x is less than or equal to the preset basic acceleration value a0, meaning the difference is less than or equal to zero, then the acceleration weight value will be 0, which will make the acceleration metric value 0, meaning the driver's behavior within one minute is considered appropriate.

[0058] In one embodiment of this application, step 322, which involves integrating the acceleration value, the duration of the driving period, and the acceleration weight value to obtain an acceleration metric, includes the following steps:

[0059] The longitudinal metric is obtained by integrating the vehicle's longitudinal acceleration value and the duration of the driving period, where the longitudinal acceleration value is the acceleration value in the driving direction.

[0060] The lateral acceleration value is obtained by integrating the vehicle's lateral acceleration value and the duration of the driving period, where the lateral acceleration value is the lateral acceleration value perpendicular to the driving direction.

[0061] In this embodiment, the acceleration value is divided into two components: longitudinal acceleration and lateral acceleration. The longitudinal acceleration value is the acceleration value in the vehicle's direction of movement, and the lateral acceleration value is the acceleration value perpendicular to the vehicle's direction of movement. For example, when the vehicle is traveling straight along the road, the driving recorder will only record the longitudinal acceleration value; when the vehicle is turning or changing lanes, the lateral acceleration value will be recorded in the driving recorder. The calculation methods for the longitudinal and lateral acceleration values ​​are the same as those for the acceleration values ​​described above. The only difference is that the input to the calculation method is either the longitudinal or lateral acceleration value, rather than the overall acceleration value.

[0062] Longitudinal and lateral acceleration metrics are calculated separately and can be used to generate driving suggestions. In one embodiment, longitudinal and lateral acceleration metrics are calculated daily, and their distributions can be summarized. Figure 4 shows an example distribution of the longitudinal acceleration metric according to an exemplary embodiment, and Figure 5 shows an example distribution of the lateral acceleration metric according to an exemplary embodiment. As can be seen from Figures 4 and 5, the distributions of the longitudinal and lateral acceleration metrics clearly show the driving style of most drivers. Based on the distributions, aggressive driving vehicles can be identified.

[0063] In one embodiment of this application, step 340, generating a driving suggestion based on the metric threshold and the driving record corresponding to any one of the at least two vehicles, includes the following steps:

[0064] Calculate the longitudinal average of the longitudinal metric and the lateral average of the lateral metric during the driving period corresponding to any one of at least two vehicles;

[0065] Acceleration recommendations are determined based on longitudinal averages and longitudinal metric thresholds;

[0066] Turning recommendations are determined based on the lateral average and lateral metric thresholds;

[0067] Driving suggestions are generated based on acceleration and cornering recommendations.

[0068] In this embodiment, the metric threshold includes two values: a longitudinal metric threshold and a lateral metric threshold, used for comparison with the longitudinal average and the lateral average, respectively. The longitudinal average and lateral average are calculated for each driving period corresponding to at least two vehicles. It is understood that each vehicle typically has multiple driving records, and the longitudinal average and lateral average are calculated for all driving periods across all driving records acquired for that vehicle to provide a more overall driving recommendation, which helps improve driver behavior.

[0069] Based on the longitudinal average and longitudinal metric threshold, the server can determine whether the driver is accelerating or decelerating too quickly. If the longitudinal average is greater than the longitudinal metric threshold, indicating that the driver is accelerating or decelerating too quickly, the server can select an appropriate acceleration suggestion, such as pressing the accelerator pedal more gently. Correspondingly, based on the lateral average and lateral metric threshold, the server can determine whether the driver is turning or changing lanes too quickly and can select a turning suggestion, such as turning more steadily.

[0070] After generating acceleration and turning suggestions, the server can combine these suggestions to generate driving recommendations.

[0071] In one embodiment of this application, step 330, the step of determining a measurement threshold based on multiple acceleration measurement values, includes the following steps:

[0072] Calculate the average of the metrics for each vehicle based on at least one acceleration metric corresponding to each vehicle, to obtain at least two average metrics;

[0073] Arrange at least two metric averages in descending order to obtain a sequence of metric values;

[0074] The target sequence position is determined based on the number of vehicles and a preset ratio;

[0075] The average value of the measurement at the target sequence position in the measurement value sequence is determined as the measurement value threshold.

[0076] In this embodiment, the server calculates an average metric for each vehicle based on at least one acceleration metric. It is understood that the number of average metrics will be the same as the number of vehicles; since there are at least two vehicles, there should be at least two average metrics. Subsequently, the at least two average metrics are sorted in descending order to obtain a sequence of metrics. Afterward, the server determines the target sequence position based on the number of vehicles and a preset ratio. The preset ratio is typically set to 95%, meaning that the top 5% of vehicles in the metric sequence will be considered aggressive driving, and driving suggestions will be generated for their drivers. Within the metric sequence, the average metric at the target sequence position is determined as the metric threshold. For example, if there are 100 metrics in the metric sequence, the 5th value in the sequence will become the metric threshold.

[0077] It is understandable that in embodiments where acceleration values ​​are divided into longitudinal and lateral acceleration values, the same method described above is used to generate metric thresholds for the longitudinal and lateral acceleration values, respectively. The only difference is that the input data will be either longitudinal or lateral acceleration values.

[0078] The following describes device embodiments of this application, which can be configured to implement the method for generating driving suggestions in the above embodiments of this application. FIG6 is a block diagram illustrating a device for generating driving suggestions according to an exemplary embodiment. Referring to FIG6, the device for generating driving suggestions provided in the embodiments of this application includes:

[0079] The record acquisition module 510 is configured to acquire N driving records, wherein the N driving records are from at least two vehicles, and each driving record includes a mapping relationship between driving period and acceleration value, wherein N is an integer greater than 1;

[0080] The measurement determination module 520 is configured to determine multiple acceleration measurement values ​​based on the acceleration values ​​in the N driving records, wherein each vehicle corresponds to at least one acceleration measurement value, and the acceleration measurement value is positively correlated with the acceleration value.

[0081] The threshold determination module 530 is configured to determine a measurement threshold based on the plurality of acceleration measurement values.

[0082] The suggestion generation module 540 is configured to generate driving suggestions based on the metric threshold and the driving record corresponding to any one of the at least two vehicles.

[0083] In embodiments of this application, the measurement determination module 520 includes:

[0084] The weight calculation unit is configured to calculate an acceleration weight value based on the acceleration value and a preset basic acceleration value for each driving record.

[0085] The integral calculation execution unit is configured to perform integral calculations based on the acceleration value, the duration of the driving period, and the acceleration weight value to obtain an acceleration metric value.

[0086] In embodiments of this application, the weight calculation unit includes:

[0087] The subtraction subunit is configured to subtract a preset basic acceleration value from the acceleration value to obtain the difference;

[0088] The acceleration weight value calculation subunit is configured to calculate the acceleration weight value based on the difference and a preset slope coefficient if the difference is greater than zero.

[0089] The acceleration weight value setting sub-unit is configured to set the acceleration weight value to zero if the difference is less than or equal to zero.

[0090] In embodiments of this application, the integral calculation execution unit includes:

[0091] The longitudinal metric value acquisition sub-unit is configured to perform integral calculation based on the vehicle's longitudinal acceleration value and the duration of the driving period to obtain the longitudinal metric value, where the longitudinal acceleration value is the acceleration value in the driving direction;

[0092] The lateral metric value acquisition sub-unit is configured to perform integral calculation based on the vehicle's lateral acceleration value and the duration of the driving period to obtain the lateral metric value, where the lateral acceleration value is the lateral acceleration value perpendicular to the driving direction;

[0093] In embodiments of this application, it is suggested that the generation module 540 includes:

[0094] The average calculation unit is configured to calculate the longitudinal average of the longitudinal measurement values ​​and the lateral average of the lateral measurement values ​​during the driving period corresponding to any one of at least two vehicles.

[0095] The acceleration suggestion determination unit is configured to determine acceleration suggestions based on the longitudinal average value and the longitudinal metric threshold.

[0096] The turning suggestion determination unit is configured as an acceleration suggestion determination unit, which determines acceleration suggestions based on the longitudinal average value and the longitudinal metric threshold.

[0097] The suggestion generation unit is configured to generate driving suggestions based on acceleration and cornering suggestions.

[0098] In embodiments of this application, the threshold determination module 530 includes:

[0099] The metric average calculation unit is configured to calculate the metric average of each vehicle based on at least one acceleration metric value corresponding to each vehicle, so as to obtain at least two metric averages;

[0100] A sequence arrangement unit is configured to arrange at least two metric averages in descending order to obtain a sequence of metric values;

[0101] The location determination unit is configured to determine the location of the target sequence based on the number of vehicles and a preset ratio;

[0102] The metric threshold determination unit is configured to determine the average metric value at the target sequence position in the metric value sequence as the metric threshold.

[0103] It is understood that these modules or units can be implemented by hardware, software, or a combination of both. When implemented in hardware, these modules or units can be one or more hardware modules, such as one or more integrated circuits specific to this application. When implemented in software, these modules or units can be one or more software programs executing on one or more processors.

[0104] Referring to FIG7, a schematic diagram of the structure of an electronic device 800 in one embodiment of this application is described below. It should be noted that the computer system 800 of the electronic device shown in FIG7 is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0105] As shown in Figure 7, the electronic device 800 includes a Central Processing Unit (CPU), which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 802 or programs loaded from storage section 808 into Random Access Memory (RAM) 803. The RAM 803 also stores various programs and data required for system operation. The CPU 801, ROM 802, and RAM 803 are interconnected via bus 804. An input / output interface (I / O interface) 805 is also connected to bus 804.

[0106] The following components are connected to the input / output interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a local area network (LAN) card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0107] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 809, and / or installed from removable medium 811. When the computer program is executed by central processing unit (CPU) 801, it performs various functions defined in the system of this application.

[0108] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0109] In an exemplary embodiment of this application, a computer-readable storage medium is also provided, on which computer-readable instructions are stored. When the processor of a computer executes the computer-readable instructions, the computer performs the method described in the above method embodiments.

[0110] According to one embodiment of this application, a program product for implementing the methods in the above-described method embodiments is also provided. This program product may employ a portable optical disc read-only memory (CD-ROM) and include program code, and may run on a terminal device such as a personal computer. However, the program product of this application is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, device, or apparatus.

[0111] The program product may be any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. Computer-readable storage media may be, for example,, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples (not an exhaustive list) of readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0112] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.

[0113] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0114] The program code used to perform the operations of this application can be written in any combination of one or more programming languages. Programming languages ​​include object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as "C" or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device, partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0115] It should be noted that although the above detailed description mentions several modules or units of the action execution device, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of the above two or more modules or units can be embodied in one module or unit. Conversely, the features and functions of the above one module or unit can be further divided into multiple modules or units to be embodied.

[0116] Furthermore, although the steps of the methods in this disclosure are described in a specific order in the accompanying drawings, this does not require or imply that these steps must be performed in a specific order, or that all the steps shown must be performed sequentially to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps, etc.

[0117] From the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by a combination of software and necessary hardware. Therefore, the technical solutions of the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or portable hard drive), or on a network, including several instructions to enable a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the embodiments according to this application.

[0118] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. The application is intended to cover any variations, uses, or adaptations of this application. Such variations, uses, or adaptations follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The descriptions and embodiments are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the appended claims.

Claims

1. A method for generating driving suggestions, comprising: Obtain N driving records, wherein the N driving records are from at least two vehicles, and each driving record includes a mapping relationship between driving period and acceleration value, where N is an integer greater than 1; For each driving record, an acceleration weight value is calculated based on the acceleration value and a preset basic acceleration value; An acceleration metric is obtained by integrating the acceleration value, the duration of the driving period, and the acceleration weight value, wherein each vehicle corresponds to at least one acceleration metric, and the acceleration metric is positively correlated with the acceleration value. In the N driving records, each vehicle has multiple driving periods, and the acceleration metric is calculated based on each driving period. The acceleration weight value is used to penalize acceleration values ​​that exceed the preset basic acceleration value. A metric threshold is determined based on the multiple acceleration metrics, and driving suggestions are generated based on the metric threshold and the driving records corresponding to any one of the at least two vehicles.

2. The method according to claim 1, characterized in that, The step of calculating an acceleration weight value for each driving record based on the acceleration value and a preset basic acceleration value includes: subtracting the preset basic acceleration value from the acceleration value to obtain a difference; if the difference is greater than zero, calculating the acceleration weight value based on the difference and a preset slope coefficient; and if the difference is less than or equal to zero, setting the acceleration weight value to zero.

3. The method according to claim 1, characterized in that, The step of integrating the acceleration value, the duration of the driving period, and the acceleration weight value to obtain an acceleration metric value includes: integrating the longitudinal acceleration value of the vehicle and the duration of the driving period to obtain a longitudinal metric value, wherein the longitudinal acceleration value is the acceleration value in the driving direction; and integrating the lateral acceleration value of the vehicle and the duration of the driving period to obtain a lateral metric value, wherein the lateral acceleration value is the lateral acceleration value perpendicular to the driving direction.

4. The method according to claim 3, characterized in that, Generating driving suggestions based on the metric threshold and driving records corresponding to any one of the at least two vehicles includes: calculating the longitudinal average of the longitudinal metric values ​​and the lateral average of the lateral metric values ​​during the driving period corresponding to any one of the at least two vehicles; determining acceleration suggestions based on the longitudinal average and the longitudinal metric threshold; determining turning suggestions based on the lateral average and the lateral metric threshold; and generating driving suggestions based on the acceleration suggestions and the turning suggestions.

5. The method according to claim 1, characterized in that, Determining a measurement threshold based on the plurality of acceleration measurements includes: calculating the average measurement value for each vehicle based on the at least one acceleration measurement value corresponding to each vehicle to obtain at least two average measurement values; arranging the at least two average measurement values ​​in descending order to obtain a measurement value sequence; determining the target sequence position based on the number of vehicles and a preset ratio; and determining the average measurement value at the target sequence position in the measurement value sequence as the measurement value threshold.

6. A device for generating driving suggestions, characterized in that, include: The record acquisition module is configured to acquire N driving records, wherein the N driving records are from at least two vehicles, and each driving record includes a mapping relationship between driving period and acceleration value, wherein N is an integer greater than 1; The weight calculation unit is configured to calculate an acceleration weight value based on the acceleration value and a preset basic acceleration value for each driving record. An integral calculation execution unit is configured to perform integral calculations based on acceleration values, driving period duration, and acceleration weight values ​​to obtain acceleration metric values. Each vehicle corresponds to at least one acceleration metric value, and the acceleration metric value is positively correlated with the acceleration value. In the N driving records, each vehicle has multiple driving periods, and the acceleration metric value is calculated based on each driving period. The acceleration weight value is used to penalize acceleration values ​​exceeding the preset basic acceleration value. A threshold determination module is configured to determine a metric threshold based on the multiple acceleration metric values. A suggestion generation module is configured to generate driving suggestions based on the metric threshold and the driving records corresponding to any one of the at least two vehicles.

7. A device for generating driving suggestions, characterized in that, include: processor; A memory for storing executable instructions of the processor; wherein the processor is configured to perform the method for generating driving suggestions according to any one of claims 1 to 5 by executing the executable instructions.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method for generating driving suggestions as described in any one of claims 1 to 5.

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