Method, device, storage medium and equipment for generating driving assistance information

Abstract

The application discloses a driving assistance information generation method and device, a storage medium and equipment. Through the technical scheme provided by the embodiment of the application, a target vehicle can upload vehicle information, driving information and environment information to a server in a driving process. The server uses a simulation model to predict based on the vehicle information, driving information and environment information, and outputs a plurality of prediction indexes of the target vehicle. Based on the plurality of prediction indexes, the target vehicle can generate assistance driving information, and based on the assistance driving information, the target vehicle can be better controlled to provide better driving performance.

Description

Technical Field

[0001] This application belongs to the field of vehicle technology, and in particular relates to a method, apparatus, storage medium and device for generating driving assistance information. Background Technology

[0002] With the development of vehicle technology, vehicles are becoming increasingly feature-rich. For example, they offer powerful entertainment functions, allowing passengers to have a better experience while riding. However, driver assistance systems are an even more important function for improving vehicle driving performance. Summary of the Invention

[0003] This application discloses a method, apparatus, storage medium, and device for generating driving assistance information, which can provide driving assistance information to drivers, thereby assisting drivers in driving vehicles.

[0004] On one hand, embodiments of this application provide a method for generating driving assistance information, the method comprising:

[0005] The vehicle information of the target vehicle, the driving information of the target vehicle, and the environmental information of the environment in which the target vehicle is located are obtained. The vehicle information includes component information of multiple components of the target vehicle, and the driving information is used to indicate the driving status of the target vehicle.

[0006] The vehicle information, driving information, and environmental information of the target vehicle are input into the simulation model. The simulation model makes predictions based on the vehicle information, driving information, and environmental information of the target vehicle, and outputs multiple prediction indicators for the target vehicle. These prediction indicators are used to predict the vehicle state of the target vehicle. The simulation model is trained based on information uploaded by multiple sample vehicles, and the multiple sample vehicles are the same model as the target vehicle.

[0007] Based on multiple predicted indicators of the target vehicle, driving assistance information for the target vehicle is generated, and the driving assistance information is used to assist driving the target vehicle.

[0008] In one possible implementation, the acquisition of vehicle information of the target vehicle, driving information of the target vehicle, and environmental information of the environment in which the target vehicle is located, wherein the vehicle information includes component information of multiple components of the target vehicle, including any one of the following:

[0009] The vehicle information, driving information, and environmental information uploaded by the target vehicle are obtained. The vehicle information is collected by multiple vehicle sensors of the target vehicle, the driving information is collected by multiple driving sensors of the target vehicle, and the environmental information is collected by multiple environmental sensors of the target vehicle.

[0010] Obtain the vehicle data packet uploaded by the target vehicle; classify the data in the vehicle data packet to obtain the vehicle information, driving information and environmental information uploaded by the target vehicle.

[0011] In one possible implementation, the vehicle information, driving information, and environmental information of the target vehicle's environment are input into a simulation model. The simulation model then makes predictions based on these information, outputting multiple predicted indicators for the target vehicle, including:

[0012] The vehicle information, driving information, and environmental information of the target vehicle are input into the simulation model. The physical object simulation model of the simulation model performs simulation calculations based on the vehicle information, driving information, and environmental information, and outputs multiple index prediction parameters of the target vehicle.

[0013] The multiple indicator prediction parameters are input into the software strategy model of the simulation model, and the software strategy model is used to fit the multiple indicator prediction parameters to output multiple prediction indicators of the target vehicle.

[0014] In one possible implementation, when the multiple predictive indicators are used to represent the total energy consumption of the target vehicle, the simulation model of the physical object of the simulation model performs simulation calculations based on the vehicle information, the driving information, and the environmental information, and outputs multiple indicator prediction parameters of the target vehicle, including:

[0015] The physical object simulation model of the simulation model queries the vehicle identifier in the vehicle information to obtain the vehicle condition correction parameters of the target vehicle;

[0016] The physical object simulation model of the simulation model queries the component information of multiple components in the vehicle information to obtain the state parameters of the multiple components;

[0017] The physical object simulation model of the simulation model is queried based on the route information in the driving information to obtain the route influence parameters of the target vehicle;

[0018] The physical object simulation model of the simulation model is identified based on the vehicle speed information in the driving information and the environmental information to obtain the electric drive condition point of the target vehicle;

[0019] Among them, the vehicle condition correction parameters, the status parameters of the multiple components, the route influence parameters, and the electric drive operating point belong to the multiple prediction index parameters.

[0020] In one possible implementation, the step of inputting the multiple indicator prediction parameters into the software strategy model of the simulation model, fitting the multiple indicator prediction parameters through the software strategy model, and outputting multiple predicted indicators of the target vehicle includes:

[0021] The vehicle condition correction parameters, the state parameters of the multiple components, the route influence parameters, and the electric drive operating point are input into the software strategy model. The software strategy model predicts the power consumption of the target vehicle based on the vehicle condition correction parameters, the route influence parameters, and the electric drive operating point. The software strategy model also predicts the component energy consumption of the target vehicle based on the state parameters of the multiple components. Finally, the software strategy model fuses the power consumption and the component energy consumption to obtain the multiple prediction indicators.

[0022] In one possible implementation, generating the driving assistance information for the target vehicle based on multiple predictive metrics of the target vehicle includes any of the following:

[0023] When the assisted driving information is instantaneous energy consumption, the instantaneous energy consumption of the target vehicle is determined based on multiple first-type prediction indicators among the multiple prediction indicators. The first-type prediction indicators refer to prediction indicators related to energy consumption.

[0024] When the assisted driving information is instantaneous power, the instantaneous power of the target vehicle is determined based on multiple second-type prediction indicators among the multiple prediction indicators. The second-type prediction indicators refer to prediction indicators related to power.

[0025] When the assisted driving information is the number of engine start-stop cycles, the number of engine start-stop cycles of the target vehicle is determined based on multiple third-type prediction indicators among the multiple prediction indicators. The third-type prediction indicators refer to prediction indicators related to the number of engine start-stop cycles.

[0026] In one possible implementation, the training method for the simulation model includes:

[0027] Obtain vehicle information, driving information, and environmental information of the multiple sample vehicles;

[0028] The vehicle information, driving information, and environmental information of the multiple sample vehicles are input into the simulation model. The simulation model makes predictions based on the vehicle information, driving information, and environmental information of the multiple sample vehicles and outputs multiple prediction indicators for each sample vehicle.

[0029] The simulation model is trained based on the differences between multiple predicted and actual indicators of each of the sample vehicles.

[0030] In one possible implementation, before inputting the vehicle information of the target vehicle, the driving information of the target vehicle, and the environmental information of the environment in which the target vehicle is located into the simulation model, the method further includes:

[0031] The vehicle information, driving information, and environmental information of the target vehicle are preprocessed.

[0032] On one hand, embodiments of this application provide a device for generating driving assistance information, the method comprising:

[0033] The information acquisition module is used to acquire vehicle information of the target vehicle, driving information of the target vehicle, and environmental information of the environment in which the target vehicle is located. The vehicle information includes component information of multiple components of the target vehicle, and the driving information is used to indicate the driving status of the target vehicle.

[0034] The input module is used to input the vehicle information, driving information, and environmental information of the target vehicle into the simulation model. The simulation model makes predictions based on the vehicle information, driving information, and environmental information of the target vehicle, and outputs multiple prediction indicators for the target vehicle. These prediction indicators are used to predict the vehicle state of the target vehicle. The simulation model is trained based on information uploaded by multiple sample vehicles, and the multiple sample vehicles are the same model as the target vehicle.

[0035] A driving assistance information generation module is used to generate driving assistance information for the target vehicle based on multiple predictive indicators of the target vehicle, and the driving assistance information is used to assist driving the target vehicle.

[0036] In one possible implementation, the information acquisition module is configured to perform any of the following:

[0037] The vehicle information, driving information, and environmental information uploaded by the target vehicle are obtained. The vehicle information is collected by multiple vehicle sensors of the target vehicle, the driving information is collected by multiple driving sensors of the target vehicle, and the environmental information is collected by multiple environmental sensors of the target vehicle.

[0038] Obtain the vehicle data packet uploaded by the target vehicle; classify the data in the vehicle data packet to obtain the vehicle information, driving information and environmental information uploaded by the target vehicle.

[0039] In one possible implementation, the input module is used to input the vehicle information of the target vehicle, the driving information of the target vehicle, and the environmental information of the environment in which the target vehicle is located into the simulation model. The physical object simulation model of the simulation model performs simulation calculations based on the vehicle information, the driving information, and the environmental information, and outputs multiple indicator prediction parameters of the target vehicle. The multiple indicator prediction parameters are then input into the software strategy model of the simulation model, and the software strategy model fits the multiple indicator prediction parameters to output multiple predicted indicators of the target vehicle.

[0040] In one possible implementation, the input module is configured to: query the physical object simulation model of the simulation model based on the vehicle identifier in the vehicle information to obtain the vehicle condition correction parameters of the target vehicle; query the physical object simulation model of the simulation model based on the component information of multiple components in the vehicle information to obtain the state parameters of the multiple components; query the physical object simulation model of the simulation model based on the route information in the driving information to obtain the route influence parameters of the target vehicle; and identify the electric drive operating point of the target vehicle based on the vehicle speed information in the driving information and the environmental information; wherein the vehicle condition correction parameters, the state parameters of the multiple components, the route influence parameters, and the electric drive operating point belong to the multiple prediction index parameters.

[0041] In one possible implementation, the input module is used to input the vehicle condition correction parameters, the state parameters of the plurality of components, the route influence parameters, and the electric drive operating point into the software strategy model. The software strategy model then predicts and outputs the power consumption of the target vehicle based on the vehicle condition correction parameters, the route influence parameters, and the electric drive operating point. The software strategy model also predicts the component energy consumption of the target vehicle's plurality of components based on the state parameters of the plurality of components. Finally, the software strategy model fuses the power consumption and the component energy consumption to obtain the plurality of predicted indicators.

[0042] In one possible implementation, the driving assistance information generation module is configured to perform any of the following:

[0043] When the assisted driving information is instantaneous energy consumption, the instantaneous energy consumption of the target vehicle is determined based on multiple first-type prediction indicators among the multiple prediction indicators. The first-type prediction indicators refer to prediction indicators related to energy consumption.

[0044] When the assisted driving information is instantaneous power, the instantaneous power of the target vehicle is determined based on multiple second-type prediction indicators among the multiple prediction indicators. The second-type prediction indicators refer to prediction indicators related to power.

[0045] When the assisted driving information is the number of engine start-stop cycles, the number of engine start-stop cycles of the target vehicle is determined based on multiple third-type prediction indicators among the multiple prediction indicators. The third-type prediction indicators refer to prediction indicators related to the number of engine start-stop cycles.

[0046] In one possible implementation, the device further includes:

[0047] The training module is used to acquire vehicle information, driving information, and environmental information of the multiple sample vehicles; input the vehicle information, driving information, and environmental information of the multiple sample vehicles into the simulation model, and make predictions based on the vehicle information, driving information, and environmental information of the multiple sample vehicles, outputting multiple predicted indicators for each sample vehicle; and train the simulation model based on the difference information between the multiple predicted indicators and the actual indicators of each sample vehicle.

[0048] In one possible implementation, the device further includes:

[0049] The preprocessing module is used to preprocess the vehicle information, driving information, and environmental information of the target vehicle.

[0050] On one hand, an electronic device is provided, the electronic device comprising:

[0051] At least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the aforementioned method for generating driving assistance information.

[0052] On the one hand, a non-transitory computer-readable storage medium stores computer instructions for causing the computer to perform the aforementioned method for generating driving assistance information.

[0053] On the one hand, embodiments of this application also provide a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the aforementioned method for generating driving assistance information.

[0054] The technical solution provided in this application allows the target vehicle to upload vehicle information, driving information, and environmental information to a server during operation. The server uses a simulation model to make predictions based on this vehicle information, driving information, and environmental information, outputting multiple predicted indicators for the target vehicle. Based on these multiple predicted indicators, assisted driving information for the target vehicle can be generated. This assisted driving information enables better control of the target vehicle, providing it with better driving performance. Attached Figure Description

[0055] To more clearly illustrate the technical solution of this application and to facilitate a further understanding of the technical effects, technical features and objectives of this application, the application will be described in detail below with reference to the accompanying drawings. The drawings constitute an essential part of the specification and are used together with the first embodiment of this application to illustrate the technical solution of this application, but do not constitute a limitation on this application.

[0056] Figure 1 A schematic diagram of an implementation environment provided for an embodiment of this application;

[0057] Figure 2 A flowchart illustrating a method for generating driving assistance information provided in an embodiment of this application;

[0058] Figure 3 A flowchart illustrating another method for generating driving assistance information provided in this application embodiment;

[0059] Figure 4 A flowchart illustrating yet another method for generating driving assistance information provided in this application embodiment;

[0060] Figure 5 A flowchart illustrating a method for training a simulation model, as provided in this application embodiment;

[0061] Figure 6 This is a schematic diagram of the structure of a driver assistance information generation device provided in an embodiment of this application;

[0062] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0063] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0064] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement an apparatus and / or a method of practice.

[0065] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this application. The drawings only show the components related to this application and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the shape, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0066] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the aspects described can be practiced without these specific details.

[0067] The concept of the Internet of Vehicles (IoV) originates from the Internet of Things (IoT), which uses vehicles in motion as information sensing objects and leverages next-generation information and communication technologies to achieve network connections between vehicles and X (i.e., vehicles, people, roads, and service platforms). This enhances the overall intelligent driving level of vehicles, provides users with a safe, comfortable, intelligent, and efficient driving experience and transportation services, and improves traffic operation efficiency and the level of intelligence in social transportation services.

[0068] The Internet of Vehicles (IoV) utilizes next-generation information and communication technologies to achieve comprehensive network connectivity between vehicles and cloud platforms, between vehicles themselves, between vehicles and roads, between vehicles and people, and within vehicles. It primarily achieves "triple play," integrating the in-vehicle network, the inter-vehicle network, and the in-vehicle mobile internet. The IoV uses sensing technology to perceive vehicle status information and leverages wireless communication networks and modern intelligent information processing technologies to achieve intelligent traffic management, intelligent decision-making for traffic information services, and intelligent vehicle control. Specifically:

[0069] 1. Communication between the vehicle and the cloud platform refers to the transmission of information between the vehicle and the vehicle network service platform through wireless communication technologies such as satellite wireless communication or mobile cellular communication, receiving control commands issued by the platform, and sharing vehicle data in real time.

[0070] 2. Communication between in-vehicle devices refers to the transmission of information between various devices inside the vehicle, used for real-time monitoring and operation control of the devices, and to establish a digital in-vehicle control system.

[0071] GT-POWER: A module in GT-SUITE software used for simulating engine operation. Developed by Gamma Technologies, Inc. (USA). It simulates engine scavenging and combustion processes, predicts engine power, fuel economy, and emissions characteristics, thereby optimizing engine design, such as intake and exhaust manifold optimization, valve timing optimization, turbocharger selection, and EGR optimization.

[0072] Simulink is a visual simulation tool within MATLAB, developed by MathWorks. Simulink is a modular graphical environment for multi-domain simulation and model-based design. It supports system design, simulation, automatic code generation, and continuous testing and verification of embedded systems. Simulink provides a graphical editor, a customizable library of modules, and solvers, enabling dynamic system modeling and simulation.

[0073] Figure 1 This is a schematic diagram illustrating the implementation environment of the driving assistance information generation method provided in this application embodiment. See also... Figure 1 The implementation environment includes an in-vehicle terminal 110 and a server 140.

[0074] The vehicle terminal 110 is connected to the server 140 via a wireless network. A target application runs on the vehicle terminal 110, which controls the server 140. This target application is a control application for a hybrid vehicle. In some embodiments, the vehicle terminal 110 and the server 140 are connected via wired or wireless means. The vehicle terminal 110 provides support for vehicle operation.

[0075] Server 140 is used to generate driving assistance information based on the information uploaded by vehicle terminal 110, and send the driving assistance information to vehicle terminal 110 for use by vehicle terminal 110.

[0076] After introducing the implementation environment of the embodiments of this application, the application scenarios of the embodiments of this application will be described below.

[0077] The method for generating driving assistance information provided in this application embodiment can be applied to various vehicles with in-vehicle terminals. After adopting the technical solution provided in this application embodiment, the vehicle's driving assistance parameters can be determined through simulation models. Based on these driving assistance parameters, the vehicle can be provided with better driving performance. Alternatively, based on these driving assistance parameters, it can be determined whether the vehicle's driving performance has been improved after the software update.

[0078] After introducing the implementation environment and application scenarios of the embodiments of this application, the method for generating driving assistance information provided by the embodiments of this application will be described below. See [link to documentation]. Figure 2 Taking the server as the executing entity as an example, the methods include:

[0079] 201. The server obtains the vehicle information of the target vehicle, the driving information of the target vehicle, and the environmental information of the environment in which the target vehicle is located. The vehicle information includes component information of multiple parts of the target vehicle, and the driving information is used to indicate the driving status of the target vehicle.

[0080] The target vehicle is the vehicle whose driver assistance information is to be determined. Vehicle information includes component information for multiple parts of the target vehicle. These components are functional parts of the target vehicle, providing different functions. For example, these components may include the target vehicle's electric motor / engine, and the component information includes relevant information about the electric motor / engine, such as torque and output power. Alternatively, these components may also include the target vehicle's air conditioning module, and correspondingly, the component information includes relevant information about the air conditioning module, such as its power and operating time. The target vehicle's driving information includes its driving route and speed. The target vehicle's environmental information includes the temperature and humidity of its surroundings.

[0081] 202. The server inputs the vehicle information, driving information, and environmental information of the target vehicle into the simulation model. The simulation model makes predictions based on the vehicle information, driving information, and environmental information of the target vehicle, and outputs multiple prediction indicators for the target vehicle. These prediction indicators are used to predict the vehicle state of the target vehicle. The simulation model is trained based on information uploaded by multiple sample vehicles, which are of the same model as the target vehicle.

[0082] The simulation model is developed based on simulation technology to simulate the vehicle model corresponding to the target vehicle. Using this model, data on the vehicle model under different operating conditions can be generated without actual vehicle testing, resulting in high efficiency. The multiple prediction metrics are indicators obtained by the simulation model based on the input data. These metrics represent the vehicle's state, including instantaneous power, instantaneous speed, instantaneous torque, total energy consumption, and remaining range. The multiple sample vehicles are used to train the simulation model.

[0083] 203. The server generates driving assistance information for the target vehicle based on multiple predictive indicators of the target vehicle. This driving assistance information is used to assist in driving the target vehicle.

[0084] Among them, the driver assistance information is used to assist in driving the target vehicle. For example, the driver assistance information is used to control the energy consumption of the target vehicle, or to limit the maximum output power of the target vehicle.

[0085] The technical solution provided in this application allows the target vehicle to upload vehicle information, driving information, and environmental information to a server during operation. The server uses a simulation model to make predictions based on this vehicle information, driving information, and environmental information, and outputs multiple predicted indicators for the target vehicle. Based on these multiple predicted indicators, assisted driving information for the target vehicle can be generated. Based on this assisted driving information, the target vehicle can be better controlled, providing better driving performance.

[0086] Steps 201-203 above are a brief description of the method for generating driving assistance information provided in the embodiments of this application. The following will provide a detailed description of the method for generating driving assistance information provided in the embodiments of this application, using some examples. See [link to relevant documentation]. Figure 3 The methods include:

[0087] 301. The server obtains the vehicle information of the target vehicle, the driving information of the target vehicle, and the environmental information of the environment in which the target vehicle is located. The vehicle information includes component information of multiple components of the target vehicle, and the driving information is used to indicate the driving status of the target vehicle.

[0088] The target vehicle is the vehicle whose driver assistance information is to be determined. Vehicle information includes component information for multiple parts of the target vehicle. These components are functional parts of the target vehicle, providing different functions. For example, these components may include the target vehicle's electric motor / engine, and the component information includes relevant information about the electric motor / engine, such as torque and output power. Alternatively, these components may also include the target vehicle's air conditioning module, and correspondingly, the component information includes relevant information about the air conditioning module, such as its power and operating time. The target vehicle's driving information includes its driving route and speed. The target vehicle's environmental information includes the temperature and humidity of its surroundings.

[0089] In one possible implementation, the server obtains the vehicle information, driving information, and environmental information uploaded by the target vehicle. The vehicle information is collected by multiple vehicle sensors of the target vehicle, the driving information is collected by multiple driving sensors of the target vehicle, and the environmental information is collected by multiple environmental sensors of the target vehicle.

[0090] In this implementation, the server can directly obtain vehicle information, driving information, and environmental information from the vehicle terminal, resulting in high information acquisition efficiency.

[0091] For example, the vehicle terminal uploads the vehicle information, driving information, and environmental information of the target vehicle to the server based on the target protocol, and the server obtains the vehicle information, driving information, and environmental information of the target vehicle. In some embodiments, the target protocol is MQTT (Message Queuing Telemetry Transport). Of course, other protocols can also be used for transmission, and this application embodiment does not limit this.

[0092] In one possible implementation, the server obtains the vehicle data packet uploaded by the target vehicle. The server classifies the data in the vehicle data packet to obtain the vehicle information, driving information, and environmental information uploaded by the target vehicle.

[0093] The vehicle data packet is a data packet collected by the vehicle terminal during the driving process of the target vehicle. The purpose of classifying the vehicle data packet by the server is to determine the corresponding parts of the vehicle information, driving information and environmental information in the vehicle data packet.

[0094] In this implementation, the vehicle information, driving information, and environmental information of the target vehicle are obtained by the server after classifying the data packets, which eliminates the need for the vehicle terminal to classify the data packets and reduces the computational load on the vehicle terminal.

[0095] For example, the server obtains vehicle data packets uploaded by the vehicle's onboard terminal via the target protocol. The server parses the vehicle data packets to obtain multiple pieces of vehicle data for the target vehicle. The server categorizes these multiple pieces of vehicle data to obtain the vehicle information, driving information, and environmental information for the target vehicle.

[0096] It should be noted that the server can obtain the vehicle information, driving information and environmental information of the target vehicle through any of the above methods, and this application embodiment does not limit this.

[0097] 302. The server preprocesses the vehicle information, driving information, and environmental information of the target vehicle.

[0098] In one possible implementation, the server filters the vehicle information, driving information, and environmental information of the target vehicle based on the target rules to obtain the filtered vehicle information, driving information, and environmental information, and then processes the filtered vehicle information, driving information, and environmental information.

[0099] The target rules are set by technical personnel according to the actual situation, and this application embodiment does not limit this. The purpose of filtering vehicle information, driving information, and environmental information is to eliminate errors in vehicle information, driving information, and environmental information, and to avoid errors affecting the server's subsequent judgment.

[0100] 303. The server inputs the vehicle information, driving information, and environmental information of the target vehicle into the simulation model. The simulation model makes predictions based on the vehicle information, driving information, and environmental information of the target vehicle, and outputs multiple prediction indicators for the target vehicle. These prediction indicators are used to predict the vehicle state of the target vehicle. The simulation model is trained based on information uploaded by multiple sample vehicles, which are of the same model as the target vehicle.

[0101] The simulation model is developed based on simulation technology and is used to simulate the vehicle model corresponding to the target vehicle. Using this simulation model, data on the vehicle model under different operating conditions can be generated without conducting real-vehicle testing, resulting in high efficiency. The multiple prediction indicators are indicators predicted by the simulation model based on the input data. These multiple prediction indicators represent the vehicle state of the target vehicle, such as instantaneous power, instantaneous speed, instantaneous torque, total energy consumption, and remaining range. The multiple sample vehicles are the vehicles used to train the simulation model. In some embodiments, the simulation model is built using commercial simulation tools such as GT-Power and Simulink, or it can be written using programming languages ​​such as C / C++ and Python; this application does not limit this approach.

[0102] In one possible implementation, the server inputs the vehicle information, driving information, and environmental information of the target vehicle into a simulation model. The physical object simulation model of this model performs simulation calculations based on the vehicle information, driving information, and environmental information, outputting multiple predicted parameters for the target vehicle. The server then inputs these multiple predicted parameters into the software strategy model of the simulation model, which fits the multiple predicted parameters to output multiple predicted indicators for the target vehicle.

[0103] The physical object simulation model is used to simulate the target vehicle, while the software strategy model is used to plan strategies based on the parameters output by the physical object simulation model, thereby adjusting the physical object simulation model.

[0104] For example, the server queries the vehicle identifier in the vehicle information based on the physical object simulation model of the simulation model to obtain the vehicle condition correction parameters for the target vehicle. The server queries the component information of multiple components in the vehicle information based on the physical object simulation model of the simulation model to obtain the state parameters of those components. The server queries the route information in the driving information based on the physical object simulation model of the simulation model to obtain the route influence parameters for the target vehicle. The server identifies the electric drive operating point of the target vehicle based on the vehicle speed information and environmental information in the driving information based on the physical object simulation model of the simulation model of the simulation model. Here, the vehicle condition correction parameters, the state parameters of the multiple components, the route influence parameters, and the electric drive operating point are among the multiple predictive index parameters, with the vehicle condition correction parameters representing the health status of the target vehicle. The server inputs the vehicle condition correction parameters, the state parameters of the multiple components, the route influence parameters, and the electric drive operating point into the software strategy model, which then predicts and outputs the power consumption of the target vehicle based on these parameters. The server uses the software strategy model to predict the component energy consumption of multiple components of the target vehicle based on the state parameters of these components. The server then uses the software strategy model to fuse the power energy consumption and component energy consumption to obtain the multiple predicted indicators.

[0105] For example, taking the prediction of energy consumption for battery electric vehicles (BEVs) as an example, BEV energy consumption prediction is directly related to the development of services such as vehicle range prediction and charging planning. Its accuracy is affected by factors such as driving behavior, traffic conditions, accessory operation, and vehicle health. See also Figure 4The server, using the physical object simulation model of the simulation model, queries the vehicle database based on the vehicle identification number (VIN) in the vehicle information to obtain the vehicle condition correction parameters for the target vehicle. The server, using the physical object simulation model, queries the route information in the driving information to obtain the route impact parameters for the target vehicle. The server, using the physical object simulation model of the simulation model, identifies the electric drive operating point for the target vehicle based on the vehicle speed information and environmental information in the driving information. The server, using the physical object simulation model of the simulation model, queries the component information of multiple components in the vehicle information to obtain the state parameters of those components. The server inputs the vehicle condition correction parameters, the state parameters of the multiple components, the route impact parameters, and the electric drive operating point into the software strategy model. The power consumption prediction sub-model of the software strategy model predicts the power consumption of the target vehicle based on the vehicle condition correction parameters, the route impact parameters, and the electric drive operating point, and outputs the power consumption of the target vehicle. The server, using the component energy consumption prediction sub-model of the software strategy model, predicts the component energy consumption of multiple components of the target vehicle based on the state parameters of the multiple components. The server uses the software strategy model to integrate the power consumption and component energy consumption to obtain multiple predictive indicators.

[0106] To provide a clearer explanation of the above implementation methods, the training method for the simulation model will be introduced below.

[0107] In one possible implementation, the server acquires vehicle information, driving information, and environmental information of the multiple sample vehicles. The server inputs this information into the simulation model, which then makes predictions based on the data, outputting multiple predicted metrics for each sample vehicle. The server trains the simulation model based on the differences between the predicted and actual metrics for each sample vehicle.

[0108] For any one of these multiple sample vehicles, see Figure 5The onboard terminal of the sample vehicle uploads its vehicle information, driving information, and environmental information to the server. This information is collectively referred to as simulation input data. Simultaneously, the onboard terminal also uploads multiple actual indicators to the server. The server inputs these information into the simulation model and uses its physical object simulation model to simulate the vehicle's information, driving information, and environmental information, outputting multiple indicator prediction parameters for the sample vehicle. The server then inputs these prediction parameters into the simulation model's software strategy model, analyzes them, and outputs multiple predicted indicators for the sample vehicle. The server compares these predicted indicators with the actual indicators and uses the comparison results to train the simulation model. For example, the server compares the deviations between the simulation model's predicted indicators and the actual indicators generated during real-world vehicle operation, analyzing and optimizing the sources and contributing factors of errors for indicators with larger deviations, further improving the simulation model's accuracy.

[0109] Taking pure electric vehicle (BEV) energy consumption prediction as an example, it is assumed that before operation, the simulation model continuously collects information such as vehicle speed, vehicle condition (including tire pressure, load, battery SOH, etc.), driving time, and driving GPS of multiple vehicles, records the power consumption of high-power components such as battery, motor, compressor, PTC, and DC-DC, records weather information such as ambient temperature and humidity, and provides relevant backend services to extract the driving trajectory and speed change time series data of each vehicle.

[0110] For the selected sample vehicles, the vehicle condition correction parameters can be directly obtained from the vehicle condition data. For the sample vehicles that have completed driving, their driving route data and real vehicle speed time series signals have been determined. For the set electric drive control strategy, the electric drive operating point and the related power energy consumption can be accurately predicted. By combining information such as vehicle operating route, weather, and power operating conditions, the historical real data of accessory energy consumption under similar operating conditions can be identified, thereby calculating the total energy consumption of the sample vehicles.

[0111] If a certain driving route does not yet exist in the historical data, after the journey is completed, parameters can be adjusted based on the actual power consumption value, vehicle speed time series data, and vehicle condition to infer the magnitude of the route's impact parameters. The relevant results are then added to the route impact parameter database for use by subsequent vehicles. Similarly, the energy consumption of attachments can be used to update the statistical model for attachment operations and support subsequent optimization of attachment energy consumption predictions.

[0112] Furthermore, the simulation model can be encapsulated as an independent service and deployed in the cloud or on the electronic control terminal. After the real vehicle test is completed, it can be combined with input data such as boundary conditions and actual vehicle operating conditions to evaluate the vehicle's performance analysis data under different strategies; or during the real vehicle operation, it can be combined with the current state of the vehicle to simulate the changes in performance index data under different hypothetical scenarios, supporting the implementation or performance optimization of other system planning algorithms.

[0113] 304. The server generates driving assistance information for the target vehicle based on multiple predictive indicators of the target vehicle. This driving assistance information is used to assist in driving the target vehicle.

[0114] Among them, the driver assistance information is used to assist in driving the target vehicle. For example, the driver assistance information is used to control the energy consumption of the target vehicle, or to limit the maximum output power of the target vehicle.

[0115] In one possible implementation, when the assisted driving information is instantaneous energy consumption, the server determines the instantaneous energy consumption of the target vehicle based on a plurality of first-type predictive indicators among the plurality of predictive indicators, wherein the first-type predictive indicators refer to predictive indicators related to energy consumption.

[0116] In one possible implementation, when the assisted driving information is instantaneous power, the server determines the instantaneous power of the target vehicle based on a plurality of second-type predictive indicators among the plurality of predictive indicators, wherein the second-type predictive indicators refer to predictive indicators related to power.

[0117] In one possible implementation, when the assisted driving information is the number of engine start-stop cycles, the server determines the number of engine start-stop cycles of the target vehicle based on multiple third-category predictive indicators among the multiple predictive indicators. The third-category predictive indicators refer to predictive indicators related to the number of engine start-stop cycles.

[0118] In some embodiments, after generating driving assistance information, the server can send the driving service information to the vehicle terminal of the target vehicle so that the vehicle terminal can control the target vehicle or display the driving assistance information to the driver based on the driving assistance information.

[0119] It should be noted that the above description is based on the example of the simulation model being deployed on a server. In other possible implementations, the simulation model can also be deployed on an in-vehicle terminal, and this application embodiment does not limit this.

[0120] The technical solution provided in this application allows the target vehicle to upload vehicle information, driving information, and environmental information to a server during operation. The server uses a simulation model to make predictions based on this vehicle information, driving information, and environmental information, and outputs multiple predicted indicators for the target vehicle. Based on these multiple predicted indicators, assisted driving information for the target vehicle can be generated. Based on this assisted driving information, the target vehicle can be better controlled, providing better driving performance.

[0121] In summary, the implementation of the above solutions first allows for the identification of relevant parameters in the simulation model based on historical data, further optimizing simulation accuracy. Taking BEV vehicle energy consumption prediction as an example, for vehicle operation scenarios where relevant simulation parameters have been verified and optimized, the simulation model can achieve highly accurate predictions. Based on this, the impact of relevant software strategies on vehicle energy consumption in each scenario can be evaluated. Furthermore, by combining data on vehicle speed, ambient weather, and accessory operations, the prediction model can use simulation methods to predict the vehicle energy consumption in each scenario, providing support for services such as range prediction and charging planning.

[0122] For a corresponding method embodiment, see [link to relevant documentation]. Figure 6 This application also provides a driving assistance information generation device 600, including: an information acquisition module 601, an input module 602, and a driving assistance information generation module 603.

[0123] The information acquisition module 601 is used to acquire vehicle information of the target vehicle, driving information of the target vehicle, and environmental information of the environment in which the target vehicle is located. The vehicle information includes component information of multiple components of the target vehicle, and the driving information is used to indicate the driving status of the target vehicle.

[0124] The input module 602 is used to input the vehicle information, driving information, and environmental information of the target vehicle into the simulation model. The simulation model makes predictions based on the vehicle information, driving information, and environmental information of the target vehicle and outputs multiple prediction indicators of the target vehicle. These prediction indicators are used to predict the vehicle state of the target vehicle. The simulation model is trained based on information uploaded by multiple sample vehicles, which are of the same model as the target vehicle.

[0125] The driving assistance information generation module 603 is used to generate driving assistance information for the target vehicle based on multiple predictive indicators of the target vehicle, and the driving assistance information is used to assist driving the target vehicle.

[0126] In one possible implementation, the information acquisition module 601 is configured to perform any of the following:

[0127] The system acquires vehicle information, driving information, and environmental information uploaded by the target vehicle. The vehicle information is collected by multiple vehicle sensors of the target vehicle, the driving information is collected by multiple driving sensors of the target vehicle, and the environmental information is collected by multiple environmental sensors of the target vehicle.

[0128] Retrieve the vehicle data packet uploaded by the target vehicle. Classify the data in the vehicle data packet to obtain the vehicle information, driving information, and environmental information uploaded by the target vehicle.

[0129] In one possible implementation, the input module 602 is used to input the vehicle information, driving information, and environmental information of the target vehicle's environment into the simulation model. The physical object simulation model of the simulation model performs simulation calculations based on the vehicle information, driving information, and environmental information, outputting multiple predicted parameters for the target vehicle. These multiple predicted parameters are then input into the software strategy model of the simulation model. The software strategy model fits the multiple predicted parameters to output multiple predicted indicators for the target vehicle.

[0130] In one possible implementation, the input module 602 is used to query the vehicle identification information in the vehicle information based on the physical object simulation model of the simulation model to obtain the vehicle condition correction parameters of the target vehicle. It also queries the component information of multiple components in the vehicle information based on the physical object simulation model of the simulation model to obtain the state parameters of those components. Furthermore, it queries the route information in the driving information based on the physical object simulation model of the simulation model to obtain the route influence parameters of the target vehicle. Finally, it identifies the electric drive operating point of the target vehicle based on the vehicle speed information and environmental information in the driving information. The vehicle condition correction parameters, the state parameters of the multiple components, the route influence parameters, and the electric drive operating point are all part of the multiple predictive index parameters.

[0131] In one possible implementation, the input module 602 is used to input the vehicle condition correction parameters, the state parameters of the multiple components, the route influence parameters, and the electric drive operating point into the software strategy model. The software strategy model then predicts and outputs the power consumption of the target vehicle based on the vehicle condition correction parameters, the route influence parameters, and the electric drive operating point. The software strategy model also predicts the component energy consumption of the target vehicle's multiple components based on the state parameters of the multiple components. Finally, the software strategy model fuses the power consumption and component energy consumption to obtain the multiple predicted indicators.

[0132] In one possible implementation, the driving assistance information generation module 603 is configured to perform any of the following:

[0133] When the assisted driving information is instantaneous energy consumption, the instantaneous energy consumption of the target vehicle is determined based on multiple first-type predictive indicators among the multiple predictive indicators. The first-type predictive indicators refer to predictive indicators related to energy consumption.

[0134] When the assisted driving information is instantaneous power, the instantaneous power of the target vehicle is determined based on multiple second-type predictive indicators among the multiple predictive indicators. The second-type predictive indicators refer to predictive indicators related to power.

[0135] When the assisted driving information is the number of engine start-stop cycles, the number of engine start-stop cycles for the target vehicle is determined based on multiple third-category predictive indicators among the multiple predictive indicators. The third-category predictive indicators refer to predictive indicators related to the number of engine start-stop cycles.

[0136] In one possible implementation, the device further includes:

[0137] The training module is used to acquire vehicle information, driving information, and environmental information of the multiple sample vehicles. This information is then input into the simulation model, which makes predictions based on this data, outputting multiple predicted metrics for each sample vehicle. The simulation model is trained based on the differences between the predicted and actual metrics for each sample vehicle.

[0138] In one possible implementation, the device further includes:

[0139] The preprocessing module is used to preprocess the vehicle information, driving information, and environmental information of the target vehicle.

[0140] The technical solution provided in this application allows the target vehicle to upload vehicle information, driving information, and environmental information to a server during operation. The server uses a simulation model to make predictions based on this vehicle information, driving information, and environmental information, and outputs multiple predicted indicators for the target vehicle. Based on these multiple predicted indicators, assisted driving information for the target vehicle can be generated. Based on this assisted driving information, the target vehicle can be better controlled, providing better driving performance.

[0141] See Figure 7 This application also provides an electronic device 700, which includes:

[0142] At least one processor; and,

[0143] A memory communicatively connected to the at least one processor; wherein the electronic device is the vehicle terminal in the above embodiments.

[0144] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method for generating driving assistance information in the foregoing method embodiments.

[0145] This application also provides a non-transitory computer-readable storage medium that stores computer instructions for causing the computer to execute the driving assistance information generation method in the foregoing method embodiments.

[0146] This application also provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to perform the driving assistance information generation method in the aforementioned method embodiments.

[0147] The following is for reference. Figure 7 The diagram illustrates a structural schematic of an electronic device 700 suitable for implementing embodiments of this application. The electronic device 700 in these embodiments may include, but is not limited to, mobile electronic devices such as laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), etc., and fixed electronic devices such as digital TVs, desktop computers, etc. Figure 7 The electronic device 700 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0148] like Figure 7 As shown, the electronic device 700 may include a processing unit (e.g., a central processing unit, a graphics processor, etc.) 701, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 702 or a program loaded from a storage device 708 into a random access memory (RAM) 703. The RAM 703 also stores various programs and data required for the operation of the electronic device 700. The processing unit 701, ROM 702, and RAM 703 are interconnected via a bus 704. An input / output (I / O) interface 705 is also connected to the bus 704.

[0149] Typically, the following devices can be connected to I / O interface 705: input devices 706 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 709. Communication device 709 allows electronic device 700 to communicate wirelessly or wiredly with other devices to exchange data. Although an electronic device 700 with various devices is shown in the figure, it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed alternatively.

[0150] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure 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 device 709, or installed from storage device 708, or installed from ROM 702. When the computer program is executed by processing device 701, it performs the functions defined in the methods of the embodiments of this application.

[0151] It should be noted that the computer-readable medium described in this disclosure can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0152] The aforementioned computer-readable medium may be included in the aforementioned electronic device; or it may exist independently and not assembled into the electronic device.

[0153] The aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquire at least two Internet Protocol (IP) addresses; send a node evaluation request including the at least two IP addresses to a node evaluation device, wherein the node evaluation device selects an IP address from the at least two IP addresses and returns it; and receive the IP address returned by the node evaluation device; wherein the acquired IP address indicates an edge node in a content delivery network.

[0154] Alternatively, the aforementioned computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receive a node evaluation request including at least two Internet Protocol (IP) addresses; select an IP address from the at least two IP addresses; and return the selected IP address; wherein the received IP address indicates an edge node in the content delivery network.

[0155] Computer program code for performing the operations of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0156] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0157] The units described in the embodiments of this application can be implemented in software or in hardware. The name of a unit does not necessarily limit the unit itself; for example, the first acquisition unit can also be described as "a unit that acquires at least two Internet Protocol addresses".

Claims

1. A method for generating driving assistance information, characterized in that, include: The vehicle information of the target vehicle, the driving information of the target vehicle, and the environmental information of the environment in which the target vehicle is located are obtained. The vehicle information includes component information of multiple components of the target vehicle, and the driving information is used to indicate the driving status of the target vehicle. The vehicle information, driving information, and environmental information of the target vehicle are input into the simulation model. The simulation model makes predictions based on the vehicle information, driving information, and environmental information of the target vehicle, and outputs multiple prediction indicators for the target vehicle. These prediction indicators are used to predict the vehicle state of the target vehicle. The simulation model is trained based on information uploaded by multiple sample vehicles, and the multiple sample vehicles are the same model as the target vehicle. Based on multiple predicted indicators of the target vehicle, driving assistance information for the target vehicle is generated, and the driving assistance information is used to assist driving the target vehicle. The process involves inputting the vehicle information, driving information, and environmental information of the target vehicle's environment into the simulation model. The simulation model then makes predictions based on these information, outputting multiple predicted indicators for the target vehicle, including: The vehicle information, driving information, and environmental information of the target vehicle are input into the simulation model. The physical object simulation model of the simulation model performs simulation calculations based on the vehicle information, driving information, and environmental information, and outputs multiple index prediction parameters of the target vehicle. The multiple indicator prediction parameters are input into the software strategy model of the simulation model, and the software strategy model is used to fit the multiple indicator prediction parameters to output multiple prediction indicators of the target vehicle.

2. The method for generating driving assistance information as described in claim 1, characterized in that, The acquisition of vehicle information of the target vehicle, driving information of the target vehicle, and environmental information of the environment in which the target vehicle is located, wherein the vehicle information includes component information of multiple components of the target vehicle, including any one of the following: The vehicle information, driving information, and environmental information uploaded by the target vehicle are obtained. The vehicle information is collected by multiple vehicle sensors of the target vehicle, the driving information is collected by multiple driving sensors of the target vehicle, and the environmental information is collected by multiple environmental sensors of the target vehicle. Obtain the vehicle data packet uploaded by the target vehicle; classify the data in the vehicle data packet to obtain the vehicle information, driving information and environmental information uploaded by the target vehicle.

3. The method for generating driving assistance information as described in claim 1, characterized in that, When the multiple predicted indicators are used to represent the total energy consumption of the target vehicle, the simulation model of the physical object of the simulation model performs simulation calculations based on the vehicle information, the driving information, and the environmental information, and outputs multiple predicted parameters of the target vehicle, including: The physical object simulation model of the simulation model queries the vehicle identifier in the vehicle information to obtain the vehicle condition correction parameters of the target vehicle; The physical object simulation model of the simulation model queries the component information of multiple components in the vehicle information to obtain the state parameters of the multiple components; The physical object simulation model of the simulation model is queried based on the route information in the driving information to obtain the route influence parameters of the target vehicle; The physical object simulation model of the simulation model is identified based on the vehicle speed information in the driving information and the environmental information to obtain the electric drive condition point of the target vehicle; Among them, the vehicle condition correction parameters, the status parameters of the multiple components, the route influence parameters, and the electric drive operating point belong to the multiple prediction index parameters.

4. The method for generating driving assistance information as described in claim 3, characterized in that, The software strategy model that inputs the multiple indicator prediction parameters into the simulation model, and fits the multiple indicator prediction parameters through the software strategy model to output multiple predicted indicators of the target vehicle includes: The vehicle condition correction parameters, the state parameters of the multiple components, the route influence parameters, and the electric drive operating point are input into the software strategy model. The software strategy model predicts the power consumption of the target vehicle based on the vehicle condition correction parameters, the route influence parameters, and the electric drive operating point. The software strategy model also predicts the component energy consumption of the target vehicle based on the state parameters of the multiple components. Finally, the software strategy model fuses the power consumption and the component energy consumption to obtain the multiple prediction indicators.

5. The method for generating driving assistance information as described in claim 1, characterized in that, The generation of driving assistance information for the target vehicle based on multiple predicted indicators includes any one of the following: When the driving assistance information is instantaneous energy consumption, the instantaneous energy consumption of the target vehicle is determined based on multiple first-type prediction indicators among the multiple prediction indicators. The first-type prediction indicators refer to prediction indicators related to energy consumption. When the driving assistance information is instantaneous power, the instantaneous power of the target vehicle is determined based on multiple second-type prediction indicators among the multiple prediction indicators. The second-type prediction indicators refer to prediction indicators related to power. When the driving assistance information is the number of engine start-stop cycles, the number of engine start-stop cycles of the target vehicle is determined based on multiple third-type prediction indicators among the multiple prediction indicators. The third-type prediction indicators refer to prediction indicators related to the number of engine start-stop cycles.

6. The method for generating driving assistance information as described in claim 1, characterized in that, The training method for the simulation model includes: Obtain vehicle information, driving information, and environmental information of the multiple sample vehicles; The vehicle information, driving information, and environmental information of the multiple sample vehicles are input into the simulation model. The simulation model makes predictions based on the vehicle information, driving information, and environmental information of the multiple sample vehicles and outputs multiple prediction indicators for each sample vehicle. The simulation model is trained based on the differences between multiple predicted and actual indicators of each of the sample vehicles.

7. The method for generating driving assistance information as described in claim 1, characterized in that, Before inputting the vehicle information, driving information, and environmental information of the target vehicle into the simulation model, the method further includes: The vehicle information, driving information, and environmental information of the target vehicle are preprocessed.

8. A device for generating driver assistance information, comprising: The information acquisition module is used to acquire vehicle information of the target vehicle, driving information of the target vehicle, and environmental information of the environment in which the target vehicle is located. The vehicle information includes component information of multiple components of the target vehicle, and the driving information is used to indicate the driving status of the target vehicle. The input module is used to input the vehicle information, driving information, and environmental information of the target vehicle into the simulation model. The simulation model makes predictions based on the vehicle information, driving information, and environmental information of the target vehicle, and outputs multiple prediction indicators for the target vehicle. These prediction indicators are used to predict the vehicle state of the target vehicle. The simulation model is trained based on information uploaded by multiple sample vehicles, and the multiple sample vehicles are the same model as the target vehicle. The step of inputting the vehicle information, driving information, and environmental information of the target vehicle into the simulation model, and having the simulation model predict and output multiple predicted indicators of the target vehicle based on the vehicle information, driving information, and environmental information of the target vehicle, includes: inputting the vehicle information, driving information, and environmental information of the target vehicle into the simulation model; having the physical object simulation model of the simulation model perform simulation calculations based on the vehicle information, driving information, and environmental information; and outputting multiple indicator prediction parameters of the target vehicle; and inputting the multiple indicator prediction parameters into the software strategy model of the simulation model; and fitting the multiple indicator prediction parameters through the software strategy model to output multiple predicted indicators of the target vehicle. A driving assistance information generation module is used to generate driving assistance information for the target vehicle based on multiple predictive indicators of the target vehicle, and the driving assistance information is used to assist driving the target vehicle.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method for generating driving assistance information according to any one of claims 1-7.

10. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method for generating driving assistance information according to any one of claims 1-7.

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