Method, device, storage medium and processor for determining monitoring strategy of vehicle

Abstract

The application discloses a kind of determination method, device, storage medium and processor of the monitoring strategy of vehicle. Among them, the method comprises: obtaining the driving environment data of vehicle and the driving habit data of the driving object of vehicle, wherein the driving habit data is used to indicate the behavior habit of the driving object driving vehicle;Driving environment data and driving habit data are input into target driving prediction model, and the target driving safety information of vehicle is predicted, wherein the target driving prediction model is obtained by training the historical driving environment data of vehicle and the historical driving habit data of the driving object of vehicle, and the target driving safety information is used to indicate the safety information in the process of vehicle driving;Based on target driving safety information, determine the target monitoring strategy of vehicle, wherein the target monitoring strategy is used to monitor the rule of functional safety of vehicle.The application solves the technical problem that the cost of monitoring vehicle functional safety is too high.

Description

Technical Field

[0001] This invention relates to the field of vehicle functional safety technology, and more specifically, to a method, apparatus, storage medium, and processor for determining a vehicle monitoring strategy. Background Technology

[0002] Currently, most functional safety development is aimed at meeting the needs of scientific and technological development. However, scientific and technological development often requires a lot of money, which contradicts the current goal of reducing costs in vehicle engineering development. Functional safety is a top priority for the vehicle industry now and in the future, and vehicle safety has always been a focus of attention.

[0003] In related technologies, many functional safety mechanisms are still very expensive and often fail to meet functional safety requirements. At the same time, many controllers at present perform functional safety monitoring and triggering of safety mechanisms throughout the entire process, requiring constant data recording and calculation. Therefore, the controllers are easily overloaded and have a high failure rate, which leads to the need to improve the performance of various aspects of the chip, which also increases costs. Thus, there is a technical problem of excessively high costs in monitoring vehicle functional safety.

[0004] There is currently no effective solution to the aforementioned technical problem of excessively high costs associated with monitoring vehicle functional safety. Summary of the Invention

[0005] This invention provides a method, apparatus, storage medium, and processor for determining a vehicle monitoring strategy, to at least address the technical problem of excessively high costs associated with monitoring vehicle functional safety.

[0006] According to one aspect of the present invention, a method for determining a vehicle monitoring strategy is provided. The method may include: acquiring vehicle driving environment data and driving habit data of the vehicle's driver, wherein the driving habit data represents the driver's driving behavior habits; inputting the driving environment data and driving habit data into a target driving prediction model to predict target driving safety information of the vehicle, wherein the target driving prediction model is trained using historical driving environment data of the vehicle and historical driving habit data of the vehicle's driver, and the target driving safety information indicates the vehicle's safety information during driving; and determining a target monitoring strategy for the vehicle based on the target driving safety information, wherein the target monitoring strategy is a rule for monitoring the functional safety of the vehicle.

[0007] Optionally, based on the target driving safety information, a target monitoring strategy for the vehicle is determined, including: matching the target driving safety information with driving safety information stored in the cloud to obtain a matching result, wherein the matching result is used to indicate the degree of matching between the target driving safety information and the driving safety information stored in the cloud; and determining the target monitoring strategy for the vehicle based on the matching result.

[0008] Optionally, based on the matching result, a target monitoring strategy for the vehicle is determined, including: in response to the matching result being greater than or equal to a matching threshold, determining a monitoring strategy corresponding to the driving safety information stored in the cloud; and determining the monitoring strategy as the target monitoring strategy.

[0009] Optionally, the method for determining the vehicle monitoring strategy further includes: storing the target driving safety information in the cloud in response to the matching result being less than the matching threshold.

[0010] Optionally, the method for determining the vehicle monitoring strategy further includes: acquiring historical driving environment data of the vehicle and historical driving habit data of the driver; using the historical driving environment data and historical driving habit data to train the initial driving prediction model to obtain the target driving prediction model.

[0011] Optionally, the target monitoring strategy includes at least: the operating mechanism of the sensors, the operating mechanism of the actuators and the operating mechanism of the controller, as well as the functional safety state that the vehicle needs to achieve.

[0012] According to another aspect of the present invention, a device for determining a vehicle monitoring strategy is also provided. The device may include: an acquisition unit for acquiring vehicle driving environment data and driving habit data of the vehicle's driver, wherein the driving habit data represents the driver's driving behavior habits; a prediction unit for inputting the driving environment data and driving habit data into a target driving prediction model to predict target driving safety information of the vehicle, wherein the target driving prediction model is trained using historical driving environment data of the vehicle and historical driving habit data of the vehicle's driver, and the target driving safety information indicates the vehicle's safety information during driving; and a determination unit for determining a target monitoring strategy for the vehicle based on the target driving safety information, wherein the target monitoring strategy is a rule for monitoring the functional safety of the vehicle.

[0013] According to another aspect of the present invention, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored program, wherein, when the program is run by a processor, it controls the device where the storage medium is located to execute the method for determining the vehicle monitoring strategy according to the embodiments of the present invention.

[0014] According to another aspect of the present invention, a processor is also provided. The processor is configured to run a program, wherein the program, when running, executes the method for determining a vehicle monitoring strategy according to the embodiments of the present invention.

[0015] According to another aspect of the present invention, a vehicle is also provided. This vehicle is used to execute the method for determining a vehicle monitoring strategy according to the embodiments of the present invention.

[0016] In this embodiment of the invention, vehicle driving environment data and driving habit data of the driver are acquired, wherein the driving habit data represents the driver's driving behavior. The driving environment data and driving habit data are input into a target driving prediction model to predict the vehicle's target driving safety information. The target driving prediction model is trained using the vehicle's historical driving environment data and the driver's historical driving habit data. The target driving safety information indicates the vehicle's safety information during driving. Based on the target driving safety information, a target monitoring strategy for the vehicle is determined, wherein the target monitoring strategy is a rule for monitoring the vehicle's functional safety. In other words, in this embodiment of the invention, by acquiring the vehicle's driving environment and the driver's driving habits, the target driving prediction model is used to predict the vehicle's target driving safety information, thereby determining the vehicle's target monitoring strategy based on the target driving safety information. Since this invention can determine the vehicle's monitoring strategy based on driving habit learning, it focuses more on the chip's performance in complex scenarios, and the overall performance indicators do not need to be very high. This achieves the goal of determining the cost of vehicle monitoring strategy, thus solving the technical problem of excessively high cost of monitoring vehicle functional safety and realizing the technical effect of reducing the cost of monitoring vehicle functional safety. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0018] Figure 1 This is a flowchart of a method for determining a vehicle monitoring strategy according to an embodiment of the present invention;

[0019] Figure 2 This is a flowchart of a method for determining a functional safety monitoring mechanism according to an embodiment of the present invention;

[0020] Figure 3 This is a schematic diagram of a vehicle monitoring strategy determination device according to an embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, functional component, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, functional components, or devices.

[0023] According to an embodiment of the present invention, an embodiment of a method for determining a vehicle monitoring strategy is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0024] Figure 1 This is a flowchart of a method for determining a vehicle monitoring strategy according to an embodiment of the present invention, such as... Figure 1 As shown, the method may include the following steps:

[0025] Step S101: Obtain vehicle driving environment data and driving habit data of the vehicle's driver.

[0026] In the technical solution provided by step S101 of the present invention, driving habit data is used to represent the driving behavior habits of the driving object.

[0027] In this embodiment, vehicle driving environment data and driving habit data of the vehicle's driver are acquired. For example, vehicle driving environment data and driving habit data of the vehicle's driver can be acquired through different sensors. This is merely an example and does not limit the specific methods for acquiring vehicle driving environment data and driving habit data of the vehicle's driver.

[0028] Optionally, by acquiring vehicle driving environment data and driver habit data, it is possible to analyze driving behavior and road conditions, promptly identify potential safety hazards, and thus improve the accuracy of determining vehicle target monitoring strategies.

[0029] Step S102: Input the driving environment data and driving habit data into the target driving prediction model to predict the vehicle's target driving safety information.

[0030] In the technical solution provided in step S102 of the present invention, the target driving prediction model is trained using the vehicle's historical driving environment data and the historical driving habit data of the vehicle's driver. The target driving safety information is used to indicate the vehicle's safety information during driving. The target driving prediction model can also be called a driving habit prediction model, and the target driving safety information can also be called a driving habit prediction result.

[0031] In this embodiment, after obtaining the vehicle's driving environment data and the driving habit data of the vehicle's driver in step S101, the driving environment data and driving habit data are input into the target driving prediction model to predict the vehicle's target driving safety information.

[0032] For example, driving habit data is input into a driving habit prediction model. Based on the driving habit identification in the driving habit data, the driving habit prediction model outputs the driving habits and the corresponding driving habit prediction results.

[0033] Step S103: Determine the target monitoring strategy for the vehicle based on the target driving safety information.

[0034] In the technical solution provided in step S103 of the present invention, the target monitoring strategy is used to monitor the rules for the functional safety of the vehicle. The target monitoring strategy can also be referred to as a functional safety mechanism.

[0035] In this embodiment, after predicting the target driving safety information of the vehicle in step S102, the target monitoring strategy of the vehicle is determined based on the target driving safety information.

[0036] For example, the target driving safety information is uploaded to the cloud and used for deep neural network learning to determine the functional safety triggering mechanism of the vehicle at this time.

[0037] Optionally, the target driving safety information is matched with the driving safety information stored in the cloud to obtain a matching result. The matching result is used to indicate the degree of matching between the target driving safety information and the driving safety information stored in the cloud, thereby determining the vehicle's target monitoring strategy based on the matching result.

[0038] For example, when the matching result is greater than or equal to the matching threshold, it means that the target driving safety information and the corresponding monitoring strategy are stored in the cloud. Based on this, the monitoring strategy corresponding to the driving safety information stored in the cloud can be determined, and the monitoring strategy can be determined as the target monitoring strategy.

[0039] For another example, when the matching result is less than the matching threshold, it means that the target driving safety information and the corresponding monitoring strategy are not stored in the cloud. Based on this, the target driving safety information can be stored in the cloud.

[0040] Optionally, the target monitoring strategy includes at least: the operating mechanism of the sensors, the operating mechanism of the actuators and the operating mechanism of the controller, as well as the functional safety state that the vehicle needs to achieve.

[0041] Optionally, by defining a target monitoring strategy, it is not necessary to have the controller monitor and calculate throughout the entire process during future functional safety development. Instead, the controller can increase its computing power to monitor only when the sensors or actuators detect similar driving behaviors of the driver, thereby reducing the cost of monitoring vehicle functional safety.

[0042] It should be noted that the above embodiments can be executed by a vehicle monitoring strategy determination device.

[0043] In steps S101 to S103 of this invention, the vehicle's driving environment data and the driver's driving habit data are acquired, wherein the driving habit data represents the driver's behavioral habits. The driving environment data and driving habit data are input into a target driving prediction model to predict the vehicle's target driving safety information. The target driving prediction model is trained using the vehicle's historical driving environment data and the driver's historical driving habit data. The target driving safety information indicates the vehicle's safety information during driving. Based on the target driving safety information, a target monitoring strategy for the vehicle is determined, wherein the target monitoring strategy is a rule for monitoring the vehicle's functional safety. In other words, in this embodiment of the invention, by acquiring the vehicle's driving environment and the driver's driving habits, the target driving prediction model predicts the vehicle's target driving safety information, thereby determining the vehicle's target monitoring strategy based on the target driving safety information. Since this invention can determine the vehicle's monitoring strategy based on learning from driving habits, it focuses more on the chip's performance in complex scenarios, and the overall performance indicators do not need to be very high, achieving the goal of determining the cost of vehicle monitoring strategies. This solves the technical problem of excessively high costs in monitoring vehicle functional safety and achieves the technical effect of reducing the cost of monitoring vehicle functional safety.

[0044] The method described in this embodiment will be further described below.

[0045] As an optional implementation method, determining a vehicle target monitoring strategy based on target driving safety information includes: matching the target driving safety information with driving safety information stored in the cloud to obtain a matching result, wherein the matching result is used to indicate the degree of matching between the target driving safety information and the driving safety information stored in the cloud; and determining a vehicle target monitoring strategy based on the matching result.

[0046] In this embodiment, the target driving safety information is matched with the driving safety information stored in the cloud to obtain a matching result; based on the matching result, a target monitoring strategy for the vehicle is determined.

[0047] Optionally, by matching with driving safety information stored in the cloud, the driving status of target vehicles can be detected in a timely manner, thereby improving the accuracy of determining vehicle target monitoring strategies.

[0048] As an optional implementation method, the target monitoring strategy for the vehicle is determined based on the matching result, including: in response to the matching result being greater than or equal to the matching threshold, determining the monitoring strategy corresponding to the driving safety information stored in the cloud; and determining the monitoring strategy as the target monitoring strategy.

[0049] In this embodiment, when the matching result is greater than or equal to the matching threshold, the monitoring strategy corresponding to the driving safety information stored in the cloud is determined; the monitoring strategy is then determined as the target monitoring strategy.

[0050] For example, assuming the matching threshold is 85%, the matching result between the target driving safety information and the driving safety information stored in the cloud is 90%. Since 90% > 85%, that is, the matching result between the target driving safety information and the driving safety information stored in the cloud is greater than the matching threshold, the monitoring strategy corresponding to the driving safety information stored in the cloud can be determined as the target monitoring strategy.

[0051] As an optional implementation method, a target monitoring strategy for the vehicle is determined based on the matching result, including: in response to the matching result being less than a matching threshold, storing the target driving safety information in the cloud.

[0052] In this embodiment, when the matching result is less than the matching threshold, the target driving safety information is stored in the cloud.

[0053] For example, assuming the matching threshold is 85%, the matching result between the target driving safety information and the driving safety information stored in the cloud is 80%. Since 80% < 85%, that is, the matching result between the target driving safety information and the driving safety information stored in the cloud is less than the matching threshold, the target driving safety information can be stored in the cloud.

[0054] As an optional implementation method, the method for determining the vehicle monitoring strategy further includes: acquiring historical driving environment data of the vehicle and historical driving habit data of the driver; and using the historical driving environment data and historical driving habit data to train an initial driving prediction model to obtain a target driving prediction model.

[0055] In this embodiment, historical driving environment data of the vehicle and historical driving habit data of the driver are acquired and used to train the initial driving prediction model to obtain the target driving prediction model.

[0056] Optionally, by using historical driving environment data of the vehicle and historical driving habit data of the driver to train the initial driving model, the target driving prediction model is determined, which ensures the accuracy of the target driving prediction model and thus improves the accuracy of determining the vehicle target monitoring strategy.

[0057] As an optional implementation method, the target monitoring strategy includes at least: the operating mechanism of the sensor, the operating mechanism of the actuator and the operating mechanism of the controller, as well as the functional safety state that the vehicle needs to achieve.

[0058] In this embodiment, the target monitoring strategy includes at least: the operating mechanism of the sensor, the operating mechanism of the actuator and the operating mechanism of the controller, as well as the functional safety state that the vehicle needs to achieve.

[0059] Optionally, the monitoring strategy needs to clearly define the functional safety states that the vehicle needs to achieve, including obeying traffic rules, avoiding collisions, and ensuring passenger safety. By monitoring the operating status of sensors, actuators, and controllers, it can be ensured that the vehicle achieves these functional safety states during operation.

[0060] It should be noted that the above embodiments can be executed by a vehicle monitoring strategy determination device.

[0061] In this embodiment, vehicle driving environment data and the driver's driving habit data are acquired, whereby the driving habit data represents the driver's driving behavior. The driving environment data and driving habit data are input into a target driving prediction model to predict the vehicle's target driving safety information. This target driving prediction model is trained using historical driving environment data and historical driving habit data of the vehicle's driver. The target driving safety information indicates the vehicle's safety information during driving. Based on the target driving safety information, a target monitoring strategy for the vehicle is determined, whereby the target monitoring strategy is a rule for monitoring the vehicle's functional safety. In other words, in this embodiment, by acquiring the vehicle's driving environment and the driver's driving habits, the target driving prediction model predicts the vehicle's target driving safety information, thereby determining the vehicle's target monitoring strategy. Since this invention can determine the vehicle's monitoring strategy based on learning from driving habits, it focuses more on the chip's performance in complex scenarios, without requiring very high overall performance indicators. This achieves the goal of determining the cost of vehicle monitoring strategies, thus solving the technical problem of excessively high costs in monitoring vehicle functional safety and realizing the technical effect of reducing the cost of monitoring vehicle functional safety.

[0062] The technical solutions of the embodiments of the present invention will be illustrated below with reference to preferred embodiments.

[0063] Currently, most functional safety development is aimed at meeting the needs of scientific and technological development. However, scientific and technological development often requires a lot of money, which contradicts the current goal of reducing costs in vehicle engineering development. Functional safety is a top priority for the vehicle industry now and in the future, and vehicle safety has always been a focus of attention.

[0064] In related technologies, many functional safety mechanisms remain costly and often fail to meet functional safety requirements. Furthermore, many current controllers perform continuous functional safety monitoring and triggering of safety mechanisms, requiring constant data recording and calculations. This easily overloads the controllers and leads to a high failure rate, necessitating improvements in various aspects of chip performance, further increasing costs. Therefore, there is a technical problem of excessively high costs associated with monitoring vehicle functional safety. Currently, no effective solution has been proposed to address this issue.

[0065] However, this invention proposes a method for determining a functional safety monitoring mechanism. By acquiring driving habit data and scenario data of the target vehicle user, the driving habit data is input into a driving habit prediction model to obtain the driving habits output by the driving habit prediction model and the corresponding driving habit prediction results. Based on the prediction results, a strategy for monitoring the functional safety of the vehicle is determined. This solves the technical problem of excessively high costs in monitoring vehicle functional safety and achieves the technical effect of reducing the cost of monitoring vehicle functional safety.

[0066] The embodiments of the present invention will be further described below.

[0067] Figure 2 This is a flowchart of a method for determining a functional safety monitoring mechanism according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes the following steps:

[0068] Step S201: Obtain driving habit data of the driver of the target vehicle.

[0069] In this embodiment, driving habit data of the driver of the target vehicle is acquired. For example, driving habit data of the driver of the target vehicle is acquired through sensors.

[0070] Optionally, driving habit data may include the following: driving speed, route preference, driving behavior during driving time, driving style, vehicle maintenance, and driving records.

[0071] Optionally, driving speed may include data such as average speed, maximum speed, number of rapid accelerations and decelerations.

[0072] Optionally, route preferences may include data such as frequently visited locations, frequently taken routes, and frequently stopped stops.

[0073] Optionally, the driving time period may include data such as the driver's preferred driving time period, such as morning, afternoon, or evening.

[0074] Optionally, driving behavior may include data such as whether one frequently uses a mobile phone or other devices, whether one wears a seatbelt, and whether one obeys traffic rules.

[0075] Optionally, driving style may include data such as whether the driving is smooth, whether the driver frequently changes lanes, or whether the driver speeds.

[0076] Optionally, vehicle maintenance may include data such as whether regular maintenance is performed, vehicle condition checks, and parts replacement.

[0077] Optionally, driving records may include data such as past accident records and traffic violation records.

[0078] Optionally, this driving habit data can be obtained through in-vehicle devices, dashcams, insurance companies, and other channels, and used to analyze drivers' driving habits, thereby providing a reference for vehicle management and safety.

[0079] Step S202: Identify the vehicle's safety measures at this time and the actions taken when the vehicle reaches a safe state.

[0080] In this embodiment, after obtaining the driving habit data of the target object in step S201, the vehicle's safety measures and actions when the vehicle reaches a safe state are identified based on the obtained driving habit data of the target object.

[0081] Optionally, the system can identify the safety measures taken by the sensors, controllers, and actuators at this time, as well as the actions taken when the vehicle reaches a safe state.

[0082] Step S203: Input the identified data into the driving behavior habit prediction model to obtain driving behavior habits and driving behavior habit prediction results.

[0083] In this embodiment, after identifying the vehicle's safety measures and actions when the vehicle reaches a safe state in step S202, the identified data is input into the driving behavior habit prediction model to obtain driving behavior habits and driving behavior habit prediction results.

[0084] Optionally, these data are input into the driving behavior habit prediction model, and the data is uploaded to the cloud for driving behavior habit recognition based on driver behavior habit data. The driving behavior habit prediction model outputs the driving behavior habit and the corresponding driving behavior habit prediction result. The driving behavior habit prediction model is incrementally learned and updated based on the user's triggering of the functional safety mechanism in the controller under this situation.

[0085] Step S204: Based on the predicted results of driving behavior habits, the functional safety mechanism is triggered in this situation.

[0086] In this embodiment, after determining the driving behavior habits and the driving behavior habit prediction results in step S203, the functional safety mechanism is triggered based on the driving behavior habit prediction results.

[0087] Optionally, the driver can predict driving behavior habits and trigger a functional safety mechanism in that situation, outputting the status of the sensors, controllers, and actuators at that time and alerting the driver.

[0088] Optionally, deep neural network learning can be conducted to conclude that a safety mechanism is triggered when similar driver behavior is encountered.

[0089] Optionally, the monitored data can be used to make predictions and judgments, and then uploaded to the cloud for deep neural network learning to determine the functional safety trigger mechanism at that time.

[0090] For example, a driver's driving habits are relatively fixed on their journey from home to work, barring unforeseen circumstances. However, in special situations, such as someone suddenly crossing the road, this situation is specifically recorded and used for deep neural network learning to predict what functional safety mechanisms the vehicle would trigger if a hazard were to occur. The actions and judgments of sensors, actuators, and controllers are recorded. This way, when driving on the same route or in similar road conditions in the future, the controller doesn't need to monitor the entire journey. Instead, it only increases its computing power when sensors detect irregular pedestrian movement or an intersection, preparing to trigger functional safety mechanisms at any time.

[0091] Alternatively, deep neural network learning refers to the process of training a deep neural network to learn representations and features from data. A deep neural network is a multi-layered neural network model, typically containing multiple hidden layers and non-linear activation functions, capable of learning complex non-linear relationships.

[0092] Optionally, the learning process of a deep neural network typically includes steps such as data preparation, network construction, parameter initialization, forward propagation, loss calculation, backpropagation, parameter optimization, and model evaluation.

[0093] Optionally, data preparation may include collecting and preparing training data, including input features and corresponding labels.

[0094] Optionally, network construction may include designing the structure of the deep neural network, including the number of neurons in the input layer, hidden layers, and output layer, activation functions, etc.

[0095] Optionally, the initialization parameters may include randomly initializing the weights and bias parameters of the neural network.

[0096] Optionally, forward propagation may include feeding training data into a neural network and calculating the output.

[0097] Optionally, calculating the loss may include using a loss function to evaluate the gap between the neural network output and the true label.

[0098] Optionally, backpropagation may include calculating the gradient of the loss function with respect to the neural network parameters using a backpropagation algorithm, and updating the parameters to reduce the loss.

[0099] Optionally, parameter optimization may include iteratively training the neural network using an optimization algorithm (such as stochastic gradient descent) until the loss function converges or a specified stopping condition is met.

[0100] Optionally, model evaluation may include evaluating the performance of the trained neural network model using a validation set or a test set.

[0101] Alternatively, deep neural network learning can be applied to various fields, such as computer vision, natural language processing, and speech recognition, and has achieved many important results and applications.

[0102] In this embodiment, the functional safety monitoring mechanism determination method provided by the present invention is mainly to save the chip cost of the domain controller. A functional safety chip that can be equipped with ASIL D (Automotive Safety Integrity Level D) requires multiple indicators, such as the Single Point Failure Metric (SPFM), the Latent Failure Metric (LFM), and the Probabilistic Mission Hardware Failure (PMHF). These indicators directly affect the chip's performance indicators, and these indicators need to undergo long-term testing, especially at the vehicle level, where multi-scenario coverage is required. This invention, through driving habit learning, focuses more on the chip's indicators under complex scenarios, and the overall indicators do not need to be so high. Therefore, it can achieve cost reduction to a certain extent. These indicators can be derived from data such as improved yield, quality, accuracy, and efficiency, savings in energy consumption, raw materials, and processes, and ease of processing, operation, control, and use.

[0103] Optionally, single point-of-failure metrics are indicators used to measure the probability or impact of a single point of failure that may occur in a system. Common single point-of-failure metrics include availability, mean time between failures (MTBF), mean time to repair (MTTR), failure probability, and fault tolerance.

[0104] Alternatively, availability refers to system availability metrics used to assess whether the system can continue to operate normally after encountering a single point of failure. Availability is usually expressed as a percentage; for example, 99.9% availability means that the system will only experience downtime of no more than 8.76 hours per year.

[0105] Optionally, Mean Time Between Failures (MTBF) refers to the average time a system operates between failures. The longer the MTBF, the higher the stability and reliability of the system.

[0106] Optionally, Mean Time To Repair (MTTR) refers to the average time it takes for the system to recover after a failure. The shorter the MTTR, the faster the system recovers.

[0107] Alternatively, failure probability refers to the probability that a system will fail within a certain period of time. By monitoring failure probability, potential problems in the system can be identified in a timely manner and measures can be taken to resolve them.

[0108] Optionally, fault tolerance refers to whether a system can continue to operate normally when it encounters a single point of failure. A system with good fault tolerance can automatically switch to a backup channel or backup device when a failure occurs, ensuring the stability and availability of the system.

[0109] Optionally, potential failure indicators refer to early warning signs that indicate possible malfunctions during equipment or system operation. These indicators help monitor the equipment's operating status, promptly identify problems, and take corrective measures to prevent production interruptions or losses due to equipment failure. Common potential failure indicators include abnormal changes in parameters such as equipment temperature, vibration frequency, noise level, current, and voltage, as well as abnormal behaviors in equipment operation. By monitoring these indicators and establishing corresponding early warning systems, potential failures can be detected in advance, enabling preventative maintenance and ensuring the normal operation of the equipment.

[0110] Optionally, the hardware random failure probability index refers to the probability that hardware will fail during operation. This index is usually expressed as a decimal, for example, 0.01 indicates that the probability of hardware failure is 1%. The hardware random failure probability index is one of the important indicators for evaluating hardware reliability. It can help users understand the stability and reliability level of the hardware, thereby taking appropriate measures to prevent and handle hardware failures.

[0111] Optionally, the present invention mainly uses deep neural networks and other learning methods to monitor and learn the information and processing solutions provided by the vehicle or other sensors when they encounter risks. This allows for advance preparation in similar situations, enabling the system to enter a state where functional safety mechanisms can be triggered at any time. This avoids overload and ensures that the original functional safety function is fulfilled at critical moments, preventing damage to the entire vehicle caused by electronic and electrical failures.

[0112] In this embodiment, by acquiring the driving habit data and scenario data of the target vehicle user, the driving habit data is input into the driving habit prediction model to obtain the driving habits and corresponding driving habit prediction results output by the driving habit prediction model. Based on the prediction results, a strategy for monitoring the functional safety of the vehicle is determined. This solves the technical problem of excessively high costs in monitoring the functional safety of vehicles and achieves the technical effect of reducing the cost of monitoring the functional safety of vehicles.

[0113] According to embodiments of the present invention, an apparatus for determining a vehicle monitoring strategy is also provided. It should be noted that this apparatus for determining a vehicle monitoring strategy can be used to execute the method for determining a vehicle monitoring strategy in the method embodiments.

[0114] Figure 3 This is a schematic diagram of a vehicle monitoring strategy determination device according to an embodiment of the present invention. Figure 3 As shown, the vehicle monitoring strategy determination device 300 may include: an acquisition unit 301, a prediction unit 302, and a determination unit 303.

[0115] The acquisition unit 301 is used to acquire vehicle driving environment data and driving habit data of the vehicle's driver, wherein the driving habit data is used to represent the driving behavior habits of the driver.

[0116] The prediction unit 302 is used to input driving environment data and driving habit data into the target driving prediction model to predict the target driving safety information of the vehicle. The target driving prediction model is trained using the vehicle's historical driving environment data and the historical driving habit data of the vehicle's driver. The target driving safety information is used to indicate the vehicle's safety information during driving.

[0117] The determining unit 303 is used to determine the target monitoring strategy of the vehicle based on the target driving safety information, wherein the target monitoring strategy is used to monitor the functional safety rules of the vehicle.

[0118] Optionally, the determining unit 303 may include: a storage module, used to match the target driving safety information with the driving safety information stored in the cloud to obtain a matching result, wherein the matching result is used to indicate the degree of matching between the target driving safety information and the driving safety information stored in the cloud; and a first determining module, used to determine the target monitoring strategy for the vehicle based on the matching result.

[0119] Optionally, the first determining module may include: a first determining submodule, used to determine the monitoring strategy corresponding to the driving safety information stored in the cloud in response to the matching result being greater than or equal to the matching threshold; and a second determining submodule, used to determine the monitoring strategy as the target monitoring strategy.

[0120] Optionally, the vehicle monitoring strategy determination device 300 may further include: a storage unit for storing target driving safety information to the cloud in response to a matching result being less than a matching threshold.

[0121] Optionally, the vehicle monitoring strategy determination device 300 may further include: a first acquisition unit for acquiring historical driving environment data of the vehicle and historical driving habit data of the driver; and a training unit for training an initial driving prediction model using the historical driving environment data and historical driving habit data to obtain a target driving prediction model.

[0122] In this embodiment, vehicle driving environment data and the driver's driving habit data are acquired, whereby the driving habit data represents the driver's driving behavior. The driving environment data and driving habit data are input into a target driving prediction model to predict the vehicle's target driving safety information. This target driving prediction model is trained using historical driving environment data and historical driving habit data of the vehicle's driver. The target driving safety information indicates the vehicle's safety information during driving. Based on the target driving safety information, a target monitoring strategy for the vehicle is determined, whereby the target monitoring strategy is a rule for monitoring the vehicle's functional safety. In other words, in this embodiment, by acquiring the vehicle's driving environment and the driver's driving habits, the target driving prediction model predicts the vehicle's target driving safety information, thereby determining the vehicle's target monitoring strategy. Since this invention can determine the vehicle's monitoring strategy based on learning from driving habits, it focuses more on the chip's performance in complex scenarios, without requiring very high overall performance indicators. This achieves the goal of determining the cost of vehicle monitoring strategies, thus solving the technical problem of excessively high costs in monitoring vehicle functional safety and realizing the technical effect of reducing the cost of monitoring vehicle functional safety.

[0123] According to an embodiment of the present invention, a computer-readable storage medium is also provided, the storage medium including a stored program, wherein the program executes a method for determining a vehicle monitoring strategy in a method embodiment.

[0124] According to an embodiment of the present invention, a processor is also provided for running a program, wherein the program executes the method for determining the vehicle monitoring strategy in the method embodiment.

[0125] According to an embodiment of the present invention, a vehicle is also provided for performing the method for determining the vehicle monitoring strategy in Embodiment 1.

[0126] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0127] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0128] In the several embodiments provided by this invention, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed can be through some interfaces; the indirect coupling or communication connection of units or modules can be electrical or other forms.

[0129] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0130] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0131] If the integrated unit is implemented as a software functional unit and sold or used as an independent functional component, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software functional component. This computer software functional component is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0132] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for determining a vehicle monitoring strategy, characterized in that, include: Acquire vehicle driving environment data and driving habit data of the driver of the vehicle, wherein the driving habit data is used to represent the driving behavior of the driver of the vehicle, and the driving habit data includes at least: driving speed, route preference, driving time period, driving behavior, driving style, vehicle maintenance and driving record; The driving environment data and the driving habit data are input into the target driving prediction model to predict the target driving safety information of the vehicle. The target driving prediction model is trained using the historical driving environment data of the vehicle and the historical driving habit data of the driver of the vehicle. The target driving safety information is used to indicate the safety information of the vehicle during driving. Based on the target driving safety information, a target monitoring strategy for the vehicle is determined. The target monitoring strategy is used to monitor the functional safety rules of the vehicle. The target monitoring strategy includes at least: the operating mechanism of the sensors, the operating mechanism of the actuators and the operating mechanism of the controller, as well as the functional safety state that the vehicle needs to achieve. The method further includes: incrementally learning the target driving prediction model based on the target monitoring strategy, and updating the target driving prediction model.

2. The method according to claim 1, characterized in that, Based on the target driving safety information, a target monitoring strategy for the vehicle is determined, including: The target driving safety information is matched with driving safety information stored in the cloud to obtain a matching result, wherein the matching result is used to indicate the degree of matching between the target driving safety information and the driving safety information stored in the cloud; Based on the matching results, the target monitoring strategy for the vehicle is determined.

3. The method according to claim 2, characterized in that, Based on the matching results, the target monitoring strategy for the vehicle is determined, including: In response to the matching result being greater than or equal to the matching threshold, a monitoring strategy corresponding to the driving safety information stored in the cloud is determined; The monitoring strategy is determined as the target monitoring strategy.

4. The method according to claim 2, characterized in that, The method further includes: In response to the matching result being less than the matching threshold, the target driving safety information is stored in the cloud.

5. The method according to claim 1, characterized in that, The method further includes: Obtain the vehicle's historical driving environment data and the driver's historical driving habit data; The initial driving prediction model is trained using the historical driving environment data and the historical driving habit data to obtain the target driving prediction model.

6. A device for determining a vehicle monitoring strategy, characterized in that, include: The acquisition unit is used to acquire vehicle driving environment data and driving habit data of the driver of the vehicle, wherein the driving habit data is used to represent the driving behavior of the driver of the vehicle, and the driving habit data includes at least: driving speed, route preference, driving time period, driving behavior, driving style, vehicle maintenance and driving record; The prediction unit is used to input the driving environment data and the driving habit data into the target driving prediction model to predict the target driving safety information of the vehicle. The target driving prediction model is trained using the historical driving environment data of the vehicle and the historical driving habit data of the driver of the vehicle. The target driving safety information is used to indicate the safety information of the vehicle during driving. The determining unit is configured to determine a target monitoring strategy for the vehicle based on the target driving safety information, wherein the target monitoring strategy is a rule for monitoring the functional safety of the vehicle, and the target monitoring strategy includes at least: the operating mechanism of the sensor, the operating mechanism of the actuator and the operating mechanism of the controller, and the functional safety state that the vehicle needs to achieve; The prediction unit is also used to incrementally learn the target driving prediction model based on the target monitoring strategy and update the target driving prediction model.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein when the program is run by a processor, it controls the device in which the storage medium is located to perform the method according to any one of claims 1 to 5.

8. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method according to any one of claims 1 to 5 when it runs.

9. A vehicle, characterized in that, The vehicle is used to perform the method according to any one of claims 1 to 5.

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