CN110920539A

CN110920539A - Vehicle driving analysis method and device, electronic device and computer storage medium

Info

Publication number: CN110920539A
Application number: CN201911117528.6A
Authority: CN
Inventors: 高文智
Original assignee: Singularity Automobile R & D Center Co Ltd
Current assignee: Singularity Automobile R & D Center Co Ltd
Priority date: 2019-11-15
Filing date: 2019-11-15
Publication date: 2020-03-27

Abstract

The embodiment of the disclosure discloses a vehicle driving analysis method and device, electronic equipment and a computer storage medium, wherein the method comprises the following steps: synchronously acquiring running state data and driving environment data of a vehicle; inputting the synchronously acquired running state data and driving environment data of the vehicle into a driving model, and outputting a driving operation instruction through the driving model; the driving model is obtained based on the synchronously acquired running state data of the vehicle, the driving environment data and the driver image training. The embodiment of the disclosure can output a correct driving operation instruction by the running state data and the driving environment data of the vehicle based on the driving model, so that the driver can perform subsequent application based on the driving operation prompt.

Description

Vehicle driving analysis method and device, electronic device and computer storage medium

Technical Field

The present disclosure relates to the field of unmanned driving, and in particular, to a vehicle driving analysis method and apparatus, an electronic device, and a computer storage medium.

Background

With the development of society, automobiles have become indispensable vehicles for people in daily traffic. In real life, drivers have many unsafe or unreasonable operations in the driving process of vehicles due to the fact that the driving technologies of the drivers are different, and traffic accidents are easily caused.

With the development of scientific technology, the unmanned technology is beginning to slowly move into people's lives. The unmanned automobile is one of intelligent automobiles, is also called as a wheeled mobile robot, and mainly utilizes an unmanned technology to realize automatic driving of a vehicle by means of a computer system in the automobile, various sensors and the like. Compared with a manual driving technology, the unmanned driving technology can avoid traffic accidents caused by fatigue driving, distraction and the like of a user, so that the driving safety is improved.

Disclosure of Invention

In order to solve the above technical problem, the embodiments of the present disclosure provide a technical solution for vehicle driving.

According to an aspect of an embodiment of the present disclosure, there is provided a vehicle driving behavior analysis method including:

synchronously acquiring running state data and driving environment data of a vehicle;

inputting the synchronously acquired running state data and driving environment data of the vehicle into a driving model, and outputting a driving operation instruction through the driving model;

the driving model is obtained based on the synchronously acquired running state data of the vehicle, the driving environment data and the driver image training.

Optionally, in the method according to any embodiment of the present invention, the synchronously acquiring the operating state data and the driving environment data of the vehicle includes:

and acquiring the running state data and the driving environment data of the vehicle according to the same acquisition frequency under the synchronous clock.

Optionally, in the method according to any embodiment of the present invention, the acquiring the operating state data of the vehicle includes: acquiring running state data of the vehicle through a vehicle-mounted sensor and a Controller Area Network (CAN) bus; and/or the presence of a gas in the gas,

the collecting driving environment data includes: and acquiring driving environment data of the vehicle through a sensor arranged outside the vehicle.

Optionally, in the method of any embodiment of the present invention, the driving model is provided at a vehicle end;

the step of inputting the running state data and the driving environment data of the vehicle which are synchronously collected into a driving model and outputting a driving operation instruction through the driving model comprises the following steps: and the vehicle end inputs the synchronously acquired running state data and driving environment data of the vehicle into a driving model, and outputs a driving operation instruction through the driving model.

Optionally, in the method of any embodiment of the present invention, the driving model is set in a server;

the step of inputting the running state data and the driving environment data of the vehicle which are synchronously collected into a driving model and outputting a driving operation instruction through the driving model comprises the following steps: and the server inputs the synchronously acquired running state data and driving environment data of the vehicle into a driving model, outputs a driving operation instruction through the driving model and returns the driving operation instruction to the vehicle end.

Optionally, in the method according to any embodiment of the present invention, after the outputting of the driving operation instruction by the driving model, the method further includes:

controlling travel of the vehicle based on the driving operation instruction.

and reminding the driver of the driving operation instruction so that the driver drives the vehicle based on the driving operation instruction.

Optionally, in the method according to any embodiment of the present invention, before the prompting the driver of the driving operation instruction, the method further includes:

acquiring the driving action of a driver based on the running state data of the vehicle;

and when the driving action of the driver is inconsistent with the driving operation instruction, executing the operation of reminding the driver of the driving operation instruction.

and determining the driving behavior ability of the driver according to whether the driving action of the driver is consistent with the driving operation instruction.

Optionally, in the method according to any embodiment of the present invention, the method further includes:

collecting a driver image;

detecting the driving state of the driver according to the driver image, and reminding when the driving state of the driver is abnormal;

the driving state abnormality includes any one or more of: fatigue driving, distraction.

Optionally, in the method according to any embodiment of the present invention, the method further includes: and determining the driving safety level of the driver according to the driving state of the driver.

collecting performance parameter information for at least one component on the vehicle;

and determining whether the working state of each component in the at least one component is normal or not according to the performance parameter information and the change condition of the at least one component in a period of time, and predicting the service life and the aging model of each component in the at least one component.

sending the collected relevant data of the vehicle to a server so that the server can store the relevant data of the vehicle; wherein the relevant data of the vehicle comprises: the method comprises the steps of synchronously collecting running state data and driving environment data of the vehicle, a driver image, driving actions of the driver, performance parameter information of at least one component on the vehicle and a vehicle identification number of the vehicle.

Optionally, in the method according to any embodiment of the present invention, after the server stores the data related to the vehicle, the method further includes:

and the server analyzes the stored relevant data of the vehicle according to a first preset period and generates an analysis report.

Optionally, in the method according to any embodiment of the present invention, the analyzing the stored data related to the vehicle includes:

respectively carrying out data conversion and feature extraction on various data in the relevant data of the vehicle;

determining each item of data to carry out abnormal state detection according to the deviation of the characteristics of each item of data and the corresponding hyperplane; the hyperplane is constructed based on the characteristics extracted from the sample data of the normal state acquired in the driving model training process;

if one or more items of data are detected to have abnormal states, determining corresponding abnormal state events based on the data with the abnormal states; wherein the abnormal state event comprises any one or more of: the running state of the vehicle is abnormal, the behavior of the driver is abnormal, and the driving state of the driver is abnormal.

and the server sends the relevant data of the vehicle and/or the analysis report to the corresponding terminal according to the pre-stored address and a second preset period.

Optionally, in the method according to any embodiment of the present invention, the training of the driving model includes:

synchronously acquiring running state data, driving environment data and a driver image of a sample vehicle;

acquiring a data set corresponding to at least one driving behavior event of a driver in a normal state, wherein the data set comprises: running state data of the vehicle, driving environment data, and a driver image;

and training a driving model based on the data set corresponding to the at least one driving behavior event.

Optionally, in the method of any embodiment of the invention, the driving behavior event includes any one or more of: the driving state of the driver, the driving behavior of the driver;

the driver driving state comprises any one or more of: fatigue driving, concentration and distraction; alternatively, the first and second electrodes may be,

the driving behavior of the driver comprises any one or more of: lane changing, advancing, accelerating, decelerating, braking, backing, turning and braking.

Optionally, in the method according to any embodiment of the present invention, the acquiring a data set corresponding to at least one driving behavior event of a driver in a normal state includes:

establishing a data group corresponding to each driving behavior event based on the synchronously acquired running state data, driving environment data and driver images of the vehicle;

detecting whether a data set in an abnormal state exists in the data sets corresponding to the driving behavior events;

and if the data group in the abnormal state exists, removing the data group in the abnormal state from the data group corresponding to each driving behavior event to obtain the data group corresponding to the at least one driving behavior event in the normal state.

detecting whether the running state data, the driving environment data and the driver image of the abnormal state exist in the running state data, the driving environment data and the driver image of the vehicle;

if the running state data, the driving environment data and/or the driver image in the abnormal state exist, removing the running state data, the driving environment data and/or the driver image in the abnormal state and other corresponding synchronously acquired data to obtain the synchronously acquired running state data, the driving environment data and the driver image in the normal state;

and establishing a data group corresponding to at least one driving behavior event of the driver based on the synchronously acquired running state data, driving environment data and the driver image in the normal state.

Optionally, in the method according to any embodiment of the present invention, the abnormal state includes: the running state of the vehicle is abnormal, the behavior of the driver is abnormal, and the driving state of the driver is abnormal.

Optionally, in the method of any embodiment of the present invention, the abnormal operating state of the vehicle includes any one or more of: abnormal vehicle running speed, abnormal vehicle running direction and abnormal working state of any part of the vehicle; and/or the presence of a gas in the gas,

the behavior abnormality of the driver comprises any one or more of the following: overspeed driving, high-speed turning or high-speed continuous lane changing, turning without turning a steering lamp, rapid acceleration, rapid deceleration, passing without according to traffic signals, giving way without according to regulations, occupying emergency lanes, going backwards, filling doors in a parking state, pressing lane lines, driving deviating lane lines, driving too close to a front vehicle, and overtaking at a curve;

the driving state abnormality of the driver includes any one or more of: fatigue driving and distraction.

Optionally, in the method according to any embodiment of the present invention, detecting whether a data set in an abnormal state exists in the data sets corresponding to the driving behavior events, or whether operating state data, driving environment data, and a driver image in the abnormal state exist includes:

detecting whether the running state of the vehicle and the behavior of the driver are abnormal from the running state data of the vehicle; detecting whether a driving state is abnormal from the driver image.

According to another aspect of the embodiments of the present disclosure, there is provided a vehicle driving analysis apparatus including:

the first acquisition module is used for synchronously acquiring the running state data and the driving environment data of the vehicle;

the driving model is used for outputting driving operation instructions according to the synchronously acquired running state data and driving environment data of the vehicle; the driving model is obtained based on the synchronous acquisition of the running state data, the driving environment data and the driver image training of the vehicle.

Optionally, in the apparatus according to any embodiment of the present invention, the first collecting module is specifically configured to collect the operating state data and the driving environment data of the vehicle according to the same collecting frequency under a synchronous clock.

Optionally, in the apparatus according to any embodiment of the present invention, the first acquisition module is specifically configured to: acquiring running state data of the vehicle through a vehicle-mounted sensor and a Controller Area Network (CAN) bus; and/or, collecting the driving environment data through a sensor arranged outside the vehicle.

Optionally, in the apparatus according to any embodiment of the present invention, the first collecting module and the driving module are disposed at a vehicle end; alternatively, the first and second electrodes may be,

the first acquisition module is arranged at a vehicle end, and the driving model is arranged in the server.

Optionally, in the apparatus according to any embodiment of the present invention, the apparatus further includes:

a control module to control travel of the vehicle based on the driving operation indication.

and the prompting module is used for prompting the driver of the driving operation instruction after the driving operation instruction is output by the driving model so that the driver drives the vehicle based on the driving operation instruction.

the first acquisition module is used for acquiring the driving action of a driver specifically based on the running state data of the vehicle;

the identification module is used for identifying whether the driving action of the driver is consistent with the driving operation instruction or not;

the prompting module is specifically configured to prompt the driver of the operation of the driving operation instruction and execute the operation of prompting the driver of the dispersion of the operation attention of the driving operation instruction when the driving action of the driver is inconsistent with the driving operation instruction according to the recognition result of the recognition module.

and the determining module is used for determining the driving behavior ability of the driver according to the recognition result of the recognition module.

the second acquisition module is used for acquiring the driver image;

the detection module is used for detecting the driving state of the driver according to the driver image;

the prompting module is used for prompting when the driving state of the driver is abnormal according to the detection result of the detection module; wherein the driving state abnormality includes any one or more of: fatigue driving, distraction.

the determining module is used for determining the driving safety level of the driver according to the driving state of the driver.

Optionally, in the apparatus according to any embodiment of the present invention, the first collecting module is further configured to collect performance parameter information of at least one component on the vehicle;

the device further comprises:

and the prediction module is used for determining whether the working state of each component in the at least one component is normal or not according to the performance parameter information and the change condition of the at least one component in a period of time, and predicting the service life and the aging model of each component in the at least one component.

Optionally, in the apparatus according to any embodiment of the present invention, the apparatus further includes: a server;

the device further comprises:

the communication module is used for sending the acquired relevant data of the vehicle to a server; wherein the relevant data of the vehicle comprises: synchronously acquiring running state data and driving environment data of the vehicle, a driver image, driving actions of the driver, performance parameter information of at least one component on the vehicle and a vehicle identification number of the vehicle;

the server includes:

a storage module for storing data relating to the vehicle; and/or the presence of a gas in the gas,

and the analysis module is used for analyzing the stored relevant data of the vehicle according to a first preset period and generating an analysis report.

Optionally, in the apparatus according to any embodiment of the present invention, the analysis module is specifically configured to:

Optionally, in the apparatus according to any embodiment of the present invention, the server further includes:

and the sending module is used for sending the relevant data of the vehicle and/or the analysis report to the corresponding terminal according to the pre-stored address and a second preset period.

Optionally, in the apparatus according to any embodiment of the present invention, the first collecting module is further configured to synchronously collect running state data and driving environment data of a sample vehicle;

the second acquisition module is used for synchronously acquiring the driver image with the first acquisition module;

the device further comprises:

the second acquisition module is used for acquiring a data set corresponding to at least one driving behavior event of the driver in a normal state, wherein the data set comprises: running state data of the vehicle, driving environment data, and a driver image;

and the training module is used for training a driving model based on the data set corresponding to the at least one driving behavior event.

Optionally, in the apparatus of any embodiment of the present invention, the driving behavior event includes any one or more of: the driving state of the driver, the driving behavior of the driver;

the driver driving state comprises any one or more of: fatigue driving, concentration and distraction;

Optionally, in the apparatus according to any embodiment of the present invention, the second obtaining module is specifically configured to:

Optionally, in the apparatus according to any embodiment of the present invention, the abnormal state includes any one or more of: the running state of the vehicle is abnormal, the behavior of the driver is abnormal, and the driving state of the driver is abnormal.

Optionally, in the apparatus according to any embodiment of the present invention, the abnormal operating state of the vehicle includes any one or more of: abnormal vehicle running speed, abnormal vehicle running direction and abnormal working state of any part of the vehicle; and/or the presence of a gas in the gas,

According to still another aspect of the embodiments of the present invention, there is provided an electronic apparatus including: a memory for storing executable instructions;

a processor in communication with the memory to execute the executable instructions to perform the operations of the vehicle driving analysis method as described above.

According to still another aspect of the embodiments of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon, where the computer program, when executed by a processor, implements the vehicle driving analysis method according to any of the above embodiments of the present disclosure.

Based on the vehicle driving analysis method and device, the electronic equipment and the computer storage medium provided by the embodiment of the disclosure, the running state data and the driving environment data of the vehicle are synchronously acquired; the driving model inputs the synchronously acquired running state data and driving environment data of the vehicle into the driving model, and outputs a driving operation instruction through the driving model, and the driving model can output a correct driving operation instruction after inputting the synchronously acquired running state data and driving environment data of the vehicle into the driving model based on the synchronously acquired running state data, driving environment data and driver image training of the vehicle, so that the driver can perform subsequent application based on the driving operation instruction, for example, the vehicle is driven based on the driving operation instruction, automatic driving of the vehicle is realized, safe driving of the vehicle can be ensured, and traffic accident probability is reduced; the driver prompts driving of the vehicle based on the driving operation, thereby learning correct driving of the vehicle under various driving environments; when the driving action of the driver is inconsistent with the driving operation instruction, the driving operation instruction reminding device reminds the driver of the driving operation instruction, so that traffic accidents caused by incorrect driving action of the driver are avoided, and safe driving of the vehicle is realized; and so on.

The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.

The present disclosure may be more clearly understood from the following detailed description, taken with reference to the accompanying drawings, in which:

FIG. 1 is a flow chart of one embodiment of a vehicle driving analysis method of the present disclosure.

FIG. 2 is a flow chart of another embodiment of a vehicle driving analysis method of the present disclosure.

FIG. 3 is a flow chart of one embodiment of a driving model training method in an embodiment of the present disclosure.

Fig. 4 is a schematic structural diagram of an embodiment of the driving analysis device of the present disclosure.

Fig. 5 is a schematic structural diagram of another embodiment of the vehicle driving analysis device according to the present disclosure.

Fig. 6 is a schematic structural diagram of another embodiment of a vehicle driving analysis device according to the present disclosure.

Fig. 7 is an exemplary block diagram of an electronic device embodiment of the present disclosure.

Detailed Description

Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.

It will be understood by those of skill in the art that the terms "first," "second," and the like in the embodiments of the present disclosure are used merely to distinguish one element from another, and are not intended to imply any particular technical meaning, nor is the necessary logical order between them.

It is also understood that in embodiments of the present disclosure, "a plurality" may refer to two or more and "at least one" may refer to one, two or more.

It is also to be understood that any reference to any component, data, or structure in the embodiments of the disclosure, may be generally understood as one or more, unless explicitly defined otherwise or stated otherwise.

In addition, the term "and/or" in the present disclosure is only one kind of association relationship describing an associated object, and means that three kinds of relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" in the present disclosure generally indicates that the former and latter associated objects are in an "or" relationship.

It should also be understood that the description of the various embodiments of the present disclosure emphasizes the differences between the various embodiments, and the same or similar parts may be referred to each other, so that the descriptions thereof are omitted for brevity.

Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.

The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.

Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate.

It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.

The disclosed embodiments may be applied to electronic devices such as terminal devices, computer systems, servers, etc., which are operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well known terminal devices, computing systems, environments, and/or configurations that may be suitable for use with electronic devices, such as terminal devices, computer systems, servers, and the like, include, but are not limited to: personal computer systems, server computer systems, thin clients, thick clients, hand-held or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, networked personal computers, minicomputer systems, mainframe computer systems, distributed cloud computing environments that include any of the above, and the like.

Electronic devices such as terminal devices, computer systems, servers, etc. may be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, etc. that perform particular tasks or implement particular abstract data types. The computer system/server may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media including memory storage devices.

FIG. 1 is a flow chart of one embodiment of a vehicle driving analysis method of the present disclosure. As shown in fig. 1, the vehicle driving analysis method includes:

and 102, synchronously acquiring the running state data and the driving environment data of the vehicle.

The operating state data of the vehicle is used for representing the operating state of the vehicle and/or the operating state of components in the vehicle, and may include, but is not limited to, any one or more of the following data: the vehicle running speed, the X, Y direction acceleration during the vehicle running process, the engine speed, the vehicle steering lamp data, the vehicle steering angle, the angle between the chassis guard plate at the bottom of the vehicle and the horizontal road surface and the like. The driving environment data is used to represent environment data in which the vehicle is traveling, and may include, for example, but not limited to, any one or more of the following: road conditions, obstacle conditions, traffic conditions, etc. in the driving environment of the vehicle.

And 104, inputting the running state data and the driving environment data of the synchronously collected vehicle into a driving model, and outputting a driving operation instruction through the driving model.

The driving model is obtained based on synchronously acquired running state data of the vehicle, driving environment data and driver image training.

For example, the driving operation indication may include, but is not limited to: steering, accelerating, decelerating, braking, stopping, etc., the driving model may output driving operation instructions in the form of data to other modules, such as to a control module of the vehicle, which controls the running of the vehicle, or to a prompting module (such as a display module, a sound, etc. on the vehicle), which prompts the driver's operations.

In some implementations of embodiments of the present disclosure, the driving model may be implemented by a neural network model, for example. The Neural Network may be a deep Neural Network, such as a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), a Long Short Term Memory (LSTM) model, and so on. The embodiment of the present disclosure does not limit the specific implementation manner of the driving model.

Based on the embodiment, the running state data and the driving environment data of the vehicle are synchronously acquired, the running state data and the driving environment data of the vehicle which are synchronously acquired are input into the driving model, and the driving operation instruction is output through the driving model.

In some implementations of embodiments of the invention, operation 102 may include: and acquiring running state data and driving environment data of the vehicle according to the same acquisition frequency under the synchronous clock.

Acquiring running state data and driving environment data of a vehicle according to the same acquisition frequency based on a synchronous clock, so that the acquired running state data and driving environment data of the vehicle can keep time consistency, so as to acquire the running state data and the driving environment data of the vehicle at the same moment, inputting the running state data and the driving environment data of the vehicle which are synchronously acquired into a driving model, and acquiring a driving operation instruction matched with the running state data and the driving environment data of the vehicle at the moment so as to perform control operation matched with the running state data and the driving environment data of the vehicle at the moment on the vehicle in an automatic driving scene, thereby improving the safety of automatic driving; or in the driving learning, the trainee can learn the correct control operation under the running state data and the driving environment data so as to realize the correct driving of the vehicle.

In some implementations of embodiments of the invention, operation 102 may include: collecting running state data of a vehicle through a vehicle-mounted sensor and a Controller Area Network (CAN) bus; and/or, collecting driving environment data of the vehicle through a sensor arranged outside the vehicle.

In some embodiments, the sensor external to the vehicle may include, for example, but not limited to, any one or more of the following: ultrasonic sensors, lidar or radar sensors, etc., wherein the onboard sensors may include, for example, but are not limited to, any one or more of: speed sensors, brake pedal Position sensors, accelerator pedal Position sensors, steering wheel pressure sensors, Global Positioning System (GPS), Inertial Measurement Unit (IMU), and the like.

The CAN bus belongs to the field bus category, is a serial communication network which effectively supports distributed control or real-time control, and has the advantages of high performance, high reliability, real-time performance and the like, and is widely applied to various departments such as industrial automation, various control devices, vehicles, medical instruments, buildings, environmental control and the like. The parameter information of the vehicle working condition CAN be known through data collected by a controller area network CAN bus.

By the laser radar, depth information of about less than 70 meters around the vehicle can be collected, so that obstacle information within 70 meters from the vehicle, such as the distance between an obstacle and the vehicle, the position of the obstacle, and the like, can be detected.

The information of the speed, the position of the brake pedal, the position of the accelerator pedal, the position of the steering wheel, the pressure of the steering wheel and the like of the vehicle can be correspondingly acquired through the speed sensor, the position sensor of the brake pedal, the position sensor of the accelerator pedal, the position sensor of the steering wheel and the pressure sensor of the steering wheel.

Through the GPS, the position of the vehicle can be collected so as to calculate the speed of the vehicle. Through the IMU, the attitude information of the vehicle can be collected, so that any one or more of the following information can be obtained: altitude, velocity, acceleration, angular rate, and the like.

Based on the embodiment, the running state data of the vehicle and the perfection data of the driving environment where the vehicle is located can be comprehensively collected, so that a correct driving operation instruction can be made based on the running state data of the vehicle and the driving environment.

In some of these embodiments, the driving model may be provided on the vehicle side. Accordingly, in operation 104, the vehicle end may input the synchronously acquired operating state data and driving environment data of the vehicle to the driving model, and output a driving operation instruction through the driving model.

Alternatively, in other embodiments, the driving model may be provided in the server. Accordingly, in operation 102, the vehicle end may transmit the synchronously acquired operating state data and driving environment data of the vehicle to the server; in operation 104, the server inputs the synchronously acquired operating state data and driving environment data of the vehicle to the driving model, outputs a driving operation instruction through the driving model, and returns the driving operation instruction to the vehicle side.

In this embodiment, the driving model also can set up in the server, because the high in the clouds server has powerful storage resource and computational analysis ability, the driving model is based on running state data and the driving environment data of vehicle, can output driving operation instruction fast to improve vehicle driving analysis efficiency.

In another embodiment of the vehicle driving analysis method of the present invention, after the above operation 104, the method may further include:

travel of the vehicle is controlled based on the driving operation instruction.

Based on the embodiment, the driving model outputs correct driving operation instructions, and the vehicle is driven based on the driving operation instructions, so that the automatic driving of the vehicle is realized, the safe driving of the vehicle can be ensured, the driving safety is improved, and the probability of traffic accidents is reduced.

the driver is prompted with a driving operation instruction so that the driver drives the vehicle based on the driving operation instruction.

Based on the embodiment, it is also possible to prompt the driver for a correct driving operation based on the driving operation instruction, so that the driver prompts the driving of the vehicle based on the driving operation, thereby learning the correct driving of the vehicle in various driving environments.

In addition, in another embodiment of the vehicle driving analysis method of the present invention, after the above operation 104, the method may further include:

and when the driving action of the driver is inconsistent with the driving operation instruction, reminding the driver of the operation of the driving operation instruction and executing the operation for reminding the driver of the driving operation instruction. The driving action of the driver may include, but is not limited to, any one or more of the following: lane changing, advancing, accelerating, decelerating, braking, backing, turning, braking and the like.

Based on the embodiment, the correct driving operation instruction can be output through the driving model, whether the driving action of the driver is consistent with the driving operation instruction or not is identified, and when the driving action of the driver is inconsistent with the driving operation instruction, the driver is timely reminded, so that traffic accidents caused by incorrect driving actions of the driver can be effectively avoided, and safe driving of the vehicle is realized.

Based on the embodiment, the driving action of the driver is acquired through the running state data of the vehicle, and compared with the driving operation instruction, the driving behavior ability of the driver can be determined and the driving safety of the driver can be determined according to the consistency between the driving action and the driving operation of the driver, so that the driving ability is improved and safe driving is realized.

FIG. 2 is a flow chart of another embodiment of a vehicle driving analysis method of the present disclosure. As shown in fig. 2, on the basis of the above embodiment, the vehicle driving analysis method according to the embodiment may further include:

202, a driver image is acquired.

The driver image may include, but is not limited to, any one of the following items: head images, hand images, etc. of the driver. In some of these embodiments, the driver images may be captured by a camera disposed within the vehicle.

204, detecting the driving state of the driver according to the driver image.

The driving state of the driver, for example, may include, but is not limited to, any one or more of the following: fatigue driving, distraction, and concentration.

In some embodiments, the driver image may be input into a pre-trained neural network, and whether the driver is tired or distracted may be detected by the pre-trained neural network, for example, whether the driver is tired may be detected by detecting a state of eyes closed of the driver in the driver image or a movement of a head of the driver in a vertical direction for a period of time; whether or not the driver is distracted can be confirmed by detecting the degree of horizontal angle deviation in the head posture angle of the driver in the driver image, the degree of line-of-sight direction deviation, the presence or absence of a distracting action (e.g., smoking, making a call, etc.). And 206, reminding when the driving state of the driver is abnormal.

When a driver drives a vehicle for a long time or driving habits are not good, fatigue or distraction is easy to occur, thereby causing traffic accidents. Based on this embodiment, the driving state of driver is detected through the driver image of gathering, can effectively detect driver's driving state, when finding that the driver has tired or the state of distraction, in time reminds the driver to avoid the occurence of failure, realize safe driving.

In another embodiment of the vehicle driving analysis method of the present invention, after the above operation 204, the method may further include: the driving safety level of the driver is determined according to the driving state of the driver.

Based on the collected driver image, the current driving state of the driver can be detected to determine the driving safety level of the driver, and the condition of the driver in the safety aspect of vehicle driving can be known through the driving safety level so as to improve the safety awareness of the driver.

In addition, in another embodiment of the vehicle driving analysis method of the present invention, the method may further include:

performance parameter information for at least one component on a vehicle is collected. Wherein, the components on the vehicle may include, for example, but are not limited to, any one or more of: engines, chassis, brakes, throttles, windows, air conditioners, audio equipment, etc.;

and determining whether the working state of each component in the at least one component is normal or not according to the performance parameter information and the change condition of the at least one component in a period of time, and predicting the service life (namely, how long the at least one component can work normally) and the aging model (namely, according to what rule the at least one component is aged).

In some embodiments, the performance parameter information of at least one component on the vehicle may be collected via a CAN bus.

Based on the embodiment, the performance parameter information of at least one part on the vehicle can be collected, whether the working state of the part in the vehicle is normal or not is determined, and the service life and the aging model of the part are predicted, so that the current state and the future state of each part can be known in time, the fault part can be replaced in time, and the driving safety of the vehicle is improved.

In addition, in the vehicle driving analysis method according to the above embodiment of the present disclosure, the method may further include:

and sending the collected relevant data of the vehicle to a server so that the server stores the relevant data of the vehicle. The relevant data of the vehicle may include, but is not limited to: the method includes the steps of synchronously collecting running state data and driving environment data of the Vehicle, an image of a driver, driving actions of the driver, performance parameter information of at least one component on the Vehicle and a Vehicle Identification Number (VIN) of the Vehicle. Wherein the VIN is used to uniquely identify a vehicle.

Alternatively, in the vehicle driving analysis method of the above embodiment, after the server stores the relevant data of the vehicle, the stored relevant data of the vehicle may be analyzed at a first preset period (e.g., 1 hour, 1 day, one month, etc.) and an analysis report may be generated.

In some embodiments, analyzing the stored data related to the vehicle may be implemented as follows:

and respectively carrying out data conversion and feature extraction on various data in the relevant data of the vehicle. The data conversion may include, for example: when abnormal data (such as data loss, large deviation from adjacent data and the like) occurs in each item of data, data acquisition is carried out again, and time domain to frequency domain conversion is carried out on each item of acquired data (such as running state data, driving environment data, driver images and the like);

and determining each item of data to carry out abnormal state detection according to the deviation of the characteristics of each item of data and the corresponding hyperplane. The hyperplane is constructed and obtained based on the characteristics extracted from the sample data of the normal state acquired in the training process of the driving model;

and if one or more items of data are detected to have abnormal states, determining corresponding abnormal state events based on the data with the abnormal states. The abnormal state event may include, but is not limited to, any one or more of the following: the running state of the vehicle is abnormal, the behavior of the driver is abnormal, the driving state of the driver is abnormal, and the like.

In some optional examples, the hyperplane is constructed by performing feature extraction on sample data of a normal state, which can be collected in a driving model training process. For example, the data set quality centers can be generated respectively from the collected running state data samples, driving environment data samples and driver image samples in the normal state, feature extraction can be performed respectively on the data set quality centers, and the hyperplane can be constructed based on the extracted features to represent the normal feature values of the data set quality centers. In the vehicle running process, after the running state data, the driving environment data and the driver image are collected, feature extraction may be performed respectively, and based on comparison between the extracted features and the corresponding hyperplane, a deviation between a principal vector (PCA) of the extracted features and a principal vector of features of the corresponding hyperplane may be calculated (for example, a Mahalanobis distance before the two may be calculated), and if the deviation exceeds a preset threshold, it is confirmed that an abnormal state occurs in the data.

For example, the Mahalanobis distance between the principal vector of the extracted feature and the principal vector of the feature of the corresponding hyperplane may be calculated as follows:

wherein S ═ S₁，S₂，...，S_n]^TRepresenting data to be analyzed; u ═ u₁.u₂,…,u_n]^TThe mean value of sample data corresponding to each item of data to be analyzed is represented, and σ represents the covariance with the mean value of the data to be analyzed and the sample data.

For example, in the disclosed embodiment, the head pose of the driver may be determined through a series of driver images to determine the driver's intention or driving state. The head posture is a 6-dimensional value, including X, Y, Z coordinates, a pitch angle, a yaw angle and a rolling angle, and the 6-dimensional values can be used for judging the intention of a driver, such as lane changing, turning, reversing and the like, and can also be used for detecting whether the driver is in a normal driving state. And performing feature extraction from the driver image, calculating the Mahalanobis distance of the features of the hyperplane, which is constructed by performing feature extraction on the driver sample image in a normal state acquired in the driving model training process, and determining whether the head posture of the driver is abnormal or not based on whether the Mahalanobis distance exceeds a preset threshold or not.

Further optionally, in the vehicle driving analysis method of the foregoing embodiment, after the server stores the relevant data of the vehicle, the server may further send the relevant data and/or the analysis report of the vehicle to the corresponding terminal using the address according to a pre-stored address and according to a second preset period (for example, 0.5 hour, 1 day, and the like), so that the owner of the vehicle or a supplier, a service provider, and the like of the vehicle may know the relevant information of the vehicle in time, for example, the operating condition of the vehicle itself or a component therein, the driving condition of the vehicle, the operating state of the vehicle, the behavior of the driver, the driving state of the driver, and the like.

In addition, before the flow of the above embodiments of the present disclosure, the driving model may also be trained in advance. FIG. 3 is a flow chart of one embodiment of a driving model training method in an embodiment of the present disclosure. As shown in fig. 3, the training method of the driving model of this embodiment includes:

and 302, synchronously acquiring running state data, driving environment data and a driver image of the sample vehicle.

Wherein the sample vehicle is a vehicle for training a driving model.

304, acquiring a data set corresponding to at least one driving behavior event of the driver in a normal state, wherein the data set comprises: running state data of the vehicle, driving environment data, and a driver image.

And 306, training a driving model based on the data set corresponding to the at least one driving behavior event.

In some of these embodiments, the driving behavior event may include, for example, but not limited to, any one or more of: driver driving status, driver driving behavior, etc. The driving state of the driver may include, but is not limited to, any one or more of the following: fatigue driving, concentration, distraction, etc. The driving behavior of the driver may include, for example, but is not limited to, any one or more of: lane change, forward, acceleration, deceleration, braking, reverse, cornering, braking, and the like.

Based on the embodiment, the data group corresponding to at least one driving behavior event of the driver in the normal state is acquired according to the running state data of the sample vehicle, the driving environment data of the sample vehicle and the image data of the driver, and the driving model is trained based on the data group corresponding to the at least one driving behavior event of the driver, so that the training of the driving model based on the driving behavior events of the driver in various running states and driving environments of the sample vehicle in the normal state is realized, the driving model can learn the correct operation of the sample vehicle in various running states and driving environments, the sample vehicle can be controlled correctly according to the running states and driving environments of the sample vehicle in the subsequent automatic driving scene, and the safety of automatic driving is improved.

In some of these implementations of embodiments of the invention, 304 may include: establishing a data group corresponding to each driving behavior event based on synchronously acquired sample vehicle running state data, driving environment data and a driver image; detecting whether a data set in an abnormal state exists in the data sets corresponding to the driving behavior events; and if the data set in the abnormal state exists, removing the data set in the abnormal state from the data set corresponding to each driving behavior event to obtain the data set corresponding to at least one driving behavior event in the normal state.

Wherein it is possible to detect whether the running state of the sample vehicle and the behavior of the driver are abnormal from the running state data of the sample vehicle; whether the driving state is abnormal is detected from the driver image.

Based on the embodiment, the data group corresponding to each driving behavior event is established firstly based on synchronously acquiring the running state data, the driving environment data and the driver image of the sample vehicle, then whether the data group in the abnormal state exists in the data group corresponding to each driving behavior event is detected, if the data group in the abnormal state exists, the data group corresponding to each driving behavior event in the normal state can be obtained by removing the data group in the abnormal state, so that the data group of the driving behavior event of the training target in the driving model can be correct, the driving model can be trained by using the normal data, and the accuracy of the driving model is improved.

Alternatively, in other implementations of embodiments of the invention, 304 may include: detecting whether the running state data, the driving environment data and the driver image of the abnormal state exist in the running state data, the driving environment data and the driver image of the sample vehicle; if the running state data, the driving environment data and/or the driver image in the abnormal state exist, removing the running state data, the driving environment data and/or the driver image in the abnormal state and other corresponding synchronously acquired data to obtain the synchronously acquired running state data, the driving environment data and the driver image in the normal state; and establishing a data group corresponding to at least one driving behavior event of the driver based on the synchronously acquired running state data, driving environment data and the driver image in the normal state.

The abnormal state data and the driving environment data and/or the driver image and the corresponding other data collected synchronously are removed, that is, in the synchronously collected operating state data, driving environment data and/or the driver image, if any abnormal state data exists, the abnormal state data and the other two items of data synchronous with the abnormal state data are removed, for example, if the operating state data is found to be an abnormal state in the synchronously collected operating state data, driving environment data and/or the driver image, the abnormal state operating state data and the driving environment data and the driver image collected synchronously with the abnormal state operating state data are removed.

Based on this embodiment, it is possible to detect the running state data of the sample vehicle, the driving environment data and the driver image, whether the running state data, the driving environment data and the driver image in the abnormal state exist or not, removing the running state data, the driving environment data and/or the driver image in the abnormal state and other corresponding synchronously acquired data to obtain the synchronously acquired running state data, the driving environment data and the driver image in the normal state, then establishing a data group corresponding to at least one driving behavior event of the driver based on the synchronously acquired running state data, driving environment data and the driver image in the normal state, therefore, the data set of the driving behavior event of the training target in the driving model can be correct, the driving model can be trained by normal data, and the accuracy of the driving model is improved.

In some embodiments, the abnormal state may include, but is not limited to, any one or more of the following: the running state of the vehicle is abnormal, the behavior of the driver is abnormal, the driving state of the driver is abnormal, and the like.

In some embodiments, the running state of the sample vehicle is abnormal, for example, but not limited to, any one or more of the following: the running speed of the sample vehicle is abnormal, the running direction of the sample vehicle is abnormal, the working state of any component of the sample vehicle is abnormal (i.e. the mechanical state of any component of the vehicle is abnormal), and the like. The behavior abnormality of the driver may include, for example, but is not limited to, any one or more of the following: speeding, high speed turning or high speed continuous lane change, turning without turning a turn signal, rapid acceleration, rapid deceleration, passing without a traffic signal, passing without a regulation, occupying an emergency lane, going backwards, a parked state accelerator, pressing a lane line, running off a lane line, passing too close to a preceding vehicle, passing a curve, and the like. The driving state abnormality of the driver may include, for example, but is not limited to, any one or more of the following: fatigue driving and distraction.

In some of the embodiments, it may be detected from the running state data of the sample vehicle whether the running state of the sample vehicle and the behavior of the driver are abnormal, and whether the driving state is abnormal from the driver image.

In the embodiment of the disclosure, after the running state data, the driving environment data and the driver image of the sample vehicle are synchronously acquired, the data of the abnormal state in the sample vehicle needs to be detected and filtered, and the data correspond to the abnormal event of the sample vehicle and the abnormal behavior event of the driver, so as to avoid that the driving model is trained by using the data of the part of the abnormal state, which results in the error of the trained driving model. In a specific application, the detection of abnormal data can be performed by:

constructing a normal hyperplane, namely generating data set quality centers respectively from the running state data samples, the driving environment data samples and the driver image samples collected in a normal state, respectively extracting features of the data set quality centers, and constructing the normal hyperplane based on the extracted features to express normal feature values of the data set quality centers;

after the running state data, the driving environment data and the driver image are collected, feature extraction is respectively carried out, the extracted features are compared with the corresponding normal hyperplane, feature deviation between the extracted features and the corresponding normal hyperplane is calculated, and if the deviation exceeds a preset threshold value, the data are confirmed to be abnormal data.

Fig. 4 is a schematic structural diagram of an embodiment of the driving analysis apparatus of the present disclosure, where the driving analysis apparatus of the embodiment can be used to implement the above-mentioned embodiments of the driving behavior analysis method of the present disclosure. As shown in fig. 4, the driving analysis apparatus of this embodiment includes: the system comprises a first acquisition module and a driving model. Wherein:

the first acquisition module is used for synchronously acquiring the running state data and the driving environment data of the vehicle.

In some embodiments, the first collecting module is specifically configured to collect the operating state data and the driving environment data of the vehicle at the same collecting frequency under the synchronous clock.

And the driving model is used for outputting driving operation instructions according to the synchronously acquired running state data and driving environment data of the vehicle. The driving model is obtained based on the synchronously acquired running state data of the vehicle, the driving environment data and the driver image training.

The vehicle driving analysis device provided based on the above embodiment of the present disclosure synchronously acquires the operating state data and the driving environment data of the vehicle, inputs the synchronously acquired operating state data and driving environment data of the vehicle into the driving model, and outputs the driving operation instruction through the driving model.

In some embodiments, the first acquisition module is specifically configured to: acquiring running state data of a vehicle through a vehicle-mounted sensor and a CAN bus; and/or, collecting driving environment data through a sensor arranged outside the vehicle.

In some alternative examples, the onboard sensors include any one or more of: a speed sensor, a brake pedal position sensor, an accelerator pedal position sensor, a steering wheel pressure sensor, a GPS sensor and an IMU inertial measurement unit sensor; the sensor disposed outside the vehicle may include any one or more of: an ultrasonic sensor, a lidar sensor, or a radar sensor.

In some embodiments, the first acquisition module and the driving module are disposed on the vehicle side. Alternatively, in other embodiments, the first acquisition module is disposed at the vehicle end and the driving model is disposed in the server.

Fig. 5 is a schematic structural diagram of another embodiment of the driving analysis device for a vehicle according to the present disclosure, and as shown in fig. 5, compared with the embodiment shown in fig. 4, the driving analysis device for the embodiment further includes:

and the control module is used for controlling the running of the vehicle based on the driving operation instruction output by the driving model.

In addition, referring to fig. 5 again, the vehicle driving analysis apparatus according to the embodiment of the present disclosure may further include: and the prompting module is used for prompting the driver of the driving operation instruction after the driving operation instruction is output through the driving model so that the driver can drive the vehicle based on the driving operation instruction.

In addition, referring to fig. 5 again, the vehicle driving analysis apparatus according to the embodiment of the present disclosure may further include: the first acquisition module is used for acquiring the driving action of a driver specifically based on the running state data of the vehicle; the identification module is used for identifying whether the driving action of the driver is consistent with the driving operation instruction; and the prompting module is specifically used for prompting the operation of the driving operation instruction of the driver and executing the operation distraction for prompting the driving operation instruction of the driver when the driving action of the driver is inconsistent with the driving operation instruction according to the identification result of the identification module.

In addition, referring to fig. 5 again, the vehicle driving analysis apparatus according to the embodiment of the present disclosure may further include: the first acquisition module is used for acquiring the driving action of a driver specifically based on the running state data of the vehicle; the identification module is used for identifying whether the driving action of the driver is consistent with the driving operation instruction; and the determining module is used for determining the driving behavior ability of the driver according to the recognition result of the recognition module.

In addition, referring to fig. 5 again, the vehicle driving analysis apparatus according to the embodiment of the present disclosure may further include: the second acquisition module is used for acquiring the driver image; the detection module is used for detecting the driving state of the driver according to the driver image; the prompting module is used for prompting when the driving state of the driver is abnormal according to the detection result of the detection module; wherein the driving state abnormality includes any one or more of: fatigue driving, distraction.

In addition, referring back to fig. 5, in the vehicle driving analysis apparatus according to the embodiment of the present disclosure, the determining module is configured to determine the driving safety level of the driver according to the driving state of the driver.

In addition, referring to fig. 5 again, in the vehicle driving analysis apparatus according to the embodiment of the present disclosure, the first collecting module may be further configured to collect performance parameter information of at least one component on the vehicle; accordingly, the vehicle driving analysis device of this embodiment may further include: and the prediction module is used for determining whether the working state of each component in the at least one component is normal or not according to the performance parameter information and the change condition of the at least one component in a period of time, and predicting the service life and the aging model of each component in the at least one component.

Fig. 6 is a schematic structural diagram of another embodiment of a vehicle driving analysis device according to the present disclosure. Referring to fig. 6, compared to the embodiment shown in fig. 4 or 5 described above, the vehicle driving analysis device of this embodiment further includes: server and communication module. The server comprises a storage module and an analysis module. Wherein:

the communication module is used for sending the acquired relevant data of the vehicle to the server; wherein the relevant data of the vehicle comprises: the method comprises the steps of synchronously collecting running state data and driving environment data of the vehicle, an image of a driver, driving actions of the driver, performance parameter information of at least one component on the vehicle and a vehicle identification number of the vehicle.

The storage module is used for storing relevant data of the vehicle; and/or the analysis module is used for analyzing the stored relevant data of the vehicle according to a first preset period and generating an analysis report.

In some embodiments, the analysis module is specifically configured to: respectively carrying out data conversion and feature extraction on various data in the relevant data of the vehicle; determining each item of data to carry out abnormal state detection according to the deviation of the characteristics of each item of data and the corresponding hyperplane; the hyperplane is constructed and obtained based on the characteristics extracted from the sample data of the normal state acquired in the training process of the driving model; if one or more items of data are detected to have abnormal states, determining corresponding abnormal state events based on the data with the abnormal states; wherein the abnormal state event comprises any one or more of the following items: the running state of the vehicle is abnormal, the behavior of the driver is abnormal, and the driving state of the driver is abnormal.

In addition, referring back to fig. 6, the server may further include: and the sending module is used for sending the relevant data and/or the analysis report of the vehicle to the corresponding terminal according to the pre-stored address and the second preset period.

In addition, referring to fig. 5 again, in the vehicle driving analysis apparatus according to the embodiment of the present disclosure, the first collection module may be further configured to synchronously collect the running state data and the driving environment data of the sample vehicle; and the second acquisition module is used for synchronously acquiring the images of the driver with the first acquisition module. Accordingly, the vehicle driving analysis device of this embodiment may further include: the second acquisition module is used for acquiring a data set corresponding to at least one driving behavior event of the driver in a normal state, and the data set comprises: running state data of the vehicle, driving environment data, and a driver image; and the training module is used for training a driving model based on the data set corresponding to the at least one driving behavior event.

In some of these embodiments, the driving behavior event may include, but is not limited to, any one or more of the following: the driving state of the driver and the driving behavior of the driver. The driving state of the driver may include, but is not limited to, any one or more of the following: fatigue driving, concentration and distraction; the driving behavior of the driver may include, but is not limited to, any one or more of: lane changing, advancing, accelerating, decelerating, braking, backing, turning and braking.

In some embodiments, the second obtaining module is specifically configured to: establishing a data group corresponding to each driving behavior event based on synchronously acquired running state data, driving environment data and a driver image of the vehicle; detecting whether a data set in an abnormal state exists in the data sets corresponding to the driving behavior events; and if the data set in the abnormal state exists, removing the data set in the abnormal state from the data set corresponding to each driving behavior event to obtain the data set corresponding to at least one driving behavior event in the normal state.

In some embodiments, the second obtaining module is specifically configured to: detecting whether the running state data, the driving environment data and the driver image of the abnormal state exist in the running state data, the driving environment data and the driver image of the vehicle; if the running state data, the driving environment data and/or the driver image in the abnormal state exist, removing the running state data, the driving environment data and/or the driver image in the abnormal state and other corresponding synchronously acquired data to obtain the synchronously acquired running state data, the driving environment data and the driver image in the normal state; and establishing a data group corresponding to at least one driving behavior event of the driver based on the synchronously acquired running state data, driving environment data and the driver image in the normal state.

In some embodiments, the abnormal state may include, but is not limited to, any one or more of the following: the running state of the vehicle is abnormal, the behavior of the driver is abnormal, and the driving state of the driver is abnormal.

The abnormal running state of the vehicle may include, but is not limited to, any one or more of the following: abnormal vehicle running speed, abnormal vehicle running direction and abnormal working state of any part of the vehicle; and/or, the driver's behavior is abnormal, which may include, but is not limited to, any one or more of: overspeed driving, high-speed turning or high-speed continuous lane changing, turning without turning a steering lamp, rapid acceleration, rapid deceleration, passing without according to traffic signals, giving way without according to regulations, occupying emergency lanes, going backwards, filling doors in a parking state, pressing lane lines, driving deviating lane lines, driving too close to a front vehicle, and overtaking at a curve; the driving state abnormality of the driver may include, but is not limited to, any one or more of the following: fatigue driving and distraction.

Based on the uneven driving technology of the vehicle drivers, the drivers have many unsafe or incorrect and unreasonable operations in the driving process of the vehicles or carry out fatigue driving, and traffic accidents are easily caused. Based on the trained driving model, in the subsequent driving scene, the correct driving operation can be made based on the driving model. Thereby avoiding traffic accidents and effectively achieving the purpose of safe driving.

In addition, an embodiment of the present disclosure also provides an electronic device, including:

a memory for storing a computer program;

a processor for executing the computer program stored in the memory, and when the computer program is executed, implementing the vehicle driving analysis method according to any of the above embodiments of the present disclosure.

Fig. 7 is an exemplary block diagram of an electronic device embodiment of the present disclosure. Next, an electronic apparatus according to an embodiment of the present disclosure is described with reference to fig. 7. The electronic device may be either or both of the first device and the second device, or a stand-alone device separate from them, which stand-alone device may communicate with the first device and the second device to receive the acquired input signals therefrom. As shown in fig. 7, the electronic device includes one or more processors and memory.

The processor may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. The processor may be configured to perform the process steps of the vehicle driving analysis method of fig. 1-3.

The memory may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, Random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, Read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on a computer readable storage medium and executed by a processor to implement the methods of vehicle driving analysis of the various embodiments of the disclosure above and/or other desired functions.

In one example, the electronic device may further include: an input device and an output device, which are interconnected by a bus system and/or other form of connection mechanism (not shown).

For simplicity, only some of the components of the electronic device relevant to the present disclosure are shown in fig. 7, omitting components such as buses, input/output interfaces, and the like. In addition, the electronic device may include any other suitable components, depending on the particular application.

In addition to the above-described methods and apparatus, embodiments of the present disclosure may also be a computer program product comprising computer program instructions that, when executed by a processor, cause the processor to perform the steps in the vehicle driving analysis method of the above-described embodiments of the present disclosure.

The computer program product may write program code for performing the operations of embodiments of the present disclosure in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

Furthermore, embodiments of the present disclosure may also be a computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, cause the processor to perform the steps in the vehicle driving analysis method of the above-described embodiments of the present disclosure.

A computer-readable storage medium may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may include, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The foregoing describes the general principles of the present disclosure in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in the present disclosure are merely examples and are not limiting, and they should not be considered essential to the various embodiments of the present disclosure. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the disclosure is not intended to be limited to the specific details so described.

In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same or similar parts in the embodiments are referred to each other. For the system embodiment, since it basically corresponds to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The block diagrams of devices, apparatuses, systems referred to in this disclosure are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations, etc. must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by those skilled in the art. Words such as "including", "comprising", "having", and the like are open-ended words that mean "including, but not limited to", and are used interchangeably therewith. As used herein, the words "or" and "refer to the word" and/or "and are used interchangeably herein unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".

The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.

It is also noted that in the devices, apparatuses, and methods of the present disclosure, each component or step can be decomposed and/or recombined. These decompositions and/or recombinations are to be considered equivalents of the present disclosure.

The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

The foregoing description has been presented for purposes of illustration and description. Furthermore, this description is not intended to limit embodiments of the disclosure to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.

Claims

1. A vehicle driving analysis method, characterized by comprising:

2. The method of claim 1, wherein the synchronously collecting operating state data and driving environment data of the vehicle comprises:

3. The method of claim 1 or 2, wherein the collecting operating state data of the vehicle comprises: acquiring running state data of the vehicle through a vehicle-mounted sensor and a Controller Area Network (CAN) bus; and/or the presence of a gas in the gas,

4. A method according to any of claims 1-3, characterized in that the driving model is arranged on the vehicle side;

5. A method according to any of claims 1-3, characterized in that the driving model is provided in a server;

6. The method according to any one of claims 1-5, further comprising, after outputting the driving operation indication via the driving model:

controlling travel of the vehicle based on the driving operation instruction.

7. The method according to any one of claims 1-5, further comprising, after outputting the driving operation indication via the driving model:

8. A vehicle driving analysis apparatus, characterized by comprising:

9. An electronic device, comprising:

a memory for storing a computer program;

a processor for executing a computer program stored in the memory, and when executed, implementing the method of any of the preceding claims 1-7.

10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of the preceding claims 1 to 7.