1. A human-vehicle sharing method based on big data analysis is characterized by comprising the following steps:

s100, obtaining vehicle distance data of a vehicle and obstacles around the vehicle at the current T moment, and obtaining weather data at the current T moment;

s200, inputting the vehicle distance data and the weather data into a trained BP neural network model to obtain driving operation data output values corresponding to the vehicle distance data and the weather data;

and S300, adjusting automatic parking parameters according to the driving operation data output values corresponding to the vehicle distance data and the weather data.

2. The people-vehicle sharing method based on big data analysis according to claim 1, characterized in that the environment information around the vehicle is collected by a laser radar and a CCD camera installed on the vehicle, and the obstacles around the vehicle comprise road type, signal lamps, signboards and side vehicles;

acquiring driving operation data through an automobile CAN, wherein the driving operation data comprises speed, acceleration, an accelerator pedal opening signal, steering wheel rotating speed, brake information and brake pedal frequency in the driving process of a driver driving an automobile;

weather data is acquired through a meteorological system.

3. The human-vehicle sharing method based on big data analysis according to claim 1, wherein the step of obtaining the vehicle distance data between the vehicle and the obstacles around the vehicle at the current time T at S100 comprises the steps of:

s10 pre-establishes the BP neural network model.

4. The human-vehicle sharing method based on big data analysis according to claim 1, wherein the step of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle at S100 further comprises the step of:

s20, collecting vehicle distance data and/or weather data of the vehicle and obstacles around the vehicle at Tn moment, and acquiring driving actions of a driver according to the vehicle distance data and the weather data;

s30, analyzing driving habits according to the vehicle distance data and/or weather data of the vehicle and obstacles around the vehicle and the driving actions made by the driver, and acquiring driving operation data corresponding to the driving habits;

s40, establishing a mapping relation model between the vehicle distance data and/or the weather data and the driving operation data;

s50, inputting the mapping relation model into a BP neural network model, and training the BP neural network model to obtain the trained BP neural network model.

5. The big data analysis-based human-vehicle sharing method according to claim 4, wherein after the step of collecting the distance data and/or the weather data between the vehicle and the obstacles around the vehicle at the time Tn and obtaining the driving action of the driver according to the distance data and the weather data at the time S20, the method comprises the steps of:

s21, the vehicle distance data are transmitted to a cloud server.

6. The human-vehicle sharing method based on big data analysis according to claim 4, wherein after the step S30 of analyzing driving habits according to the vehicle distance data and/or weather data of the vehicle and the obstacles around the vehicle and the driving actions made by the driver, and obtaining the driving operation data corresponding to the driving habits, the method comprises the steps of:

s31 transmits the driving operation data to the cloud.

7. The human-vehicle sharing method based on big data analysis according to claim 4, wherein the step of S50 inputting the mapping relationship model into a BP neural network model, training the BP neural network model, and obtaining the trained BP neural network model specifically comprises the steps of:

s51, the BP neural network model is trained in the cloud server.

8. The human-vehicle sharing method based on big data analysis according to claim 1, wherein after the step S300 of adjusting the automatic parking parameters according to the driving operation data output values corresponding to the vehicle distance data and the weather data, the method comprises the steps of:

and S400, automatically driving the vehicle according to the automatic parking parameters.

9. The human-vehicle sharing method based on big data analysis according to claim 1, wherein the step of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle at S100 further comprises the step of:

s60 establishing a connection with the meteorological system.

10. The human-vehicle sharing method based on big data analysis according to claim 1, wherein the step of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle at S100 further comprises the step of:

s70 connection with the cloud server is established.

Background

At present, many vehicles have certain automatic driving functions, such as lane keeping ACC at high speed, and cruise at high speed is realized based on automatic driving of a number L3 of some factories. In addition, when a low-speed automatic driving system such as automatic parking is used, the driver can still be in the seat when the driver needs to park, but the system can complete the parking action in the whole process.

However, a common problem of these automatic driving functions is that the driving habit of the driver is not considered, which results in the following two problems:

the safety of the system use is reduced. For example, for an automatic parking system, how to design a reasonable vehicle speed for parking is an engineering problem. However, too high may cause the driver in the vehicle to become strained and near the obstacle. Even if the system is safe and can be stopped before an obstacle, the driver can intervene in the system due to the tension of the driver, such as taking over or braking, so that the system falls down and unsafe results are caused. In addition, in a high-speed scene, when a vehicle cut-in arrives at the lane or two lanes arrive at a large truck, the tension of the driver can cause intervention, and unsafe conditions are caused.

The experience of the system usage is reduced. For the same reason, even if the vehicle is eventually successfully taken over by the driver, the overall experience is poor.

The fundamental reason for the above problems is that the design of these autopilot systems may take into account the statistical experience and experience of the driver, but fails to customize the individual drivers so that these problems cannot be solved effectively when the individual drivers use these systems.

Disclosure of Invention

The invention aims to provide a man-vehicle sharing method based on big data analysis, and aims to solve the existing problems.

In order to achieve the purpose, the invention provides the following technical scheme: a human-vehicle sharing method based on big data analysis comprises the following steps:

s100, obtaining vehicle distance data of a vehicle and obstacles around the vehicle at the current T moment, and obtaining weather data at the current T moment;

s200, inputting the vehicle distance data and the weather data into a trained BP neural network model to obtain driving operation data output values corresponding to the vehicle distance data and the weather data;

and S300, adjusting automatic parking parameters according to the driving operation data output values corresponding to the vehicle distance data and the weather data.

Preferably, the people and vehicle sharing method based on big data analysis of the invention is that the environment information around the vehicle is collected by a laser radar and a CCD camera which are arranged on the vehicle, and the obstacles around the vehicle comprise road types, signal lamps, signboards and side vehicles;

acquiring driving operation data through an automobile CAN, wherein the driving operation data comprises speed, acceleration, an accelerator pedal opening signal, steering wheel rotating speed, brake information and brake pedal frequency in the driving process of a driver driving an automobile;

weather data is acquired through a meteorological system.

As a man-vehicle sharing method based on big data analysis of the present invention, it is preferable that the step of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle at S100 includes the following steps before obtaining the weather data at the current time T:

s10 pre-establishes the BP neural network model.

As a man-vehicle sharing method based on big data analysis of the present invention, preferably, the step of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle at S100 further includes the following steps before obtaining the weather data at the current time T:

s20, collecting vehicle distance data and/or weather data of the vehicle and obstacles around the vehicle at Tn moment, and acquiring driving actions of a driver according to the vehicle distance data and the weather data;

s30, analyzing driving habits according to the vehicle distance data and/or weather data of the vehicle and obstacles around the vehicle and the driving actions made by the driver, and acquiring driving operation data corresponding to the driving habits;

s40, establishing a mapping relation model between the vehicle distance data and/or the weather data and the driving operation data;

s50, inputting the mapping relation model into a BP neural network model, and training the BP neural network model to obtain the trained BP neural network model.

As a man-vehicle sharing method based on big data analysis of the present invention, it is preferable that the method comprises the following steps after the step of collecting the distance data and/or the weather data between the vehicle and the obstacles around the vehicle at the time Tn at S20, and acquiring the driving action of the driver according to the distance data and the weather data:

s21, the vehicle distance data are transmitted to a cloud server.

As a man-vehicle sharing method based on big data analysis of the present invention, it is preferable that after the step S30 of analyzing driving habits according to the vehicle-to-vehicle distance data and/or weather data of the vehicle and the obstacles around the vehicle and the driving actions performed by the driver, and acquiring the driving operation data corresponding to the driving habits, the method comprises the steps of:

s31 transmits the driving operation data to the cloud.

Preferably, in the human-vehicle sharing method based on big data analysis of the present invention, the step of S50 inputting the mapping relationship model to the BP neural network model, and training the BP neural network model to obtain the trained BP neural network model specifically includes:

s51, the BP neural network model is trained in the cloud server.

As a human-vehicle sharing method based on big data analysis of the present invention, after the step S300 adjusts an automatic parking parameter according to a driving operation data output value corresponding to the vehicle distance data and the weather data, the method preferably includes the steps of:

and S400, automatically driving the vehicle according to the automatic parking parameters.

As a man-vehicle sharing method based on big data analysis of the present invention, preferably, the step of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle at S100 further includes the following steps before obtaining the weather data at the current time T:

s60 establishing a connection with the meteorological system.

As a man-vehicle sharing method based on big data analysis of the present invention, preferably, the step of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle at S100 further includes the following steps before obtaining the weather data at the current time T:

s70 connection with the cloud server is established.

Compared with the prior art, the invention has the following beneficial effects: the automatic driving system is in a man-vehicle driving mode for a long time when the final full automatic driving is not realized. After the automatic driving system is put into use, a user can form a use portrait of the specific driver through the input of data when the user drives by himself or the input of take-over data of the user when the automatic driving is started, and the user can conduct targeted parameter adjustment, so that the problem that when people and vehicles drive together, due to the fact that the automatic driving and the manual driving habits are different greatly, manual intervention on the automatic driving is caused can be solved, the safety of system use is improved, and the experience of system use is improved.

In conclusion, the invention relieves or solves the tension of the driver in various scenes in the man-vehicle driving mode of the automatic driving system, thereby reducing human intervention, improving the safety of the system and improving the experience.

Drawings

FIGS. 1-7 are flow diagrams of the present invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1 and 7, the present invention provides the following technical solutions: a human-vehicle sharing method based on big data analysis comprises the following steps:

s100, obtaining vehicle distance data of a vehicle and obstacles around the vehicle at the current T moment, and obtaining weather data at the current T moment;

s200, inputting the vehicle distance data and the weather data into a trained BP neural network model to obtain driving operation data output values corresponding to the vehicle distance data and the weather data;

and S300, adjusting automatic parking parameters according to the driving operation data output values corresponding to the vehicle distance data and the weather data.

Specifically, the method comprises the steps that the information of the surrounding environment of a vehicle is collected through a laser radar and a CCD camera which are installed on the vehicle, and obstacles around the vehicle comprise a road type, signal lamps, signboards and a side vehicle;

acquiring driving operation data through an automobile CAN, wherein the driving operation data comprises speed, acceleration, an accelerator pedal opening signal, steering wheel rotating speed, brake information and brake pedal frequency in the driving process of a driver driving an automobile;

weather data is acquired through a meteorological system.

In the embodiment, the distance data and the weather data of the vehicle and the obstacles around the vehicle at the current moment are adopted, the distance data and the weather data are input into a trained BP neural network model to obtain corresponding driving operation data output values, and then automatic parking parameters are adjusted according to the driving operation data output values, wherein the automatic parking parameters are close to or the same as the driving data generated by the driving habits of the driver, so that the automatic parking mode is similar to the man-made driving behavior, and the bad experience of the driver during automatic parking can be avoided so as to avoid man-made intervention automatic driving, the safety is improved, and meanwhile, the experience of a driver is improved.

Referring to fig. 2-4, specifically, before the step S100 of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle, the method includes the steps of:

s10 pre-establishes the BP neural network model.

Specifically, before the step S100 of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle, the method further includes the steps of:

s20, collecting vehicle distance data and/or weather data of the vehicle and obstacles around the vehicle at Tn moment, and acquiring driving actions of a driver according to the vehicle distance data and the weather data;

s30, analyzing driving habits according to the vehicle distance data and/or weather data of the vehicle and obstacles around the vehicle and the driving actions made by the driver, and acquiring driving operation data corresponding to the driving habits;

s40, establishing a mapping relation model between the vehicle distance data and/or the weather data and the driving operation data;

s50, inputting the mapping relation model into a BP neural network model, and training the BP neural network model to obtain a trained BP neural network model;

s60 establishing a connection with the meteorological system.

In this embodiment, Tn is a random time when people are driving; when the vehicle is artificially driven at present, vehicle distance data of the vehicle and obstacles around the vehicle at the moment and weather data at the moment are collected, driving behaviors of a driver under different vehicle distances and weather are obtained, a mapping relation is established, and a pre-established BP neural network model is trained, updated and iterated, so that the automatic driving behavior adjusted according to the data generated by the BP neural network model is closer to the driving habit of the driver.

Referring to fig. 5, specifically, after the step S20 of collecting the distance data and/or the weather data between the vehicle and the obstacle around the vehicle at the time Tn and acquiring the driving action of the driver according to the distance data and the weather data, the method includes the steps of:

s21, the vehicle distance data are transmitted to a cloud server.

Specifically, after the step S30 of analyzing driving habits according to the inter-vehicle distance data and/or weather data of the vehicle and the obstacles around the vehicle and the driving action performed by the driver, and acquiring the driving operation data corresponding to the driving habits, the method includes the steps of:

s31 transmits the driving operation data to the cloud.

Specifically, the step S50 of inputting the mapping relationship model into a BP neural network model, and training the BP neural network model to obtain a trained BP neural network model specifically includes the steps of:

s51, the BP neural network model is trained in the cloud server.

Specifically, before the step S100 of obtaining the vehicle distance data between the vehicle at the current time T and the obstacles around the vehicle, the method further includes the steps of:

s70 connection with the cloud server is established.

In this embodiment, the obtained data are transmitted to the cloud server, and model training, updating and iteration are performed on the cloud server, so that a large amount of data can be saved on one hand, and the operation efficiency can be improved on the other hand.

Referring to fig. 6, specifically, after the step S300 adjusts the automatic parking parameter according to the driving operation data output value corresponding to the vehicle distance data and the weather data, the method includes the steps of:

and S400, automatically driving the vehicle according to the automatic parking parameters.

The invention adopts big data to analyze the parking behavior of the driver, and knows the commonly used speed of the driver when parking, the distance between the driver and the obstacle when braking, the commonly used parking route, the time spent in the parking process and the like, thereby adjusting the parameters of the automatic parking system such as the braking distance, the safety boundary between the driver and the obstacle, the speed when backing a car and the like, and finally providing a system which can solve the problem of parking for the user, simultaneously, the user is not nervous in the parking process and accords with the expected behavior.

Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.