Method for improving automatic driving reliability based on artificial intelligence

Abstract

The invention discloses a method for improving automatic driving reliability based on artificial intelligence, and the method comprises the steps: W1, collecting time sequence data in real time through a sensor installed on a vehicle, and enabling the time sequence data to comprise vehicle condition data and road condition data in a driving process; processing the vehicle condition data and the road condition data to obtain a vehicle dynamic driving environment signal and a corresponding data label; w2, on the basis of the vehicle dynamic driving environment signals and the data labels in the step S1, vehicle electric power data of the driving vehicle on the current road section are obtained, the vehicle speed of the vehicle is corrected on the basis of the vehicle electric power data, and the vehicle driving reliability is improved; the current driving environment of the vehicle is based on the road driving data, the road environment data, the road historical data and the vehicle tire pressure data of the vehicle on the current road, and the current environment, the vehicle condition and the previous road data are combined, so that the driving environment data of the vehicle is more accurate and high in reliability.

Description

Technical Field

[0001] The present invention relates to the technical field of autonomous driving, and particularly relates to a method for improving the reliability of autonomous driving based on artificial intelligence. Background Art

[0002] Intelligent driving refers to the machine assisting people in driving and completely replacing people in driving under special circumstances. As an important part of the intelligent transportation systems developed by various countries, intelligent driving is still in continuous exploration and experimentation. Intelligent driving plays a significant role in the economic and technological development of each country and the enhancement of its comprehensive national strength. Driverless driving is the future development direction of the automotive industry and is of great significance as the core of intelligent driving. Driverless driving refers to the technology of perceiving and judging the surrounding environment during vehicle driving by carrying a variety of sensing devices such as advanced sensors, thereby obtaining vehicle state and surrounding environment information and automatically planning a driving route to control the vehicle to reach the destination.

[0003] For example, patent application No. 202011298591.7 discloses an autonomous driving deviation processing method based on artificial intelligence. The method includes the following steps: Step 1, obtaining vehicle driving data; Step 2, simulating a harsh driving environment; Step 3, identifying lane lines and other non-road objects; Step 4, real-time calibrating vehicle yaw; Step 5, matching vehicle speed and safety distance; Step 6, turning on autonomous driving interruption to handle abnormal driving.

[0004] Based on the artificial intelligence algorithm and the autonomous driving deviation processing method based on artificial intelligence, the present invention improves the YOLOv3 algorithm to realize the detection of driving images and proposes a yaw correction model for automatic vehicle driving. However, this technology does not consider the driving environment of the vehicle during actual use and cannot effectively obtain the driving environment of the vehicle, resulting in the inability to correct the driving speed of the vehicle during autonomous driving and the inability to effectively ensure the driving stability of the vehicle. Secondly, the relationship between vehicle power consumption, vehicle speed and remaining distance is not combined during the autonomous driving correction process of the vehicle, resulting in insufficient reliability during the autonomous driving process of the vehicle. Summary of the Invention

[0005] The object of the present invention is to provide a method for improving the reliability of autonomous driving based on artificial intelligence, which collects timing data in real time through sensors installed on the vehicle. The timing data includes vehicle condition data and road condition data during driving, that is, the current driving environment of the vehicle is obtained through the road condition data, and the current driving speed of the vehicle is limited according to the current driving environment of the vehicle to ensure the driving stability of the vehicle under the current driving environment. By collecting timing data in real time through sensors installed on the vehicle, the timing data includes vehicle condition data and road condition data during driving, that is, the current driving environment of the vehicle is obtained through the road condition data, and the current driving speed of the vehicle is limited according to the current driving environment of the vehicle to ensure the driving stability of the vehicle under the current driving environment.

[0006] The object of the present invention can be achieved by the following technical solutions: A method for improving the reliability of autonomous driving based on artificial intelligence includes the following steps: W1: Collect timing data in real time through sensors installed on the vehicle. The timing data includes vehicle condition data and road condition data during driving, and vehicle dynamic driving environment signals and corresponding data labels are obtained by processing the vehicle condition data and road condition data; W2: Based on the vehicle dynamic driving environment signals and data labels in S1, obtain the vehicle power data of the driving vehicle on the current section, and correct the vehicle speed based on the vehicle power data to improve the driving reliability of the vehicle; The process of obtaining vehicle power data is as follows: Intercept a time interval from the collected timing data. The time interval includes data at multiple moments, and the data at each moment includes data of multiple fields; the data of multiple fields includes the output voltage of the vehicle power battery, the output current of the vehicle power battery, the output voltage of the vehicle drive motor, and the output current of the vehicle drive motor; Obtain the working power loss values of each driving time period of the vehicle and the vehicle speeds corresponding to the working power loss values of each driving time period of the vehicle, denoted as Vcdi, Obtain the current remaining power of the vehicle, denoted as M; Obtain the current remaining travel kilometers of the vehicle, denoted as L; Obtain the power consumption per kilometer of the vehicle, denoted as N; If CD max ≤M, it means that within the speed range corresponding to the current data label, no matter what speed the vehicle travels at, the remaining power of the vehicle can complete the remaining travel kilometers; where Vcd max is the vehicle speed corresponding to the working power loss value CD max corresponding vehicle speed, CDmax To select the moment with the maximum working power loss value in the current vehicle speed dataset, and use the working power loss value at this moment; If CD mid > M, it means that within the speed range corresponding to the current data label, regardless of the vehicle speed, the remaining battery power of the vehicle cannot complete the remaining travel kilometers; where, Vcd mid is the working power loss value CD mid corresponding vehicle speed, CDmid is to select the moment with the minimum working power loss value in the current vehicle speed dataset, and use the working power loss value at this moment; Perform weighted processing on the working power loss value within the remaining travel kilometers and the travel time within the remaining travel kilometers. The weight ratio of the working power loss value within the remaining travel kilometers is n1, and the weight ratio of the travel time within the remaining travel kilometers is n2, where n1 + n2 = 0, and both n1 and n2 are greater than 0; Through the formula Calculate the comprehensive loss value of the vehicle. Arrange the obtained vehicle comprehensive loss values in ascending order, and obtain the working power loss value corresponding to the minimum vehicle loss value and the vehicle speed corresponding to the working power loss value, so that the vehicle maintains this speed and travels on the current road. Where, CDi is the working power loss value of the vehicle during each driving period.

[0007] As a further solution of the present invention: In W1, the road condition data includes road driving data, road environment data, road historical data, and vehicle tire pressure data.

[0008] As a further solution of the present invention: The process of obtaining road driving data is as follows: Obtain the position information of the vehicle on the current road, and obtain the vehicle identification information of the vehicles traveling in the same direction as the vehicle on the current road. Through the vehicle identification information, obtain the vehicle types of the vehicles traveling in the same direction on the current road, and classify the vehicle types into large vehicles, medium vehicles, and small vehicles; Mark the number of all large vehicles as Sd; Mark the number of all medium vehicles as Sz; Mark the number of all small vehicles as Sx; Through the formula Calculate the road driving data DX, where d1, d2, and d3 are preset proportionality coefficients.

[0009] As a further solution of the present invention: Determine the number of vehicles on the current road according to the obtained vehicle types, and classify all vehicle numbers according to vehicle types. Where, the vehicle length is positioned as X; When X < 4.8 meters, the vehicle is a small vehicle; When 4.8 ≤ X < 8 meters, the vehicle is a medium-sized vehicle; When X ≥ 8 meters, the vehicle is a large-sized vehicle.

[0010] As a further solution of the present invention: The process of obtaining road environment data is as follows: Obtain the position information of the vehicle on the current road and obtain the environmental information of the current road. The environmental information of the current road includes the wind direction monitoring data, visibility data, and rainfall rate data of the current road; Mark the wind direction monitoring data of the current road as Wt; Mark the visibility data of the current road as Jt; Mark the rainfall rate data of the current road as Yt; According to the formula Calculate to obtain the road environment data , where a1, a2, and a3 are preset proportionality coefficients.

[0011] As a further solution of the present invention: The process of obtaining road historical data is as follows: Obtain the position information of the vehicle on the current road and obtain the historical information of the current road. The historical information of the current road includes the accident rate, number of repairs, and number of road bends of the current road; Mark the accident rate of the current road as J1; Mark the number of repairs of the current road as J2; Mark the number of road bends of the current road as J3; Through the formula Obtain the road historical data DL of the current road, where , k are preset proportionality coefficients.

[0012] As a further solution of the present invention: The process of obtaining vehicle tire pressure data is as follows: Obtain the temperature data of the tire and mark it as Ti; Obtain the speed data of the tire and mark it as Si; Perform weighted processing on the obtained temperature data and speed data of the tire. The weight ratio of the obtained temperature data Ti of the tire is assigned as ; The weight ratio of the obtained speed data Si of the tire is assigned as ; where k1 + k2 = 1, k1 > k1 > 0; According to the formula Byi = (Ti * + Si * ) * Bc to obtain the theoretical value Byi of the tire pressure of a single tire. i is the tire number, and Bc is the initial value of the tire pressure; Obtain the standard deviation Bycz of the four tires according to the theoretical values of the tire pressures of multiple tires of the vehicle.

[0013] As a further solution of the present invention: Denote the road driving data as DX, the road environment data as DH, and the road historical data as DL, and process them to obtain the road condition data base; According to the formula calculate to obtain the road condition data base, where b1, b2, b3, and b4 are preset proportionality coefficients, is the correction coefficient; Obtain the vehicle dynamic driving parameters through the formula where is the preset proportionality coefficient.

[0014] As a further solution of the present invention: Preset the limits of the vehicle dynamic driving parameter thresholds as D1 and D2, where D1 < D2: When D < D1, the vehicle dynamic driving environment is poor, and the data label 0 is obtained; When D1 < D < D2, the vehicle dynamic driving environment is good, and the data label 1 is obtained; When D > D2, the vehicle dynamic driving environment is excellent, and the data label 2 is obtained.

[0015] As a further solution of the present invention: When the data label corresponding to the obtained vehicle dynamic driving environment signal is 0, the corresponding vehicle driving speed does not exceed 50 Km / h; Define that when the data label corresponding to the obtained vehicle dynamic driving environment signal is 1, the corresponding vehicle driving speed does not exceed 80 Km / h; Define that when the data label corresponding to the obtained vehicle dynamic driving environment signal is 2, the corresponding vehicle driving speed does not exceed 100 Km / h.

[0016] The beneficial effects of the present invention: (1) The present invention is to collect real-time sequential data through sensors installed on the vehicle. The sequential data includes vehicle condition data and road condition data during driving, that is, obtain the current driving environment of the vehicle through the road condition data, and limit the current driving speed of the vehicle according to the current driving environment of the vehicle to ensure the driving stability of the vehicle under the current driving environment; Among them, the current driving environment of the vehicle is based on the road driving data, road environment data, road historical data, and vehicle tire pressure data of the vehicle on the current road, combined with the current environment, vehicle condition, and previous road data, making the driving environment data of the vehicle more accurate and highly reliable; (2)Based on the vehicle dynamic driving environment signal, the present invention obtains the vehicle power data of the running vehicle on the current section, that is, by processing the output voltage of the vehicle power battery, the output current of the vehicle power battery, the output voltage of the vehicle drive motor, and the output current of the vehicle drive motor, the working power loss value of the vehicle is obtained, and based on the working power loss value, combined with the current remaining power of the vehicle, the current remaining mileage of the vehicle, and the power consumption per kilometer of the vehicle, the vehicle driving speed is set to ensure that the vehicle completes the remaining journey at the set speed and with low energy consumption, improving the reliability of vehicle driving. Brief Description of the Drawings

[0017] The present invention will be further described below with reference to the drawings; Figure 1 is a flowchart of the present invention. Detailed Embodiment

[0018] 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. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Please refer to Figure 1 As shown, the present invention is a method for improving the reliability of autonomous driving based on artificial intelligence, including the following steps: W1: Real-time collect time-series data through sensors installed on the vehicle. The time-series data includes vehicle condition data and road condition data during driving, and obtain the vehicle dynamic driving environment signal by processing the vehicle condition data and road condition data; The vehicle dynamic driving environment signal includes poor vehicle dynamic driving environment, good vehicle dynamic driving environment, and excellent vehicle dynamic driving environment; Among them, The vehicle condition data includes vehicle power data; The road condition data includes road driving data, road environment data, road historical data, and vehicle tire pressure data; The process of obtaining road driving data is as follows: Obtain the position information of the vehicle on the current road, and obtain the vehicle identification information of the vehicles in the same driving direction as the vehicle on the current road. Obtain the vehicle types of the vehicles in the same driving direction on the current road through the vehicle identification information, and classify the vehicle types into large vehicles, medium-sized vehicles, and small vehicles; Determine the number of vehicles on the current road according to the obtained vehicle types, classify all the vehicle numbers according to the vehicle types, and among them, locate the vehicle length as X; When X < 4.8 meters, the vehicle is a small vehicle; When 4.8 ≤ X < 8 meters, the vehicle is a medium-sized vehicle; When X ≥ 8 meters, the vehicle is a large-sized vehicle; Mark the number of all large-sized vehicles as Sd; Mark the number of all medium-sized vehicles as Sz; Mark the number of all small-sized vehicles as Sx; Through the formula Calculate the road driving data DX, where d1, d2, and d3 are preset proportionality coefficients; From the formula for obtaining the road driving data above, the more large-sized, medium-sized, and small-sized vehicles there are in the same driving direction on the vehicle's current road, the larger the obtained road driving data, which means the more congested the vehicle's current driving road is and the worse the vehicle's driving state is.

[0020] The process of obtaining road environment data is as follows: Obtain the position information of the vehicle on the current road and obtain the environmental information of the current road. The environmental information of the current road includes the wind direction monitoring data, visibility data, and rainfall rate data of the current road; Mark the wind direction monitoring data of the current road as Wt; Mark the visibility data of the current road as Jt; Mark the rainfall rate data of the current road as Yt; According to the formula Calculate the road environment data DH, where a1, a2, and a3 are preset proportionality coefficients; From the formula for obtaining the road environment data above, the greater the wind force and the rainfall rate on the current road, the greater the road environment data indicates, and the greater the road environment data, the worse the driving environment of the current road; The greater the visibility of the current road, the smaller the road environment data indicates, and the better the current road environment data; Among them, the rainfall rate data is obtained by sampling with a rain sensor and a volume sensor. Specifically, when the rain sensor detects rainfall, the volume sensor takes a preset time t1 as a detection unit, and the time interval between adjacent two detection units t1 is not greater than the preset time t2. The ambient volume is continuously detected multiple times within t1 time, and the time interval between adjacent two detections is the same, so as to obtain a set of ambient volume information Fr1, Fr2,..., Frf within t1 time. According to the formula Calculate the standard deviation S2 of this set of data, where Frp is the average value of Fr1, Fr2, ..., Frf. When S2 is greater than or equal to the preset value S, delete the maximum and / or minimum values in this set of data, and recalculate the standard deviation S2 of this set of data until S2 is less than the preset value S. Calculate the average value of the remaining data in this set of data, that is, obtain the rainfall rate data Yt of the current road; The process of obtaining road historical data is as follows: Obtain the position information of the vehicle on the current road, and obtain the historical information of the current road. The historical information of the current road includes the accident rate, the number of repairs, and the number of road bends of the current road; Mark the accident rate of the current road as J1; Mark the number of repairs of the current road as J2; Mark the number of road bends of the current road as J3; Through the formula Obtain the road historical data DL of the current road, where , k is a preset proportionality coefficient; It can be seen from the above formula for obtaining road historical data that the higher the accident rate, the more repair times, and the higher the number of road bends of the current road, the greater the obtained road historical data, which means that the current road has greater danger in the past driving process.

[0021] Process the road driving data, road environment data, and road historical data to obtain the road condition data base; Specifically, according to the formula Calculate the road condition data base, where b1, b2, b3, and b4 are preset proportionality coefficients, is a correction coefficient.

[0022] The process of obtaining vehicle tire pressure data is as follows: Obtain the temperature data of the tire and mark it as Ti; Obtain the speed data of the tire and mark it as Si; Perform weighted processing on the obtained temperature data and speed data of the tire. The weight ratio of the obtained temperature data Ti of the tire is assigned as ; The weight ratio of the obtained speed data Si of the tire is assigned as; where, k1 + k2 = 1, k1 > k1 > 0; According to the formula Byi = (Ti * + Si * ) * Bc to obtain the theoretical value Byi of the tire pressure of a single tire, i is the tire number, and Bc is the initial value of the tire pressure (that is, the tire pressure value at normal temperature and when not started); Mark the theoretical tire pressure values of the four tires of the vehicle as By1, By2, By3, and By4 respectively, so as to obtain the standard deviation value Bycz of the four tires; Through the formula Obtain the vehicle dynamic driving parameters, where is a preset proportionality coefficient; Preset the limits of the vehicle dynamic driving parameter threshold as D1 and D2, where D1 < D2: When D < D1, the vehicle dynamic driving environment is poor, and the data label 0 is obtained; When D1 < D < D2, the vehicle dynamic driving environment is good, and the data label 1 is obtained; When D > D2, the vehicle dynamic driving environment is excellent, and the data label 2 is obtained; W2: Based on the vehicle dynamic driving environment signal in S1, obtain the vehicle power data of the driving vehicle on the current section, and correct the vehicle speed based on the vehicle power data to improve the vehicle driving reliability.

[0023] Among them, it is defined that when the data label corresponding to the obtained vehicle dynamic driving environment signal is 0, the corresponding vehicle driving speed does not exceed 50 Km / h; It is defined that when the data label corresponding to the obtained vehicle dynamic driving environment signal is 1, the corresponding vehicle driving speed does not exceed 80 Km / h; It is defined that when the data label corresponding to the obtained vehicle dynamic driving environment signal is 2, the corresponding vehicle driving speed does not exceed 100 Km / h; The process of obtaining the vehicle power data is as follows: Intercept the time interval from the collected time-series data. The time interval includes data at multiple moments, and the data at each moment includes data in multiple fields; the output voltage of the vehicle power battery, the output current of the vehicle power battery, the output voltage of the vehicle drive motor, and the output current of the vehicle drive motor; Obtain the output voltage Vc and output current Ci of the vehicle power battery, multiply the output voltage Vc by the output current Ci and integrate over time to obtain the working electric energy of the vehicle power battery in each time period, and mark it as CEi; Obtain the input current Di and working voltage Vd of the vehicle drive motor, multiply the input current by the working voltage and integrate over time to obtain the working electric energy of the vehicle drive motor in each time period, and mark it as DEi; Subtract the working electric energy of the vehicle power battery in each time period from the working electric energy of the vehicle drive motor in each time period and integrate over time to obtain the working electric energy loss value of the vehicle in each time period of driving. Mark the working electric energy loss value of the vehicle in each time period of driving as CDi; Obtain the vehicle speed corresponding to the working power loss value in each time period of vehicle driving, and record the vehicle speed corresponding to the working power loss value in each time period of vehicle driving as Vcdi; Then, obtain the data set of the current vehicle speed (Vcd1, Vcdi) according to the data label corresponding to the vehicle dynamic driving environment, select the moment when the working power loss value in the current vehicle speed data set is the smallest, and record the working power loss value at this moment as CD mid , and record the working power loss value CD mid The corresponding vehicle speed is recorded as Vcd mid ; Select the moment when the working power loss value in the current vehicle speed data set is the largest, and record the working power loss value at this moment as CD max , and record the working power loss value CD max The corresponding vehicle speed is recorded as Vcd max ; Obtain the current remaining battery power of the vehicle, and record the current remaining battery power of the vehicle as M; Obtain the current remaining driving kilometers of the vehicle, and record the current remaining driving kilometers of the vehicle as L; Obtain the power consumption per kilometer of the vehicle, and record the power consumption per kilometer of the vehicle as N; If CD max ≤M, it means that within the speed range corresponding to the current data label, regardless of the vehicle speed, the remaining battery power of the vehicle can complete the remaining driving kilometers; Based on this, within the speed range corresponding to the current data label, it is possible to select to drive at the vehicle speed Vcd max corresponding to the working power loss value, so as to minimize the time to complete the current remaining driving kilometers of the vehicle; Or within the speed range corresponding to the current data label, it is possible to select to drive at the vehicle speed Vcd max corresponding to the working power loss value, so as to minimize the power loss for completing the current remaining driving kilometers of the vehicle; If CD mid >M, it means that within the speed range corresponding to the current data label, regardless of the vehicle speed, the remaining battery power of the vehicle cannot complete the remaining driving kilometers; Based on this, perform weighted processing on the working power loss value within the remaining driving kilometers and the driving time within the remaining driving kilometers. Let the weight ratio of the working power loss value within the remaining driving kilometers be n1, and the weight ratio of the driving time within the remaining driving kilometers be n2, where n1 + n2 = 0, and both n1 and n2 are greater than 0; Through the formula Calculate the comprehensive loss value of the vehicle, arrange the obtained comprehensive loss values of the vehicle in ascending order, obtain the working power loss value corresponding to the minimum vehicle loss value and the vehicle speed corresponding to the working power loss value, and make the vehicle travel at this vehicle speed on the current road.

[0024] The above has described an embodiment of the present invention in detail, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the application of the present invention should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for improving the reliability of autonomous driving based on artificial intelligence, characterized in that, It includes the following steps: W1: Real-time collect sequential data through sensors installed on the vehicle. The sequential data includes vehicle condition data and road condition data during driving. Obtain the vehicle dynamic driving environment signal and the corresponding data label by processing the vehicle condition data and the road condition data; W2: Based on the vehicle dynamic driving environment signal and data label in S1, obtain the vehicle power data of the driving vehicle on the current section, and correct the vehicle speed based on the vehicle power data to improve the vehicle driving reliability; The process of obtaining the vehicle power data is as follows: Intercept a time interval from the collected sequential data. The time interval includes data at multiple moments, and the data at each moment includes data of multiple fields; The data of multiple fields includes the output voltage of the vehicle power battery, the output current of the vehicle power battery, the output voltage of the vehicle drive motor, and the output current of the vehicle drive motor; Obtain the working power loss value of each driving time period of the vehicle from the data of multiple fields, and record the vehicle speed corresponding to the working power loss value of each driving time period of the vehicle as Vcdi; Obtain the current remaining battery power of the vehicle, and record the current remaining battery power of the vehicle as M; Obtain the current remaining driving mileage of the vehicle, and record the current remaining driving mileage of the vehicle as L; Obtain the power consumption per kilometer of the vehicle, and record the power consumption per kilometer of the vehicle as N; If CD max ≤ M, it means that within the speed range corresponding to the current data label, regardless of the speed at which the vehicle travels, the remaining battery power of the vehicle can complete the remaining travel kilometers; Among them, Vcd max is the working power loss value CD max The corresponding vehicle speed, CD max is to select the moment with the largest working power loss value in the current vehicle speed data set, and use the working power loss value at this moment; If CD mid > M, it means that within the speed range corresponding to the current data label, regardless of the vehicle speed, the remaining battery power of the vehicle cannot complete the remaining mileage; where Vcd mid is the working power loss value CD mid corresponding to the vehicle speed, CDmid is the moment when the working power loss value is the smallest in the selected current vehicle speed data set, and the working power loss value at this moment; Perform weighted processing on the working power loss value within the remaining driving mileage and the driving time within the remaining driving mileage. The weight ratio of the working power loss value within the remaining driving mileage is n1, and the weight ratio of the driving time within the remaining driving mileage is n2, where n1 + n2 = 0, and both n1 and n2 are greater than 0; The comprehensive loss value of the vehicle is calculated through the formula Arrange the obtained comprehensive loss values of the vehicle in ascending order, obtain the working power loss value corresponding to the minimum vehicle loss value and the vehicle speed corresponding to the working power loss value, and make the vehicle maintain this vehicle speed to drive on the current road, where CDi is the working power loss value of the vehicle during each driving time period.

2. The method for improving the reliability of autonomous driving based on artificial intelligence according to claim 1, characterized in that, In W1, the road condition data includes road driving data, road environment data, road historical data, and vehicle tire pressure data.

3. The method for improving the reliability of autonomous driving based on artificial intelligence according to claim 2, wherein The process of obtaining the road driving data is as follows: Obtain the position information of the vehicle on the current road, and obtain the vehicle identification information of the vehicles in the same driving direction on the current road. Obtain the vehicle types of the vehicles in the same driving direction on the current road through the vehicle identification information, and classify the vehicle types into large vehicles, medium vehicles, and small vehicles; Mark the number of all large vehicles as Sd; Mark the number of all medium vehicles as Sz; Mark the number of all small vehicles as Sx; The road driving data DX is calculated through the formula where d1, d2 and d3 are preset proportionality coefficients.

4. A method for improving the reliability of autonomous driving based on artificial intelligence according to claim 3, characterized in that, Determine the number of vehicles on the current road according to the obtained vehicle types, and classify all vehicle numbers according to vehicle types, where the vehicle length is positioned as X; When X < 4.8 meters, the vehicle is a small vehicle; When 4.8 ≤ X < 8 meters, the vehicle is a medium vehicle; When X ≥ 8 meters, the vehicle is a large vehicle.

5. A method for improving the reliability of autonomous driving based on artificial intelligence according to claim 2, characterized in that, The process of obtaining the road environment data is as follows: Obtain the position information of the vehicle on the current road, and obtain the environmental information of the current road. The environmental information of the current road includes the wind direction monitoring data, visibility data, and rainfall rate data of the current road; Mark the wind direction monitoring data of the current road as Wt; Mark the visibility data of the current road as Jt; Mark the rainfall rate data of the current road as Yt; According to the formula the road environment data is calculated , where a1, a2, and a3 are preset proportionality coefficients.

6. The method for improving the reliability of autonomous driving based on artificial intelligence according to claim 2, characterized in that The process of obtaining the road historical data is as follows: Obtain the position information of the vehicle on the current road, and obtain the historical information of the current road. The historical information of the current road includes the accident rate, the number of repairs, and the number of road bends of the current road; Mark the accident rate of the current road as J1; Mark the number of repairs of the current road as J2; Mark the number of road bends of the current road as J3; Obtain the road historical data DL of the current road through the formula where , and k is a preset proportionality coefficient.

7. A method for improving the reliability of autonomous driving based on artificial intelligence according to claim 2, characterized in that, The process of obtaining the vehicle tire pressure data is as follows: Obtain the temperature data of the tire and mark it as Ti; Obtain the speed data of the tire and mark it as Si; Perform weighted processing on the obtained temperature data and speed data of the tire, and allocate the weight proportion of the obtained temperature data Ti of the tire as ; allocate the weight proportion of the obtained speed data Si of the tire as ; where k1 + k2 = 1, and k1 > k1 > 0; According to the formula Byi = (Ti * + Si * ) * Bc, the theoretical value Byi of the tire pressure of a single tire is obtained, where i is the tire number and Bc is the initial value of the tire pressure; Obtain the standard deviation Bycz of the four tires according to the theoretical tire pressure values of multiple vehicle tires.

8. A method for improving the reliability of autonomous driving based on artificial intelligence according to claim 7, characterized in that, Record the road driving data as DX, the road environment data as DH, and the road historical data as DL, and process them to obtain the road condition data base; According to the formula calculate the base of the road condition data, where b1, b2, b3, and b4 are preset proportionality coefficients, is the correction coefficient; Obtain vehicle dynamic driving parameters through the formula , where is a preset proportionality coefficient.

9. The method for improving the reliability of autonomous driving based on artificial intelligence according to claim 8, characterized in that, Preset the limits of the vehicle dynamic driving parameter thresholds as D1 and D2, where D1 < D2: When D < D1, the vehicle dynamic driving environment is poor, and the data label 0 is obtained; When D1 < D < D2, the vehicle dynamic driving environment is good, and the data label 1 is obtained; When D > D2, the vehicle dynamic driving environment is excellent, and the data label 2 is obtained.

10. A method for improving the reliability of autonomous driving based on artificial intelligence according to claim 9, characterized in that When the data label corresponding to the obtained vehicle dynamic driving environment signal is 0, the corresponding vehicle driving speed does not exceed 50 Km / h; Define that when the data label corresponding to the obtained vehicle dynamic driving environment signal is 1, the corresponding vehicle driving speed does not exceed 80 Km / h; Define that when the data label corresponding to the obtained vehicle dynamic driving environment signal is 2, the corresponding vehicle driving speed does not exceed 100 Km / h.

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