Intelligent driving management system and method based on traffic control

Abstract

The invention relates to the technical field of traffic control and intelligent driving, in particular to an intelligent driving management system and method based on traffic control, and the system comprises a data collection module which is used for collecting vehicle driving information and traffic environment information in real time; the intelligent driving strategy module is used for outputting the control instruction, optimizing the output of the control instruction, intelligently controlling the lane change decision, judging the normal condition of the driving state and outputting according to the judgment result; the air conditioner adjusting module is used for intelligently adjusting a vehicle air conditioner; and the decision execution feedback module is used for updating the control instruction and is also used for carrying out feedback regulation on the process of optimizing the membership function in the air conditioner regulation module. Differentiation control strategies can be generated in real time according to different users, intelligent control over the interior of the vehicle is achieved, vehicle energy consumption can be reduced, and driving safety, efficiency and comfort are improved.

Description

Technical Field

[0001] The present invention relates to the field of traffic control and intelligent driving technology, and in particular to an intelligent driving management system and method based on traffic control. Background Art

[0002] Traditional intelligent driving algorithms find it difficult to accurately adjust vehicle speed, braking and steering strategies in real time, which can easily lead to traffic accidents. At the same time, the air-conditioning adjustment in the car is seriously disconnected from the traffic conditions. In bad weather, traffic congestion is more common, and vehicles stop and go. Traditional air-conditioning systems cannot be intelligently adjusted according to such changes in traffic conditions and the special impact of bad weather on the vehicle environment. In addition, new energy vehicles consume more energy in bad weather, and traditional air-conditioning adjustment methods do not combine traffic control information for energy optimization, further exacerbating the pressure on vehicle endurance.

[0003] Chinese Patent Publication No. CN118675317A discloses an intelligent traffic control system, method, device, and medium related to intelligent driving technology. The system includes: a roadside monitoring sensor for acquiring road scene data within a preset range of its location; a roadside edge server for analyzing and processing the received road scene data to determine driving strategies and traffic flow scheduling strategies, and transmitting the driving strategies to the corresponding vehicle-side signal receiving controller and the traffic flow scheduling strategies to the traffic scheduling control terminal; the vehicle-side signal receiving controller for controlling vehicle driving based on the received driving strategies; and the traffic scheduling control terminal for scheduling vehicles on the road based on the received traffic flow scheduling strategies. However, this solution struggles to generate differentiated control strategies for different users in real time, and it struggles to intelligently control the vehicle interior, reduce vehicle energy consumption, or improve driving safety, efficiency, and comfort. Summary of the Invention

[0004] To this end, the present invention provides an intelligent driving management system and method based on traffic control to overcome the problems in the prior art of difficulty in generating differentiated control strategies for different users in real time, difficulty in intelligently controlling the interior of the vehicle, difficulty in reducing vehicle energy consumption, and difficulty in improving driving safety, efficiency and comfort.

[0005] To achieve the above objectives, the present invention provides an intelligent driving management system based on traffic control, the system comprising: Data acquisition module, used to collect vehicle driving information and traffic environment information in real time; An intelligent driving strategy module, which is used to output control instructions based on vehicle driving information and traffic environment information, optimize the output of control instructions based on vehicle driving information and traffic environment information, intelligently control lane change decisions through a lane change decision algorithm, and determine the normal driving state based on vehicle driving information and traffic environment information, and output the judgment results; Air conditioning adjustment module, used to intelligently adjust the vehicle air conditioning according to vehicle driving information, traffic environment information and fuzzy algorithm; The decision execution feedback module is used to update the control instructions according to the vehicle driving information and traffic environment information, and is also used to provide feedback and adjustment to the process of optimizing the membership function in the air conditioning adjustment module according to the vehicle driving information and traffic environment information.

[0006] Furthermore, the intelligent driving strategy module compares the front vehicle distance Dc and the rear vehicle distance Dh with the preset front-to-rear safety distance D01, and compares the left vehicle distance Dz and the right vehicle distance Dy with the preset left-right safety distance D02, and judges whether the vehicle safety distance in each direction meets the standard based on the comparison results, and outputs a control command based on the judgment results; Furthermore, the intelligent driving strategy module also compares the front and rear vehicle relative speed B1 with the preset front and rear relative speed B01, and the left and right vehicle relative speed B2 with the preset left and right relative speed B02, and judges the validity of the preset front and rear safety distance D01 and the preset left and right safety distance D02 according to the comparison results, and adjusts the preset front and rear safety distance D01 and the preset left and right safety distance D02 according to the judgment results; The intelligent driving strategy module also compares the front vehicle relative angle C1 with the preset front vehicle relative angle C01, the rear vehicle relative angle C2 with the preset rear vehicle relative angle C02, the side vehicle relative angle C3 with the preset side vehicle relative angle C03, and the overtaking vehicle relative angle C4 with the preset overtaking vehicle relative angle C04, and judges the validity of the front and rear vehicle relative speed B1 and the left and right vehicle relative speed B2 according to the comparison results, and corrects the front and rear vehicle relative speed B1 and the left and right vehicle relative speed B2 according to the judgment results.

[0007] Furthermore, the intelligent driving strategy module further calculates the front vehicle time interval T1, the rear vehicle time interval T2, the left vehicle time interval T3, and the right vehicle time interval T4 based on the front vehicle distance Dc, the rear vehicle distance Dh, the left vehicle distance Dz, and the right vehicle distance Dy, the front and rear vehicle relative speed B1, and the left and right vehicle relative speed B2, where: , , , ; The intelligent driving strategy module compares the front vehicle time interval T1, the rear vehicle time interval T2, the left time interval T3 and the right time interval T4 with the preset front vehicle time interval T01, the preset rear vehicle time interval T02, the preset left time interval T03 and the preset right time interval T04 respectively, and judges the high and low situations of the front vehicle time interval T1, the rear vehicle time interval T2, the left time interval T3 and the right time interval T4 according to the comparison results, and optimizes the control instructions according to the judgment results.

[0008] Furthermore, the intelligent driving strategy module also calculates the driving state value G according to the driver's fatigue state value E and the attention concentration value F, wherein: G=0.5×(E+F); The intelligent driving strategy module compares the driving state value G with the preset driving state value G0, judges the normal situation of the driving state based on the comparison result, and corrects the preset front and rear safety distance D01, the preset left and right safety distance D02 and the original speed v1 based on the judgment result.

[0009] Furthermore, the intelligent driving strategy module also compares the number of times the driver has driven the vehicle H with the preset number of times the driver has driven the vehicle H0, judges the driver's familiarity with the vehicle based on the comparison result, and adjusts the preset driving state value G0 based on the judgment result.

[0010] Furthermore, the intelligent driving strategy module also compares the weather condition value Q with the preset weather condition value Q0, judges whether the vehicle driving environment conditions meet the standards based on the comparison results, and modifies the driver's familiarity with the vehicle based on the judgment results.

[0011] Furthermore, the decision execution feedback module compares the number of sudden braking times J1 of the vehicle with the preset number of sudden braking times J01 of the vehicle, judges the rationality of the control instruction according to the comparison result, and updates the control instruction according to the judgment result.

[0012] Furthermore, the decision execution feedback module also compares the number of times the vehicle occupants adjust the air conditioning J2 with the preset number of times the vehicle occupants adjust the air conditioning J02, judges the effectiveness of the optimized membership function based on the comparison results, and performs feedback adjustment on the process of optimizing the membership function based on the judgment results.

[0013] On the other hand, the present invention also provides an intelligent driving management method based on traffic control, the method comprising: Step S1, real-time collection of vehicle driving information and traffic environment information; Step S2, outputting control instructions based on vehicle driving information and traffic environment information; Step S3, optimizing the output of the control command according to the vehicle driving information and traffic environment information; Step S4, intelligently controlling the lane change decision through a lane change decision algorithm; Step S5, judging the normality of the driving state based on the vehicle driving information and the traffic environment information, and outputting the judgment result; Step S6, intelligently adjusting the vehicle air conditioner according to the vehicle driving information, traffic environment information and fuzzy algorithm; Step S7, updating the control instructions according to the vehicle driving information and traffic environment information; Step S8: Feedback adjustment is performed on the process of optimizing the membership function in the air conditioning adjustment module according to the vehicle driving information and the traffic environment information.

[0014] Compared with the existing technology, the beneficial effect of the present invention is that the system collects vehicle driving information and traffic environment information in real time through the data acquisition module, provides comprehensive and accurate information support, ensures the timeliness of data acquisition, enables the system to quickly respond to changes in the road environment, and improves driving safety and efficiency. The system analyzes and processes the collected data through the intelligent driving strategy module, outputs optimized control instructions, effectively avoids safety accidents such as collisions and rear-end collisions, improves driving safety, and ensures stable operation of the system in different environments. The system uses a fuzzy algorithm through the air-conditioning adjustment module to automatically adjust the air-conditioning operation according to the in-vehicle environment data, providing a comfortable driving environment, and adjusts the air-conditioning power according to real-time needs to reduce vehicle energy consumption. The system updates the control instructions through the decision-making execution feedback module, and performs feedback adjustment on the optimization process of the air-conditioning module, timely discovers and corrects deviations and faults in the system operation, improves the overall reliability of the system, ensures driving safety and the stability and efficiency of the overall operation of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the structure of the intelligent driving management system based on traffic control in this embodiment; Figure 2 Schematic diagram of the flow of the intelligent driving management method based on traffic control in this embodiment. DETAILED DESCRIPTION

[0016] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0017] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0018] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0019] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0020] See also Figure 1 As shown in FIG, it is a schematic diagram of the structure of the intelligent driving management system based on traffic control in this embodiment, and the system includes: Data acquisition module, used to collect vehicle driving information and traffic environment information in real time; an intelligent driving strategy module, configured to output control instructions based on vehicle driving information and traffic environment information, optimize the output of control instructions based on the vehicle driving information and traffic environment information, intelligently control lane change decisions through a lane change decision algorithm, determine the normality of the driving state based on the vehicle driving information and traffic environment information, and output the results of the determination. The intelligent driving strategy module is connected to the data acquisition module; The air conditioning adjustment module is used to intelligently adjust the vehicle air conditioning according to the vehicle driving information, traffic environment information and fuzzy algorithm. The air conditioning adjustment module is connected to the data acquisition module; The decision execution feedback module is used to update the control instructions based on the vehicle driving information and traffic environment information, and is also used to provide feedback and adjustment to the process of optimizing the membership function in the air conditioning adjustment module based on the vehicle driving information and traffic environment information. The decision execution feedback module is connected to the data acquisition module, the intelligent driving strategy module and the air conditioning adjustment module.

[0021] Specifically, the system is applied to the vehicle-mounted control terminal. The system obtains vehicle driving information and traffic environment information in real time through the data acquisition module, and outputs control instructions based on the vehicle driving information and traffic environment information through the intelligent driving strategy module, optimizes driving behavior, processes lane change decisions, and judges in real time whether the driving status is normal. The air-conditioning adjustment module also uses a fuzzy algorithm to automatically adjust the air-conditioning operation according to the in-vehicle environment to provide a comfortable driving environment. Finally, the decision-making execution feedback module serves as the closed-loop control core, which is responsible for updating the control instructions and performing feedback adjustment on the optimization process of the air-conditioning module to ensure the overall stable and efficient operation of the system, thereby improving driving safety, efficiency and comfort. In particular, the system collects vehicle driving information and traffic environment information in real time through the data acquisition module, and provides comprehensive and accurate information Support, ensure the timeliness of data collection, enable the system to quickly respond to changes in the road environment, and improve driving safety and efficiency. The system analyzes and processes the collected data through the intelligent driving strategy module, and outputs optimized control instructions to effectively avoid safety accidents such as collisions and rear-end collisions, improve driving safety, and ensure the stable operation of the system in different environments. The system uses fuzzy algorithms through the air-conditioning adjustment module to automatically adjust the air-conditioning operation according to the in-vehicle environmental data to provide a comfortable driving environment. At the same time, it adjusts the air-conditioning power according to real-time needs to reduce vehicle energy consumption. The system updates the control instructions through the decision-making execution feedback module, and provides feedback adjustment on the optimization process of the air-conditioning module, so as to timely discover and correct deviations and faults in the system operation, improve the overall reliability of the system, ensure driving safety and the stability and efficiency of the overall operation of the system.

[0022] Specifically, when the data acquisition module collects vehicle driving information and traffic environment information in real time, the vehicle driving information includes: the driver's fatigue state value E, the attention concentration value F, the number of times the driver drives the vehicle H, the vehicle's remaining power Y, the number of times the vehicle suddenly brakes J1, and the number of times the occupants adjust the air conditioner J2; the traffic environment information includes: the distance to the vehicle in front Dc, the distance to the vehicle behind Dh, the distance to the vehicle on the left Dz, the distance to the vehicle on the right Dy, the relative speed B1 of the front and rear vehicles, the relative speed B2 of the left and right vehicles, the relative angle C1 of the vehicle in front, the relative angle C2 of the vehicle behind, the relative angle C3 of the vehicle on the side, the relative angle C4 of the overtaking vehicle, and the weather condition value Q.

[0023] Specifically, the driver fatigue state value E refers to an indicator that quantifies the driver's current fatigue level, the attention concentration value F refers to a quantitative indicator reflecting the driver's concentration on the driving task, the number of times the driver has driven the vehicle H refers to the total number of times a specific driver has driven the current vehicle, the vehicle's remaining power Y refers to the percentage of power currently remaining in the vehicle's battery pack, the number of sudden braking times J1 of the vehicle refers to the number of times the vehicle has suddenly braked within a certain period of time, the number of times the occupant adjusted the air conditioning J2 refers to the number of times the occupant manually adjusted the air conditioning settings, the front vehicle distance Dc refers to the straight-line distance between the vehicle and the vehicle in front, the rear vehicle distance Dh refers to the straight-line distance between the vehicle and the vehicle behind, the left vehicle distance Dz refers to the lateral distance between the vehicle and the vehicle in the left adjacent lane, the right vehicle distance Dy refers to the lateral distance between the vehicle and the vehicle in the right adjacent lane, and the front and rear vehicle relative speed B1 refers to the distance between the vehicle and the front vehicle. The relative speed difference between the rear vehicles, the left and right vehicle relative speed B2 refers to the relative speed difference between the vehicle and the vehicles in the left and right adjacent lanes, the front vehicle relative angle C1 refers to the relative angle between the vehicle and the front vehicle, the rear vehicle relative angle C2 refers to the relative angle between the vehicle and the rear vehicle, the side vehicle relative angle C3 refers to the relative angle between the vehicle and the vehicle in the side adjacent lane, the overtaking vehicle relative angle C4 refers to the relative angle between the vehicle and the vehicle being overtaken, the weather condition value Q refers to the value used to quantify the degree of impact of current weather conditions on driving safety. This embodiment does not limit the real-time collection method of vehicle driving information and traffic environment information. Relevant technical personnel in this field can freely set it according to actual needs, and only need to meet the need for real-time collection of vehicle driving information and traffic environment information. For example, it can be set to collect traffic environment information in real time through on-board millimeter wave radar and collect vehicle driving information in real time through vehicle recorder.

[0024] Specifically, the data acquisition module collects vehicle driving information and traffic environment information in real time, provides comprehensive and accurate information support, ensures the timeliness of data acquisition, enables the system to quickly respond to changes in the road environment, and improves driving safety and efficiency.

[0025] Specifically, the intelligent driving strategy module compares the front vehicle distance Dc and the rear vehicle distance Dh with the preset front-to-rear safety distance D01, and compares the left vehicle distance Dz and the right vehicle distance Dy with the preset left-right safety distance D02, and judges whether the vehicle safety distance in each direction meets the standard based on the comparison results, and outputs control instructions based on the judgment results, where: When Dc≥D01, it is determined that the safety distance to the vehicle ahead meets the standard and no control command is output; When Dc<D01, it is determined that the safety distance to the vehicle ahead does not meet the standard, and the deceleration command is output as the control command; When Dh≥D01, it is determined that the safety distance to the rear vehicle meets the standard and no control command is output; When Dh<D01, it is determined that the safety distance to the rear vehicle does not meet the standard, and the acceleration command is output as the control command; When Dz≥D02, it is determined that the safety distance of the left vehicle meets the standard and no control command is output; When Dz<D02, it is determined that the safety distance of the left vehicle does not meet the standard, and the right approach instruction is output as the control instruction; When Dy≥D02, it is determined that the safety distance of the vehicle on the right meets the standard and no control command is output; When Dy<D02, it is determined that the safety distance of the vehicle on the right does not meet the standard, and the left approach instruction is output as the control instruction; The intelligent driving strategy module also compares the front and rear vehicle relative speed B1 with the preset front and rear relative speed B01, and the left and right vehicle relative speed B2 with the preset left and right relative speed B02, and judges the validity of the preset front and rear safety distance D01 and the preset left and right safety distance D02 according to the comparison results, and adjusts the preset front and rear safety distance D01 and the preset left and right safety distance D02 according to the judgment results, wherein: When B1≤B01, the preset front and rear safety distance D01 is determined to be valid, and the preset front and rear safety distance D01 is not adjusted; When B1>B01, the preset front and rear safety distance D01 is determined to be invalid, and the preset front and rear safety distance D01 is adjusted. The adjusted preset front and rear safety distance is set to D01*, D01*=α×d01, α is the front and rear relative speed coefficient, and α=1.42-0.33e (-0.7×（B1-B01）) ; When B2≤B02, the preset left and right safety distance D02 is determined to be valid, and the preset left and right safety distance D02 is not adjusted; When B2>B02, the preset left and right safety distance D02 is determined to be invalid, and the preset left and right safety distance D02 is adjusted. The adjusted preset left and right safety distance is set to D02*, D02*=β×d02, β is the left and right relative speed coefficient, and β=1.32-0.23e (-0.7×（B2 -B02）) ; The intelligent driving strategy module also compares the front vehicle relative angle C1 with the preset front vehicle relative angle C01, the rear vehicle relative angle C2 with the preset rear vehicle relative angle C02, the side vehicle relative angle C3 with the preset side vehicle relative angle C03, and the overtaking vehicle relative angle C4 with the preset overtaking vehicle relative angle C04, and judges the validity of the front and rear vehicle relative speed B1 and the left and right vehicle relative speed B2 according to the comparison results, and corrects the front and rear vehicle relative speed B1 and the left and right vehicle relative speed B2 according to the judgment results, wherein: When C1≤C01, C2≤C02, the relative speed B1 of the front and rear vehicles is determined to be valid, and no correction is made to the relative speed B1 of the front and rear vehicles; When C1≤C01 and C2>C02, the relative speed B1 of the front and rear vehicles is determined to be invalid and the relative speed B1 of the front and rear vehicles is corrected; When C1>C01, C2≤C02, the relative speed B1 of the front and rear vehicles is determined to be invalid, and the relative speed B1 of the front and rear vehicles is corrected; When C1>C01 and C2>C02, the relative speed B1 of the front and rear vehicles is determined to be invalid and the relative speed B1 of the front and rear vehicles is corrected; When C3≤C02, the relative speed B2 of the left and right vehicles is determined to be valid, and no correction is made to the relative speed B2 of the left and right vehicles; When C3>C02, the relative speed B2 of the left and right vehicles is determined to be invalid, and the relative speed B2 of the left and right vehicles is corrected; When C4≤C01 and C4≤C02, the front-to-rear relative speed B1 and the left-to-right relative speed B2 are determined to be valid, and no correction is made to the front-to-rear relative speed B1 and the left-to-right relative speed B2; When C4≤C01 and C4>C02, it is determined that the front-to-rear vehicle relative speed B1 is valid, but the left-to-right vehicle relative speed B2 is invalid. The front-to-rear vehicle relative speed B1 is not corrected, but the left-to-right vehicle relative speed B2 is corrected. When C4>C01 and C4≤C02, the front-to-rear vehicle relative speed B1 is determined to be invalid, but the left-to-right vehicle relative speed B2 is valid, and the front-to-rear vehicle relative speed B1 is corrected, but the left-to-right vehicle relative speed B2 is not corrected; When C4>C01 and C4>C02, the front-to-rear vehicle relative speed B1 and the left-to-right vehicle relative speed B2 are determined to be invalid, and the front-to-rear vehicle relative speed B1 and the left-to-right vehicle relative speed B2 are corrected; When the intelligent driving strategy module corrects the front-to-rear relative speed B1 and the left-to-right relative speed B2, it sets the corrected front-to-rear relative speed to Bc1 and the corrected left-to-right relative speed to Bc2, where Bc1=1.24×B1 and Bc2=1.25×B2.

[0026] Specifically, the preset front-to-rear safety distance D01 refers to a distance value preset during normal driving of the vehicle to ensure a safe distance from the front and rear vehicles, for example, D01=30 meters. The preset left-right safety distance D02 refers to a distance value preset during driving of the vehicle to ensure a safe distance from vehicles in the left and right adjacent lanes, for example, D02=1.5 meters. The deceleration instruction refers to a control instruction requiring the vehicle to decelerate. The acceleration instruction refers to a control instruction requiring the vehicle to accelerate. The right approach instruction refers to a control instruction requiring the vehicle to slightly adjust its driving direction to the right. The left approach instruction refers to a control instruction requiring the vehicle to slightly adjust its driving direction to the left. The preset front-to-rear relative speed B01 refers to a preset value for maintaining a relatively stable speed difference with the front and rear vehicles during normal driving, for example, B01=5 km / h. The preset left-right relative speed B02 refers to a preset value for maintaining a relatively stable speed difference with vehicles in the left and right adjacent lanes during driving, for example, B0 2=3km / h, the front-to-rear relative speed coefficient refers to a coefficient used to adjust the preset front-to-rear safety distance according to the difference between the actual front-to-rear vehicle relative speed and the preset front-to-rear relative speed, the left-to-right relative speed coefficient refers to a coefficient used to adjust the preset left-to-right safety distance according to the difference between the actual left-to-right vehicle relative speed and the preset left-to-right relative speed, the preset front vehicle relative angle C01 refers to a preset value at which the vehicle maintains a relatively stable angle with the vehicle in front during normal driving, for example, C01=10 degrees, the preset rear vehicle relative angle C02 refers to a preset value at which the vehicle maintains a relatively stable angle with the vehicle behind during driving, for example, C02=15 degrees, the preset side vehicle relative angle C03 refers to a preset value at which the vehicle maintains a relatively stable angle with the vehicle in the adjacent lane on the side during driving, for example, C03=20 degrees, and the preset overtaking vehicle relative angle C04 refers to a preset value at which the vehicle maintains a relatively stable angle with the overtaken vehicle during overtaking, for example, C04=30 degrees.

[0027] Specifically, the intelligent driving strategy module monitors the safe distance and relative speed between the vehicle and surrounding vehicles in real time, and issues early warning and control instructions in a timely manner to avoid collision accidents, thereby protecting the lives of drivers and passengers. The left and right relative speed coefficient β is always greater than 1 and infinitely close to 1.32, so that when the preset left and right safety distance D02 is invalid, the preset left and right safety distance D02 is gradually increased until the preset left and right safety distance D02 is valid, thereby protecting the lives of drivers and passengers. At the same time, when the relative angle C1 of the front vehicle, the relative angle C2 of the rear vehicle, the relative angle C3 of the side vehicle, and the relative angle C4 of the overtaking vehicle are large, the relative speed B1 of the front and rear vehicles and the relative speed B2 of the left and right vehicles are corrected to reduce the error between the actual relative speed caused by the excessive relative angle, so as to further avoid the occurrence of collision accidents.

[0028] Specifically, the intelligent driving strategy module further calculates the front vehicle time interval T1, the rear vehicle time interval T2, the left vehicle time interval T3, and the right vehicle time interval T4 based on the front vehicle distance Dc, the rear vehicle distance Dh, the left vehicle distance Dz, and the right vehicle distance Dy, the front and rear vehicle relative speed B1, and the left and right vehicle relative speed B2, where: , , , ; The intelligent driving strategy module compares the front vehicle time interval T1, the rear vehicle time interval T2, the left time interval T3, and the right time interval T4 with the preset front vehicle time interval T01, the preset rear vehicle time interval T02, the preset left time interval T03, and the preset right time interval T04, respectively, and judges the high and low situations of the front vehicle time interval T1, the rear vehicle time interval T2, the left time interval T3, and the right time interval T4 according to the comparison results, and optimizes the control instructions according to the judgment results, wherein: When T1 ≥ T01, the time interval T1 of the preceding vehicle is determined to be high, and the control command is optimized to maintain the current driving state; When T1<T01, it is determined that the time interval T1 of the preceding vehicle is low, and the control command is optimized to a deceleration command; When T2 ≥ T02, the time interval T2 of the following vehicle is determined to be high, and the control command is optimized to maintain the current driving state; When T2<T02, it is determined that the time interval T2 of the following vehicle is low, and the control command is optimized to an acceleration command; When T3 ≥ T03, the left time interval T3 is determined to be high, and the control command is optimized to maintain the current driving state and not change lanes to the left; When T3<T03, the left time interval T3 is determined to be low, and the control command is optimized to maintain the current driving state and change lanes to the left; When T4 ≥ T04, the high / low condition of the right time interval T4 is determined to be high, and the control command is optimized to maintain the current driving state and not change lanes to the right; When T4<T04, it is determined that the high and low situation of the right time interval T4 is low, and the control instruction is optimized to maintain the current driving state and change lanes to the right.

[0029] Specifically, the front vehicle time interval T1 refers to the relative time interval between the vehicle and the front vehicle, that is, the time required for the vehicle to catch up with the front vehicle according to the relative speed B1 of the front and rear vehicles. The rear vehicle time interval T2 refers to the relative time interval between the rear vehicle and the vehicle, that is, the time required for the rear vehicle to catch up with the vehicle according to the relative speed B1 of the front and rear vehicles. The left time interval T3 refers to the relative time interval between the vehicle and the left vehicle, that is, the time required for the vehicle and the left vehicle to reach the same position in the horizontal direction according to the relative speed B2 of the left and right vehicles. The right time interval T4 refers to the relative time interval between the vehicle and the right vehicle, that is, the time required for the vehicle and the right vehicle to reach the same position in the horizontal direction according to the relative speed B2 of the left and right vehicles. The preset front vehicle time interval T2 is the relative time interval between the rear vehicle and the vehicle on the left, that is, the time required for the vehicle and the right vehicle to reach the same position in the horizontal direction according to the relative speed B2 of the left and right vehicles. The time interval T01 refers to a preset safety value for the time interval between the preceding vehicles, for example, the preset time interval between the preceding vehicles T01=2s; the preset time interval between the following vehicles T02 refers to a preset safety value for the time interval between the following vehicles, for example, the preset time interval between the following vehicles T02=1.5s; the preset left time interval T03 refers to a preset safety value for the time interval between the preceding vehicles, for example, T03=3 seconds; the preset right time interval T04 refers to a preset safety value for the time interval between the following vehicles, for example, T04=3 seconds; the maintain current driving state refers to a control instruction requiring the vehicle to maintain the current driving state and continue driving; the no left lane change refers to a control instruction requiring the vehicle to prohibit changing lanes to the left; and the no right lane change refers to a control instruction requiring the vehicle to prohibit changing lanes to the right.

[0030] Specifically, the intelligent driving strategy module dynamically judges the safety conditions around the vehicle through comparison, effectively avoiding the occurrence of collision accidents.

[0031] Specifically, the intelligent driving strategy module also intelligently controls lane change decisions through a lane change decision algorithm, which includes: Step G01: Define the state space and set the vehicle's state to s, which includes the vehicle's speed, acceleration, and current lane. The state of the surrounding vehicles is m1: The relative distance between the vehicle and the front, rear, left and right adjacent vehicles is dij; The road environment state is m2, which includes road curvature, speed limit, traffic flow density, traffic signals and traffic rules; The pedestrian state is m3, which includes the pedestrian's position, direction and speed; Step G02: Define the action space. Set the action space to A, which includes the current lane a0, the left lane change a1, and the right lane change a2. A = {a0, a1, a2}; Step G03: Set the reward function to R, R=w1×R1+w2×R2+w3×R3, where: w1, w2, w3 are weight coefficients, w1+w2+w3=1; R1 is the efficiency reward function, R1=k1(v2-v1), v2 is the increased speed, v1 is the original speed, and k1 is the weight coefficient of the efficiency reward function; R2 is the safety reward function, , is the minimum safe distance from vehicle j after lane change, is the minimum safe distance from vehicle j before lane change, k2 is the weight coefficient of the safety reward function; R3 is the stability reward function, R3=k3, k3 is the weight coefficient of the stability reward function; Step G04: Define the state transition probability and set it to P, P(s`|s,a), where s is the current state, a is action a, and s` is the next state after action a is executed in the current state s; Step G05, solve the optimal strategy, solve the optimal strategy through the value function V(s), set , where k is the number of iterations, is the discount factor, and 0< <1, is the reward obtained by taking action a in state s to transfer to state s', is the optimal value function of state s' when the number of iterations is k, is the optimal value function of state s when the number of iterations is k+1; Step G06, repeat step G05 until | - |<€, the The corresponding strategy is output as a lane change decision, where € is a preset minimum value.

[0032] Specifically, the state space refers to the set of factors that need to be considered when making lane change decisions. The speed of the vehicle refers to the current speed of the vehicle. The acceleration refers to the rate of change of the vehicle's speed. The current lane refers to the lane number and position information of the vehicle. The relative distances between the front, rear, left and right adjacent vehicles and the vehicle refer to the distances between the front, rear, left and right adjacent vehicles and the vehicle. The road curvature refers to a parameter that describes the degree of road curvature. The speed limit refers to the maximum speed limit specified for the road. The traffic flow density refers to the number of vehicles on a unit length of road, in units of vehicles / km. The traffic signal refers to the state of the traffic light. The traffic rules refer to the road Traffic rules, the position of the pedestrian refers to the specific position coordinates of the pedestrian on the road, the direction refers to the direction in which the pedestrian is walking, the speed refers to the speed at which the pedestrian is walking, the current lane a0 refers to the vehicle driving in the current lane without changing lanes, the left lane change a1 refers to changing the vehicle from the current lane to the left adjacent lane, the right lane change a2 refers to changing the vehicle from the current lane to the right adjacent lane, the reward function refers to the function used to evaluate the effect of taking an action and give a corresponding reward value, the weight coefficient refers to the function used to adjust the importance of different reward factors in the total reward, the efficiency reward function refers to the function used to encourage vehicles to increase their driving speed to improve traffic efficiency, and the increased speed refers to the speed of the vehicle before performing the action. After that, the expected driving speed is the speed that the vehicle is expected to reach. The original speed refers to the driving speed of the vehicle before the action is performed. The weight coefficient of the efficiency reward function refers to the parameter used to measure the degree of influence of the efficiency reward function on the reward function. The minimum safe distance from vehicle j after lane change refers to the minimum safe distance maintained between the vehicle and the surrounding vehicles after completing the lane change action. The minimum safe distance from vehicle j before lane change refers to the minimum safe distance maintained between the vehicle and the surrounding vehicles before performing the lane change action. The weight coefficient of the safety reward function refers to the function used to ensure the safety of the vehicle during the lane change process and avoid collisions with other vehicles. The stability reward function refers to the function used to evaluate the stability of the vehicle during the lane change process. The weight coefficient of the stability reward function refers to a parameter used to measure the degree of influence of the stability reward function on the reward function. The state transition probability refers to the probability of transitioning to the next state s after taking action a in the current state s. The current state refers to the current state when making a lane change decision. The action a refers to the action taken in the current state. The next state after performing action a in the current state s refers to the next state that may be reached after taking action a in the current state s. The optimal strategy refers to the lane change decision strategy that can maximize the reward function under a given state. The value function V(s) refers to a parameter used to evaluate the quality of state s. The number of iterations refers to the number of times the value function V(s) is calculated.The discount factor is a parameter used to measure the impact of future rewards on current decisions. The optimal value function is the expected cumulative reward that can be obtained by making decisions according to the optimal strategy starting from the current state. The preset minimum value is a pre-set value to determine whether the algorithm has converged. For example, the preset minimum value is set to 0.01.

[0033] Specifically, the intelligent driving strategy module intelligently controls lane change decisions through a lane change decision algorithm, thereby significantly improving driving safety and efficiency.

[0034] Specifically, the intelligent driving strategy module further calculates the driving state value G according to the driver's fatigue state value E and the attention concentration value F, wherein: G = 0.5 × (E + F); The intelligent driving strategy module compares the driving state value G with the preset driving state value G0, judges the normal state of the driving state based on the comparison result, and corrects the preset front and rear safety distance D01, the preset left and right safety distance D02 and the original speed v1 based on the judgment result, wherein: When G≥G0, the driving state is determined to be normal, and no correction is made to the preset front and rear safety distance D01, the preset left and right safety distance D02, and the original speed v1; When G<G0, the driving state is determined to be abnormal, and the preset front and rear safety distance D01, the preset left and right safety distance D02 and the original speed v1 are corrected. The corrected preset front and rear safety distance is set to Dg01, the corrected preset left and right safety distance is set to Dg02, and the corrected original speed is set to vg1. Dg01=(1+Δ)×D01, Dg02=(1+Δ)×D02, vg1=(Δ-1)×v1, Δ is the driving state coefficient, and 1.1≤β≤1.5; The intelligent driving strategy module also compares the number of times the driver has driven the vehicle H with the preset number of times the driver has driven the vehicle H0, and judges the driver's familiarity with the vehicle based on the comparison result, and adjusts the preset driving state value G0 based on the judgment result, wherein: When H≥H0, the driver's familiarity with the vehicle is determined to be high, and the preset driving state value G0 is not adjusted; When H<H0, it is determined that the driver's familiarity with the vehicle is low, and the preset driving state value G0 is adjusted. The preset driving state value after adjustment is set to Gh0, Gh0=(1+(H0-H) / H0)×G0; The intelligent driving strategy module also compares the weather condition value Q with the preset weather condition value Q0, and judges whether the vehicle driving environment conditions meet the standards based on the comparison result, and modifies the driver's familiarity with the vehicle based on the judgment result, wherein: When Q≥Q0, the vehicle driving environment is judged to meet the standard, and the driver's familiarity with the vehicle is not modified; When Q<Q0, it is determined that the vehicle driving environment condition does not meet the standard, and the driver's familiarity with the vehicle is modified to be low.

[0035] Specifically, the driving state value G refers to a quantitative indicator used to evaluate whether the driver's current driving state is normal. The preset driving state value G0 refers to a pre-set benchmark value for judging whether the driver's current state meets the standard, for example, G0=5. The driving state coefficient refers to a parameter used to correct the preset front and rear safety distance D01, the preset left and right safety distance D02 and the original speed v1. The preset number of times the driver has driven the vehicle H0 refers to a pre-set reference value for judging the driver's familiarity with the vehicle, for example, H0=100 times. The preset weather condition value Q0 refers to a pre-set standard value for judging whether the current weather affects driving safety, for example, Q0=70.

[0036] Specifically, the intelligent driving strategy module significantly improves the safety and adaptability of the vehicle through multi-dimensional data fusion and dynamic adjustment mechanism.

[0037] Specifically, the air conditioning adjustment decision module intelligently adjusts the vehicle air conditioning according to a fuzzy algorithm, and the fuzzy algorithm includes: Step K01, setting input variables and output variables, wherein the input variables are the actual temperature t and the actual humidity n in the vehicle, and the output variables are the air conditioning cooling / heating power Z and the air conditioning wind speed fv; Step K02: Define fuzzy linguistic variables. Set the fuzzy linguistic variables for the actual interior temperature t to include "low temperature," "medium temperature," and "high temperature," the fuzzy linguistic variables for the actual interior humidity n to include "low humidity," "medium humidity," and "high humidity," the fuzzy linguistic variables for the air conditioning cooling / heating power Z to include "low power," "medium power," and "high power," and the fuzzy linguistic variables for the air conditioning wind speed fv to include "low speed," "medium speed," and "high speed." Step K03, defining the membership function, setting the membership function to a triangular membership function, where: (1) For the fuzzy linguistic variable of the actual temperature t inside the car: Low temperature: When the actual temperature inside the car is t≤20℃, its temperature membership is 1; When the actual temperature inside the car is 20℃<t≤22℃, its temperature membership decreases linearly from 1 to 0; Medium temperature: When the actual temperature inside the car is 20℃<t≤24℃, its temperature membership increases linearly from 0 to 1; When the actual temperature inside the vehicle is 24℃<t≤26℃, its temperature membership remains at 1; When the actual temperature inside the car is 26℃<t<28℃, its temperature membership decreases linearly from 1 to 0; "high temperature": When the actual temperature inside the car is 26℃<t<28℃, its temperature membership increases linearly from 0 to 1; When the actual temperature inside the vehicle is t≥28℃, its temperature membership is 1; (2) For the fuzzy linguistic variable of the actual humidity n in the car: Low humidity: When the actual humidity n in the car is n≤40%, its humidity membership is 1; When the actual humidity n in the car is 40%<n≤45%, its humidity membership decreases linearly from 1 to 0; "Medium wet": When the actual humidity n in the car is 40%<n≤50%, its humidity membership increases linearly from 0 to 1; When the actual humidity n in the car is 50%<n≤60%, its humidity membership remains at 1; When the actual humidity in the car is n60%<n≤65%, its humidity membership decreases linearly from 1 to 0; High humidity: When the actual humidity in the car is n60%<n≤65%, its humidity membership increases linearly from 0 to 1; When the actual humidity in the car is n>65%, its humidity membership is 1; (3) For the fuzzy linguistic variables of air conditioning cooling / heating power Z: "Low Power": When the air conditioning cooling / heating power Z is Z≤20kW, its power membership is 1; When the air conditioning cooling / heating power Z is 20kW<Z≤40kW, its power membership decreases linearly from 1 to 0; Medium Power: When the air conditioning cooling / heating power Z is 30kW<Z≤45kW, its power membership increases linearly from 0 to 1; When the air conditioning cooling / heating power Z is 45kW<Z≤55kW, its power membership remains at 1; When the air conditioning cooling / heating power Z is 55kW<Z≤70kW, its power membership decreases linearly from 1 to 0; High Power: When the air conditioning cooling / heating power Z is 55kW<Z≤70kW, its power membership increases linearly from 0 to 1; When the air conditioning cooling / heating power Z is Z>70kW, its power membership is 1; (4) For the fuzzy linguistic variable of air conditioning wind speed fv: "Low speed": When the air conditioning wind speed fv is fv≤2m / s, its wind speed membership is 1; When the air conditioning wind speed fv is 2m / s<fv≤3m / s, its wind speed membership decreases linearly from 1 to 0; Medium speed: When the air conditioning wind speed fv is 2m / s<fv≤4m / s, its wind speed membership increases linearly from 0 to 1; When the air conditioning wind speed fv is 4m / s<fv≤5m / s, its wind speed membership remains at 1; When the air conditioning wind speed fv is 5m / s<fv≤7m / s, its wind speed membership decreases linearly from 1 to 0; "high speed": When the air conditioning wind speed fv is 5m / s<fv≤7m / s, its wind speed membership increases linearly from 0 to 1; When the air conditioning wind speed fv is fv>7m / s, its wind speed membership is 1; Step K04: Optimizing the membership function and adjusting the vehicle air conditioning. An ANFIS model is trained based on the user's historical preference data, and the trained ANFIS model is output as a fuzzy inference model to obtain a fuzzy inference model. The user's current preference data is input into the fuzzy inference model to obtain the user's membership function preferences. The membership function is optimized based on the user's membership function preferences to obtain an optimized membership function, and the vehicle air conditioning is adjusted based on the optimized membership function. Step K05: compare the vehicle's remaining power Y with the preset vehicle's remaining power Y0, determine whether the vehicle's power is sufficient based on the comparison result, and optimize the vehicle's air conditioning adjustment process based on the determination result, wherein: When Y≥Y0, it is determined that the vehicle has sufficient power and the vehicle air conditioning adjustment process is not optimized; When Y<Y0, it is determined that the vehicle's battery power is insufficient, and the vehicle's air conditioning adjustment process is optimized. The optimization method is: If the vehicle's air conditioner is on, the air conditioner cooling / heating power Z is adjusted to "low power" and the air conditioner wind speed fv is adjusted to "low speed"; If the vehicle air conditioner is in the off state, the adjustment process of the vehicle air conditioner is not adjusted.

[0038] Specifically, the fuzzy algorithm refers to an algorithm based on fuzzy set theory, the input variable refers to the original data used by the fuzzy algorithm to make decisions, the output variable refers to the result calculated by the fuzzy algorithm based on the input variable, the actual temperature t in the vehicle refers to the real-time temperature inside the vehicle, the actual humidity n in the vehicle refers to the real-time humidity inside the vehicle, the air conditioning cooling / heating power Z refers to the cooling and heating power output by the vehicle air conditioning in the current state, the air conditioning wind speed fv refers to the wind speed output by the vehicle air conditioning in the current state, the fuzzy linguistic variable refers to the vocabulary used to describe the fuzzy state of the input variable and the output variable, the membership function refers to the mathematical function used to quantify the membership of the fuzzy linguistic variable, the triangular membership function refers to the membership function used to describe the fuzzy state of the actual temperature, humidity, air conditioning power and wind speed in the vehicle, the temperature membership function refers to the membership function used to describe the fuzzy state of the actual temperature, humidity, air conditioning power and wind speed in the vehicle, The membership refers to the membership of the actual temperature t in the vehicle corresponding to "low temperature", "medium temperature" and "high temperature", the humidity membership refers to the membership of the actual humidity n in the vehicle corresponding to "low humidity", "medium humidity" and "high humidity", the power membership refers to the membership of the air conditioning cooling / heating power Z corresponding to "low power", "medium power" and "high power", the wind speed membership refers to the membership of the air conditioning wind speed fv corresponding to "low speed", "medium speed" and "high speed", the user historical preference data refers to the preference record left by the user when using the vehicle air conditioning in the past, the ANFIS model refers to a neural network and fuzzy logic intelligent model used to train and optimize fuzzy inference models, the user current preference data refers to the user's preference setting for the vehicle air conditioning at the current moment, and the preset vehicle remaining power Y0 refers to a preset vehicle remaining power value, for example, Y0=25%, Specifically, the air conditioning adjustment decision module realizes intelligent adjustment and energy saving and consumption reduction of the air conditioning by combining fuzzy algorithm and user preference data.

[0039] Specifically, the decision execution feedback module compares the number of sudden braking times J1 of the vehicle with the preset number of sudden braking times J01 of the vehicle, judges the rationality of the control instruction based on the comparison result, and updates the control instruction based on the judgment result, wherein: When J1≤J01, the control instruction is judged to be reasonable and the control instruction is not updated; When J1>J01, the control instruction is determined to be unreasonable and the control instruction is updated. The data acquisition module updates the vehicle driving information and the traffic environment information, and updates the control instruction according to the updated vehicle driving information and the updated traffic environment information until the control instruction is reasonable.

[0040] Specifically, the preset number of emergency braking times J01 of the vehicle refers to a preset value of the number of emergency braking times, for example, J01=8 times.

[0041] Specifically, the decision execution feedback module can promptly detect whether the control instructions are reasonable by comparing the number of sudden braking times J1 of the vehicle with the preset value J01, and timely update the control instructions to reduce sudden braking situations, reduce the risk of traffic accidents such as rear-end collisions caused by sudden braking, and ensure driving safety.

[0042] Specifically, the decision execution feedback module also compares the number of times the occupants adjust the air conditioner J2 with the preset number of times the occupants adjust the air conditioner J02, judges the effectiveness of the optimized membership function based on the comparison result, and provides feedback adjustment to the process of optimizing the membership function based on the judgment result, wherein: When J2≤J02, the optimized membership function is determined to be valid, and no feedback adjustment is performed on the process of optimizing the membership function; When J1>J01, it is determined that the optimized membership function is invalid, feedback adjustment is performed on the process of optimizing the membership function, and a feedback adjustment window is pushed to the occupants of the vehicle. The occupants of the vehicle fill in the membership function feedback adjustment plan in the feedback adjustment window. The decision execution feedback module obtains the membership function feedback adjustment plan in the feedback adjustment window, and feedbacks the adjustment plan based on the membership function in the feedback adjustment window, and outputs the plan as the optimized membership function.

[0043] Specifically, the preset number of times J02 that the occupants adjust the air conditioner refers to the preset expected number of manual adjustments to the air conditioner, for example, J02=5 times. The feedback adjustment window refers to the interactive interface displayed to the occupants when the decision execution feedback module determines that the membership function is invalid after optimization. The membership function feedback adjustment plan refers to the adjustment suggestions made by the occupants for the membership function based on their own feelings and needs for the in-car environment.

[0044] Specifically, the decision execution feedback module can promptly detect whether the optimized membership function is effective by comparing the number of times the occupants adjust the air conditioning with a preset threshold. If not, the feedback adjustment plan of the occupants is obtained through the feedback adjustment window, and the membership function is further optimized, so that the air conditioning system can better adapt to the personalized needs and different usage scenarios of different occupants, thereby improving the comfort of the occupants.

[0045] See also Figure 2 , which is a flow chart of the intelligent driving management method based on traffic control in this embodiment, the method includes: Step S1, real-time collection of vehicle driving information and traffic environment information; Step S2, outputting control instructions based on vehicle driving information and traffic environment information; Step S3, optimizing the output of the control command according to the vehicle driving information and traffic environment information; Step S4, intelligently controlling the lane change decision through a lane change decision algorithm; Step S5, judging the normality of the driving state based on the vehicle driving information and the traffic environment information, and outputting the judgment result; Step S6, intelligently adjusting the vehicle air conditioner according to the vehicle driving information, traffic environment information and fuzzy algorithm; Step S7, updating the control instructions according to the vehicle driving information and traffic environment information; Step S8: Feedback adjustment is performed on the process of optimizing the membership function in the air conditioning adjustment module according to the vehicle driving information and the traffic environment information.

[0046] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. An intelligent driving management system based on traffic control, characterized in that: The system comprises: Data acquisition module, used to collect vehicle driving information and traffic environment information in real time; An intelligent driving strategy module, which is used to output control instructions based on vehicle driving information and traffic environment information, optimize the output of control instructions based on vehicle driving information and traffic environment information, intelligently control lane change decisions through a lane change decision algorithm, and determine the normal driving state based on vehicle driving information and traffic environment information, and output the judgment results; Air conditioning adjustment module, used to intelligently adjust the vehicle air conditioning according to vehicle driving information, traffic environment information and fuzzy algorithm; The decision execution feedback module is used to update the control instructions according to the vehicle driving information and traffic environment information, and is also used to provide feedback and adjustment to the process of optimizing the membership function in the air conditioning adjustment module according to the vehicle driving information and traffic environment information.

2. The intelligent driving management system based on traffic control according to claim 1 is characterized in that: The intelligent driving strategy module compares the front vehicle distance Dc and the rear vehicle distance Dh with the preset front-to-rear safety distance D01, and compares the left vehicle distance Dz and the right vehicle distance Dy with the preset left-right safety distance D02, judges whether the vehicle safety distance in each direction meets the standard based on the comparison results, and outputs control instructions based on the judgment results.

3. The intelligent driving management system based on traffic control according to claim 2 is characterized in that: The intelligent driving strategy module also compares the front and rear vehicle relative speed B1 with the preset front and rear relative speed B01, and the left and right vehicle relative speed B2 with the preset left and right relative speed B02, and judges the validity of the preset front and rear safety distance D01 and the preset left and right safety distance D02 according to the comparison results, and adjusts the preset front and rear safety distance D01 and the preset left and right safety distance D02 according to the judgment results; The intelligent driving strategy module also compares the front vehicle relative angle C1 with the preset front vehicle relative angle C01, the rear vehicle relative angle C2 with the preset rear vehicle relative angle C02, the side vehicle relative angle C3 with the preset side vehicle relative angle C03, and the overtaking vehicle relative angle C4 with the preset overtaking vehicle relative angle C04, and judges the validity of the front and rear vehicle relative speed B1 and the left and right vehicle relative speed B2 according to the comparison results, and corrects the front and rear vehicle relative speed B1 and the left and right vehicle relative speed B2 according to the judgment results.

4. The intelligent driving management system based on traffic control according to claim 3 is characterized in that: The intelligent driving strategy module also calculates the front vehicle time interval T1, the rear vehicle time interval T2, the left vehicle time interval T3, and the right vehicle time interval T4 based on the front vehicle distance Dc, the rear vehicle distance Dh, the left vehicle distance Dz, and the right vehicle distance Dy, the front and rear vehicle relative speed B1, and the left and right vehicle relative speed B2, where: ， ， ， ； The intelligent driving strategy module compares the front vehicle time interval T1, the rear vehicle time interval T2, the left time interval T3 and the right time interval T4 with the preset front vehicle time interval T01, the preset rear vehicle time interval T02, the preset left time interval T03 and the preset right time interval T04 respectively, and judges the high and low situations of the front vehicle time interval T1, the rear vehicle time interval T2, the left time interval T3 and the right time interval T4 according to the comparison results, and optimizes the control instructions according to the judgment results.

5. The intelligent driving management system and method based on traffic control according to claim 4 is characterized in that: The intelligent driving strategy module also calculates the driving state value G according to the driver's fatigue state value E and the attention concentration value F, wherein: G=0.5×(E+F); The intelligent driving strategy module compares the driving state value G with the preset driving state value G0, judges the normal situation of the driving state based on the comparison result, and corrects the preset front and rear safety distance D01, the preset left and right safety distance D02 and the original speed v1 based on the judgment result.

6. The intelligent driving management system based on traffic control according to claim 5 is characterized in that: The intelligent driving strategy module also compares the number of times the driver has driven the vehicle H with the preset number of times the driver has driven the vehicle H0, judges the driver's familiarity with the vehicle based on the comparison result, and adjusts the preset driving state value G0 based on the judgment result.

7. The intelligent driving management system based on traffic control according to claim 6 is characterized in that: The intelligent driving strategy module also compares the weather condition value Q with the preset weather condition value Q0, judges whether the vehicle driving environment conditions meet the standards based on the comparison results, and modifies the driver's familiarity with the vehicle based on the judgment results.

8. The intelligent driving management system based on traffic control according to claim 1 is characterized in that: The decision execution feedback module compares the number of sudden braking times J1 of the vehicle with the preset number of sudden braking times J01 of the vehicle, judges the rationality of the control instruction based on the comparison result, and updates the control instruction based on the judgment result.

9. The intelligent driving management system based on traffic control according to claim 8, characterized in that: The decision execution feedback module also compares the number of times the vehicle occupants adjust the air conditioning J2 with the preset number of times the vehicle occupants adjust the air conditioning J02, judges the effectiveness of the optimized membership function based on the comparison results, and provides feedback adjustment to the process of optimizing the membership function based on the judgment results.

10. A method for an intelligent driving management system based on traffic control according to any one of claims 1 to 9, characterized in that: The method comprises: Step S1, real-time collection of vehicle driving information and traffic environment information; Step S2, outputting control instructions based on vehicle driving information and traffic environment information; Step S3, optimizing the output of the control command according to the vehicle driving information and traffic environment information; Step S4, intelligently controlling the lane change decision through a lane change decision algorithm; Step S5, judging the normality of the driving state based on the vehicle driving information and the traffic environment information, and outputting the judgment result; Step S6, intelligently adjusting the vehicle air conditioner according to the vehicle driving information, traffic environment information and fuzzy algorithm; Step S7, updating the control instructions according to the vehicle driving information and traffic environment information; Step S8: Feedback adjustment is performed on the process of optimizing the membership function in the air conditioning adjustment module according to the vehicle driving information and the traffic environment information.

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