CN117253301A - Electric automobile safety supervision system and method based on artificial intelligence - Google Patents
Electric automobile safety supervision system and method based on artificial intelligence Download PDFInfo
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- G—PHYSICS
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- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C5/00—Registering or indicating the working of vehicles
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- G07C5/0841—Registering performance data
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- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
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Abstract
The invention discloses an electric automobile safety supervision system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, wherein the system comprises the steps of utilizing a radar sensor group to collect information data around a vehicle and integrating the collected information data; processing data information acquired by a radar sensor group, and establishing a dynamic digital plane model for vehicle safety supervision; performing intelligent analysis on a target in the dynamic digital plane model to determine judgment data of vehicle safety supervision; judging whether the vehicle has the condition of accelerating overtaking currently or not by utilizing an overtaking analysis unit according to the judging data; selectively performing intelligent control on the acceleration rate of the vehicle according to the judgment result; according to the method and the device, whether the overtaking condition exists or not is analyzed by judging the current state of the vehicle, and then the acceleration rate of the vehicle is controlled, so that the probability of traffic accidents can be reduced, and the consumption of energy can be avoided.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to an electric automobile safety supervision system and method based on artificial intelligence.
Background
The electric automobile is an automobile which uses electric energy as a power source and runs through a motor, belongs to a new energy automobile, and is a new era, and meanwhile, the electric automobile is widely accepted by people by virtue of the characteristics of low noise, diversity, no pollution, simple structure and high energy efficiency;
the acceleration rate of the electric automobile is higher than that of the fuel oil automobile, so that the speed of the electric automobile is increased faster in the running process, safety accidents are easy to occur when the electric automobile overtakes in an acceleration lane change mode, if the electric automobile is not overtaken in an acceleration preparation mode, the electric automobile is decelerated and energy is lost, and the electric automobile is loaded with an energy recovery system, but cannot realize hundred percent recovery and has energy consumption, so that the safety of the electric automobile is monitored to be solved urgently;
therefore, an electric automobile safety supervision system and method based on artificial intelligence are urgently needed to solve the technical problems.
Disclosure of Invention
The invention aims to provide an electric automobile safety supervision system and method based on artificial intelligence, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions: the electric automobile safety supervision method based on artificial intelligence comprises the following steps:
s1, acquiring information data around a vehicle by using a radar sensor group, and integrating the acquired information data;
s2, processing data information acquired by a radar sensor group, and establishing a dynamic digital plane model for vehicle safety supervision;
s3, performing intelligent analysis on the targets in the dynamic digital plane model, and determining judgment data of vehicle safety supervision;
s4, judging whether the vehicle has an accelerating overtaking condition currently or not by using an overtaking analysis unit according to the judging data determined in the S3;
s5, selectively performing intelligent control on the acceleration rate of the vehicle according to the judgment result of the S4.
According to the above technical solution, in S1, the information data collected by the radar sensor group forms a set q= { Q 1 ,Q 2 ,Q 3 ,...,Q n }, wherein Q n Representing a set of data acquired by an nth radar sensor;
Q i ＝{q 1 ,q 2 ,q 3 ,...,q m }, wherein Q i Representing the set of data acquired by the ith radar sensor, q m The data of the ith radar sensor is represented by the data of the ith acquired by the ith radar sensor, and the radar sensor acquires information data once every time interval t.
According to the above technical solution, in S2, the building of the dynamic digitized planar model includes the following steps:
s201, detecting a distance value L between the vehicle and a preceding vehicle by using a radar sensor j According to the distance L j Establishing a plane position model of the front vehicle;
s202, detecting distance values S between the two sides of the road by using a radar sensor j According to the distance value S j Determining a lane to which a vehicle belongs;
s203, detecting a distance value p between the vehicle and the vehicle on other lanes by using a radar sensor j According to the distance value p j Establishing a plane position model of the vehicle on other lanes;
s204, establishing a dynamic digital plane model according to the results of S201-S203.
According to the above technical solution, in S3, the intelligent analysis of the dynamic digitized planar model includes the following steps:
s301, determining a lane to which a vehicle belongs, and starting intelligent analysis on a dynamic digital plane model when the vehicle is in an edge lane;
s302, determining that a vehicle distance value between the vehicle and the front vehicle is L j Determining the speed of the vehicle as V 0 According to the distance L j The speed of the front vehicle is determined as V by changing and the speed of the vehicle 1 ；
S303, detecting other vehicles in front of the side of the vehicle by using a plurality of radar sensors to acquire detection data, and forming a set P= { P 1 ,p 2 ,p 3 ,...,p s -wherein s represents data acquired by s radar sensors;
when the detection data in the set P is larger than a set threshold value, judging that the detection data is invalid;
finding the mutation point of the detection data in the set P according to the following formula:
*p＝|p k+1 -p k |；
wherein P represents the absolute value of the difference between two adjacent detection data in the set P;
when p is greater than or equal to a, the data p meets the requirement of mutation points k Or p k+1 Is mutation point data;
further analysis was performed according to the following formula;
*p＝p k+1 -p k ；
when p'. Gtoreq.a, p k Is mutation point data;
when p' < -a, p k+1 Is mutation point data;
when p < a, p is not satisfied with the requirement of mutation point k And p k+1 Neither is the mutation point data, wherein a represents a set mutation point judgment threshold value, and a is a positive number;
because the detection of other vehicles in front of the side uses a plurality of radar sensors, the included angle of the detection direction between every two adjacent radar sensors is alpha, and the error influence of the length of the vehicle in front of the side on the later data analysis can be reduced through analysis and interpretation of the abrupt points;
s304, when other vehicles exist in front of the vehicle side, two mutation point data P exist in the set P e And p f ，e、f∈[1,s]The method comprises the steps of carrying out a first treatment on the surface of the Because when nowWhen a row of radar sensors with different angles detect other vehicles in front of the side, as the other vehicles have a certain length, mutation points of detection data can necessarily appear in areas close to the heads and parking spaces of the other vehicles;
s305, extracting mutation point data p e And p f Corresponding radar sensor information, and obtaining an included angle beta between the radar sensor and the forward direction of the vehicle e And beta f ；
Comparison of beta e And beta f The small size of (2) is extracted as usage data, and is defined as beta min The corresponding detection data is p min ；
S306, according to the extracted usage data beta min And detection data p min The distance D between the side front vehicle and the front vehicle in the vehicle traveling direction is calculated according to the following formula j ；
D j ＝L j -cos(β min )*p min ；
According to D j Front vehicle speed V 1 The vehicle speed of the side front vehicle is analyzed to be V 2 。
According to the above technical scheme, in S4, the analysis result is judged by using the overtaking analysis unit:
when D is j Not less than u and L j When the vehicle overtaking condition is not less than w, judging that the vehicle overtaking condition is met;
when D is j < u or L j When w is less than w, judging that the overtaking condition is not met;
w＝γ 1 *V 0 +γ 2 *V 1 +Ω 1 ；
u＝γ 1 *V 0 +γ 3 *V 2 +Ω 2 ；
wherein, gamma 1 、γ 2 、γ 3 Representing the set coefficient, Ω 1 And omega 2 Representing an error factor;
because under different vehicle speeds, for L j Is different, in the case of a low vehicle speed, L j The distance of (2) may be suitably small when the vehicle speed is relatively highWhen L j The distance of the front-side vehicle V2 is larger than the vehicle speed V0, and the threshold value u is also required to be adjusted, and the factors affecting the threshold value w, that is, the speed of the preceding vehicle and the speed of the vehicle, affect the threshold value u, that is, the front-side vehicle and the speed of the vehicle, and therefore, the threshold value w and the threshold value u are required to be constantly changed and adjusted;
in S5, when the current state of the vehicle meets the overtaking condition, not controlling the acceleration rate of the vehicle;
and when the current state of the vehicle does not meet the overtaking condition, controlling the acceleration rate of the vehicle by utilizing the acceleration control unit.
Through the technical scheme, when the overtaking is judged to be unable to be completed, the acceleration rate of the vehicle is controlled, because the acceleration rate of the electric vehicle is higher than that of the common fuel vehicle, dangerous driving behaviors are usually made when overtaking is carried out by partial drivers because of high acceleration rate, traffic accidents are easy to occur, and meanwhile, the drivers have to emergently brake under the condition that the overtaking is unable to be found, and the electric vehicle has an energy recovery system, but the energy is still consumed without accident, so that the acceleration rate of the electric vehicle is controlled, the probability of traffic accidents is reduced, and the energy consumption is avoided.
The system comprises a radar sensor group for collecting information data, a data integration unit for detecting the information data in the range of at least 180 DEG in front of the vehicle, a digital analysis module for analyzing the data integrated by the data integration unit, and an overtaking analysis unit for judging whether overtaking conditions exist according to the digital analysis module.
According to the technical scheme, the system further comprises a tag adding unit for adding tags to a plurality of radar sensors of the radar sensor group.
According to the technical scheme, the data integrated by the data integration unit is sent to the model building unit, and the model building unit is used for building a dynamic digital plane model of the current position of the vehicle according to the information data acquired by the radar sensor group.
According to the technical scheme, the digital analysis module comprises a lane positioning unit, a distance analysis unit and a vehicle speed comparison unit;
the lane positioning unit is used for analyzing and positioning a current lane of the vehicle according to data information acquired by the radar sensor group, the distance analysis unit is used for detecting vertical distances among other vehicles according to detection data of the radar sensor group, and the vehicle speed comparison unit is used for comparing the speeds among the other vehicles and judging vertical distance changes among the other vehicles in cooperation with the distance analysis unit.
According to the technical scheme, the system further comprises an acceleration control unit, and the acceleration control unit is used for controlling the acceleration rate of the vehicle when the overtaking analysis unit judges that the overtaking condition is not accelerated.
Compared with the prior art, the invention has the beneficial effects that: according to the method and the device, whether the overtaking condition exists is judged through the current state of the vehicle, the overtaking speed of the vehicle is controlled, because the overtaking speed of the electric vehicle is higher than that of an ordinary fuel vehicle, dangerous driving behaviors are usually made when the overtaking speed of a part of drivers is high, traffic accidents are easy to occur, meanwhile, the drivers have to emergently brake under the condition that overtaking cannot be found, and the electric vehicle has an energy recovery system but causes no-cause consumption of energy, so that the overtaking speed of the electric vehicle is controlled, the probability of traffic accidents can be reduced, and the energy consumption can be avoided.
Drawings
FIG. 1 is a schematic flow chart of steps of an electric vehicle safety supervision method based on artificial intelligence;
FIG. 2 is a schematic diagram of the connection relationship of an electric vehicle safety supervision system based on artificial intelligence;
FIG. 3 is a schematic diagram of a dynamic digital planar model of an electric vehicle safety supervision system and method based on artificial intelligence in accordance with the present invention;
fig. 4 is a schematic diagram of a radar sensor group of an electric automobile safety supervision system and method based on artificial intelligence.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1 to 4, the present invention provides the following technical solutions, and an electric vehicle safety supervision method based on artificial intelligence, where the electric vehicle safety supervision method includes the following steps:
s1, acquiring information data around a vehicle by using a radar sensor group, and integrating the acquired information data;
information data collected by radar sensor group forms a set Q= { Q 1 ,Q 2 ,Q 3 ,...,Q n }, wherein Q n Representing a set of data acquired by an nth radar sensor, such as: n is 7, and represents a left side radar sensor, a right side radar sensor, a front radar sensor, a left side 30 DEG radar sensor, a left side 60 DEG radar sensor, a right side 30 DEG radar sensor and a right side 60 DEG radar sensor respectively;
Q i ＝{q 1 ,q 2 ,q 3 ,...,q m }, wherein Q i Representing the set of data acquired by the ith radar sensor, q m The data of the ith radar sensor is represented by the data of the ith acquired by the ith radar sensor, and the radar sensor acquires information data once every time interval t.
S2, processing data information acquired by a radar sensor group, and establishing a dynamic digital plane model for vehicle safety supervision;
the establishment of the dynamic digital plane model comprises the following steps:
s201, detecting a distance value L between the vehicle and a preceding vehicle by using a radar sensor j According to the distance L j Establishing a plane position model of the front vehicle;
s202, detecting distance values S between the two sides of the road by using a radar sensor j According to the distance value S j Determining a lane to which a vehicle belongs;
s203, detecting a distance value p between the vehicle and the vehicle on other lanes by using a radar sensor j According to the distance value p j Establishing a plane position model of the vehicle on other lanes;
s204, establishing a dynamic digital plane model according to the results of S201-S203.
S3, performing intelligent analysis on the targets in the dynamic digital plane model, and determining judgment data of vehicle safety supervision;
the intelligent analysis of the dynamic digital plane model comprises the following steps:
s301, determining a lane to which the vehicle belongs, and starting intelligent analysis on the dynamic digital plane model when the vehicle is in an edge lane, wherein the edge lane refers to lanes on two sides, for example: the current expressway is a bidirectional eight-lane, and then the first lane and the fourth lane are edge lanes;
s302, determining that a vehicle distance value between the vehicle and the front vehicle is L j Determining the speed of the vehicle as V 0 According to the distance L j The speed of the front vehicle is determined as V by changing and the speed of the vehicle 1 ；
S303, detecting other vehicles in front of the side of the vehicle by using a plurality of radar sensors to acquire detection data, and forming a set P= { P 1 ,p 2 ,p 3 ,...,p s -wherein s represents data acquired by s radar sensors;
when the detection data in the set P is larger than a set threshold value, judging that the detection data is invalid;
when the detection data is larger than the set threshold value, the vehicle information of the non-adjacent lanes collected by the radar sensor is described, but the vehicle information of one lane is separated, and the vehicle existing in one lane is not influenced by lane changing of the vehicle, so the vehicle is invalid data;
finding the mutation point of the detection data in the set P according to the following formula:
*p＝|p k+1 -p k |；
wherein P represents the absolute value of the difference between two adjacent detection data in the set P;
when p is greater than or equal to a, the data p meets the requirement of mutation points k Or p k+1 Is mutation point data;
for further analysis and determination of the mutation point data, further analysis was performed according to the following formula;
*p'＝p k+1 -p k ；
when p'. Gtoreq.a, p k Is mutation point data;
when p' < -a, p k+1 Is mutation point data;
when p < a, p is not satisfied with the requirement of mutation point k And p k+1 Neither is the mutation point data, wherein a represents a set mutation point judgment threshold value, and a is a positive number;
because the detection of other vehicles in front of the side uses a plurality of radar sensors, the included angle of the detection direction between every two adjacent radar sensors is alpha, and the error influence of the length of the vehicle in front of the side on the later data analysis can be reduced through analysis and interpretation of the abrupt points;
s304, when other vehicles exist in front of the vehicle side, two mutation point data P exist in the set P e And p f ，e、f∈[1,s]The method comprises the steps of carrying out a first treatment on the surface of the Because the other vehicles have certain lengths when the radar sensors with different angles detect other vehicles in front of the sides, abrupt points of detection data can be necessarily generated in areas close to the heads and parking spaces of the other vehicles;
s305, extracting mutation point data p e And p f Corresponding radar sensor information, and obtaining an included angle beta between the radar sensor and the forward direction of the vehicle e And beta f ；
Comparison ofβ e And beta f The small size of (2) is extracted as usage data, and is defined as beta min The corresponding detection data is p min ；
S306, according to the extracted usage data beta min And detection data p min The distance D between the side front vehicle and the front vehicle in the vehicle traveling direction is calculated according to the following formula j ；
D j ＝L j -cos(β min )*p min ；
According to D j Front vehicle speed V 1 The vehicle speed of the side front vehicle is analyzed to be V 2 。
S4, judging whether the vehicle has an accelerating overtaking condition currently or not by using an overtaking analysis unit according to the judging data determined in the S3;
in S4, the analysis result is judged by the overtaking analysis unit:
when D is j Not less than u and L j When the vehicle overtaking condition is not less than w, judging that the vehicle overtaking condition is met;
when D is j < u or L j When w is less than w, judging that the overtaking condition is not met;
w＝γ 1 *V 0 +γ 2 *V 1 +Ω 1 ；
u＝γ 1 *V 0 +γ 3 *V 2 +Ω 2 ；
wherein, gamma 1 、γ 2 、γ 3 Representing the set coefficient, Ω 1 And omega 2 Representing an error factor;
because under different vehicle speeds, for L j Is different, in the case of a low vehicle speed, L j The distance of L can be suitably small when the vehicle speed is high j The distance of the front-side vehicle V2 is larger than the vehicle speed V0, and the threshold value u is also required to be adjusted, and the factors affecting the threshold value w, that is, the speed of the preceding vehicle and the speed of the vehicle, affect the threshold value u, that is, the front-side vehicle and the speed of the vehicle, and therefore, the threshold value w and the threshold value u are required to be constantly changed and adjusted;
s5, selectively performing intelligent control on the acceleration rate of the vehicle according to the judgment result of the S4.
When the current state of the vehicle meets the overtaking condition, the acceleration rate of the vehicle is not controlled;
and when the current state of the vehicle does not meet the overtaking condition, controlling the acceleration rate of the vehicle by utilizing the acceleration control unit.
Through the technical scheme, when the overtaking is judged to be unable to be completed, the acceleration rate of the vehicle is controlled, because the acceleration rate of the electric vehicle is higher than that of the common fuel vehicle, dangerous driving behaviors are usually made when overtaking is carried out by partial drivers because of high acceleration rate, traffic accidents are easy to occur, and meanwhile, the drivers have to emergently brake under the condition that the overtaking is unable to be found, and the electric vehicle has an energy recovery system, but the energy is still consumed without accident, so that the acceleration rate of the electric vehicle is controlled, the probability of traffic accidents is reduced, and the energy consumption is avoided.
The system comprises a radar sensor group for collecting information data, a data integration unit for detecting the information data in front of the vehicle within at least 180 DEG, a digital analysis module for analyzing the data integrated by the data integration unit,
the digital analysis module comprises a lane positioning unit, a distance analysis unit and a vehicle speed comparison unit;
the lane positioning unit is used for analyzing and positioning a current lane of the vehicle according to data information acquired by the radar sensor group, the distance analysis unit is used for detecting vertical distances among other vehicles according to detection data of the radar sensor group, and the vehicle speed comparison unit is used for comparing the speeds among the other vehicles and judging vertical distance changes among the other vehicles in cooperation with the distance analysis unit.
And judging whether an overtaking analysis unit for overtaking conditions exists or not according to the digital analysis module.
The system also comprises a label adding unit for adding labels to a plurality of radar sensors of the radar sensor group so as to be convenient for regulating the collected data information and facilitating later analysis and calling.
The data integrated by the data integration unit is sent to a model building unit, and the model building unit is used for building a dynamic digital plane model of the current position of the vehicle according to the information data acquired by the radar sensor group.
The system further comprises an acceleration control unit, wherein the acceleration control unit is used for controlling the acceleration rate of the vehicle when the overtaking analysis unit judges the condition of no acceleration overtaking, so that potential safety hazards and energy waste caused by the acceleration overtaking of a vehicle owner are avoided, and the acceleration rate refers to the speed lifting rate of the electric vehicle.
Example 1:
13 radar sensors are arranged in front of the electric vehicle to form a radar sensor group;
judging that the current road is a bidirectional four-lane by using a left radar sensor and a right radar sensor, and judging that the vehicle is currently positioned in the left lane;
the distance value detected by the front radar sensor is L j The current vehicle speed is V 0 Along with the update of the collected data, the speed of the front vehicle is calculated as V 1 ；
The radar sensor detects other vehicles in front of the side of the vehicle, acquires detection data, and forms a set P= { P 1 ,p 2 ,p 3 ,p 4 ,p 5 }；
Finding the mutation point of the detection data in the set P according to the following formula:
*p＝|p 2 -p 1 |＝4；
*p＝|p 3 -p 2 |＝0.25；
*p＝|p 4 -p 3 |＝7；
*p＝|p 5 -p 4 |＝0.15；
wherein P represents the absolute value of the difference between two adjacent detection data in the set P;
* p is greater than or equal to a=1, which indicates that the requirement of mutation points is met, and the data p 2 Or p 1 Or p 4 Or p 5 Is mutation point data;
further analysis was performed according to the following formula;
*p'＝p 2 -p 1 ＝-4；
*p'＝p 5 -p 4 ＝6；
*p’≥a＝1，p 5 is mutation point data;
*p’＜-a＝-1，p 2 is mutation point data;
extraction of mutation Point data p 2 And p 5 Corresponding radar sensor information, and obtaining an included angle beta between the radar sensor and the forward direction of the vehicle 2 =60° and β 5 ＝15°；
Comparison of beta 2 And beta 5 The small size of (2) is extracted as usage data, and is defined as beta min =15°, the corresponding detection data is p min ＝7；
From the extracted usage data beta min And detection data p min The distance D between the side front vehicle and the front vehicle in the vehicle traveling direction is calculated according to the following formula j ；
D j ＝L j -cos(β min )*p min ＝40-0.97*7＝33.21；
According to D j Front vehicle speed V 1 The vehicle speed of the side front vehicle is analyzed to be V 2 。
Judging an analysis result by using the overtaking analysis unit:
D j u=20 and L j More than or equal to w=30, judging that the overtaking condition is met;
w＝γ 1 *V 0 +γ 2 *V 1 +Ω 1 ；
u＝γ 1 *V 0 +γ 3 *V 2 +Ω 2 ；
wherein, gamma 1 、γ 2 、γ 3 Representing the set coefficient, Ω 1 And omega 2 Representing an error factor;
it will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. The electric automobile safety supervision method based on artificial intelligence is characterized by comprising the following steps of:
s1, acquiring information data around a vehicle by using a radar sensor group, and integrating the acquired information data;
s2, processing data information acquired by a radar sensor group, and establishing a dynamic digital plane model for vehicle safety supervision;
s3, performing intelligent analysis on the targets in the dynamic digital plane model, and determining judgment data of vehicle safety supervision;
s4, judging whether the vehicle has an accelerating overtaking condition currently or not by using an overtaking analysis unit according to the judging data determined in the S3;
s5, selectively performing intelligent control on the acceleration rate of the vehicle according to the judgment result of the S4.
2. The electric automobile safety supervision method based on artificial intelligence according to claim 1, wherein the method comprises the following steps: in S1, the information data collected by the radar sensor group constitutes a set q= { Q 1 ,Q 2 ,Q 3 ,...,Q n }, wherein Q n Representing a set of data acquired by an nth radar sensor;
Q i ＝{q 1 ,q 2 ,q 3 ,...,q m }, wherein Q i Representing the set of data acquired by the ith radar sensor, q m The data of the ith radar sensor is represented by the data of the ith acquired by the ith radar sensor, and the radar sensor acquires information data once every time interval t.
3. The method for monitoring and controlling the safety of the electric automobile based on the artificial intelligence according to claim 2, wherein in S2, the establishment of the dynamic digital plane model comprises the following steps:
s201, detecting a distance value L between the vehicle and a preceding vehicle by using a radar sensor j According to the distance L j Establishing a plane position model of the front vehicle;
s202, detecting distance values S between the two sides of the road by using a radar sensor j According to the distance value S j Determining a lane to which a vehicle belongs;
s203, detecting a distance value p between the vehicle and the vehicle on other lanes by using a radar sensor j According to the distance value p j Establishing a plane position model of the vehicle on other lanes;
s204, establishing a dynamic digital plane model according to the results of S201-S203.
4. The electric automobile safety supervision method based on artificial intelligence according to claim 3, wherein the method comprises the following steps: in S3, the intelligent analysis of the dynamic digitized planar model includes the steps of:
s301, determining a lane to which a vehicle belongs, and starting intelligent analysis on a dynamic digital plane model when the vehicle is in an edge lane;
s302, determining that a vehicle distance value between the vehicle and the front vehicle is L j Determining the speed of the vehicle as V 0 According to the distance L j The speed of the front vehicle is determined as V by changing and the speed of the vehicle 1 ；
S303, detecting other vehicles in front of the side of the vehicle by using a plurality of radar sensors to acquire detection data, and forming a set P= { P 1 ,p 2 ,p 3 ,...,p s -wherein s represents data acquired by s radar sensors;
when the detection data in the set P is larger than a set threshold value, judging that the detection data is invalid;
finding the mutation point of the detection data in the set P according to the following formula:
*p＝|p k+1 -p k |；
wherein P represents the absolute value of the difference between two adjacent detection data in the set P;
when p is greater than or equal to a, the data p meets the requirement of mutation points k Or p k+1 Is mutation point data;
further analysis was performed according to the following formula;
*p’＝p k+1 -p k ；
when p'. Gtoreq.a, p k Is mutation point data;
when p' < -a, p k+1 Is mutation point data;
when p < a, p is not satisfied with the requirement of mutation point k And p k+1 Neither is the mutation point data, wherein a represents a set mutation point judgment threshold value, and a is a positive number;
s304, when other vehicles exist in front of the vehicle side, two mutation point data P exist in the set P e And p f ，e、f∈[1,s]；
S305, extracting mutation point data p e And p f Corresponding radar sensor information, and obtaining an included angle beta between the radar sensor and the forward direction of the vehicle e And beta f ；
Comparison of beta e And beta f The small size of (2) is extracted as usage data, and is defined as beta min The corresponding detection data is p min ；
S306, according to the extracted usage data beta min And detection data p min The distance D between the side front vehicle and the front vehicle in the vehicle traveling direction is calculated according to the following formula j ；
D j ＝L j -cos(β min )*p min ；
According to D j Front vehicle speed V 1 The vehicle speed of the side front vehicle is analyzed to be V 2 。
5. The method for monitoring and controlling safety of an electric vehicle according to claim 4, wherein in S4, the analysis result is determined by using an overtaking analysis unit:
when D is j Not less than u and L j When the vehicle overtaking condition is not less than w, judging that the vehicle overtaking condition is met;
when D is j < u or L j When w is less than w, judging that the overtaking condition is not met;
w＝γ 1 *V 0 +γ 2 *V 1 +Ω 1 ；
u＝γ 1 *V 0 +γ 3 *V 2 +Ω 2 ；
wherein, gamma 1 、γ 2 、γ 3 Representing the set coefficient, Ω 1 And omega 2 Representing an error factor;
in S5, when the current state of the vehicle meets the overtaking condition, not controlling the acceleration rate of the vehicle;
and when the current state of the vehicle does not meet the overtaking condition, controlling the acceleration rate of the vehicle by utilizing the acceleration control unit.
6. An electric vehicle safety supervision system for implementing the electric vehicle safety supervision method based on artificial intelligence as defined in any one of claims 1 to 5, characterized in that: the system comprises a radar sensor group for collecting information data, a data integration unit for detecting the information data in the range of at least 180 degrees in front of the vehicle, a digital analysis module for analyzing the data integrated by the data integration unit, and an overtaking analysis unit for judging whether overtaking conditions exist according to the digital analysis module, wherein the radar sensor group is arranged in front of the vehicle and used for detecting the information data in the range of at least 180 degrees in front of the vehicle.
7. The electric vehicle safety supervision system according to claim 6, wherein: the system further comprises a tag adding unit for adding tags to a number of radar sensors of the radar sensor group.
8. The electric vehicle safety supervision system according to claim 7, wherein: the data integrated by the data integration unit is sent to a model building unit, and the model building unit is used for building a dynamic digital plane model of the current position of the vehicle according to the information data acquired by the radar sensor group.
9. The electric vehicle safety supervision system according to claim 8, wherein: the digital analysis module comprises a lane positioning unit, a distance analysis unit and a vehicle speed comparison unit;
the lane positioning unit is used for analyzing and positioning a current lane of the vehicle according to data information acquired by the radar sensor group, the distance analysis unit is used for detecting vertical distances among other vehicles according to detection data of the radar sensor group, and the vehicle speed comparison unit is used for comparing the speeds among the other vehicles and judging vertical distance changes among the other vehicles in cooperation with the distance analysis unit.
10. The electric vehicle safety supervision system according to claim 9, wherein: the system further comprises an acceleration control unit for controlling the acceleration rate of the vehicle when the overtaking analysis unit judges the condition of no acceleration overtaking.
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