CN116161028B - Auxiliary control method for automatic driving vehicle based on artificial intelligence - Google Patents
Auxiliary control method for automatic driving vehicle based on artificial intelligence Download PDFInfo
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- CN116161028B CN116161028B CN202310459153.1A CN202310459153A CN116161028B CN 116161028 B CN116161028 B CN 116161028B CN 202310459153 A CN202310459153 A CN 202310459153A CN 116161028 B CN116161028 B CN 116161028B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/14—Adaptive cruise control
- B60W30/143—Speed control
- B60W30/146—Speed limiting
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
- B60W40/105—Speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
- B60W2050/143—Alarm means
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2554/00—Input parameters relating to objects
- B60W2554/40—Dynamic objects, e.g. animals, windblown objects
- B60W2554/404—Characteristics
- B60W2554/4041—Position
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2720/00—Output or target parameters relating to overall vehicle dynamics
- B60W2720/10—Longitudinal speed
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/72—Electric energy management in electromobility
Abstract
The invention discloses an auxiliary control method of an automatic driving vehicle based on artificial intelligence, which comprises the following steps of: positioning the vehicle position to obtain the position information of the vehicle, and obtaining the running environment information of the vehicle based on the position information of the vehicle; w2: obtaining a road safety signal according to the vehicle running environment information, and sending out warning reminding and vehicle speed limitation in the vehicle running process based on the road safety signal, so that auxiliary control of the vehicle is achieved; according to the invention, the real-time dynamic information of the current vehicle driving road is obtained through the real-time road vehicle data and the speed data, the history warning information of the current vehicle driving road is obtained through the actual road condition data of the current road and the history accident data of the current road, namely, the driving environment of the vehicle road is identified through the comprehensive processing of other vehicles, road surface information and history accident information, so that the obtained driving environment of the vehicle road is more real and effective.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to an auxiliary control method for an automatic driving vehicle based on artificial intelligence.
Background
Intelligent driving refers to a robot that helps a person to drive and in special cases completely replaces the person to drive. Intelligent driving is still continuously explored and tested as part of the important development of intelligent traffic systems in various countries. The intelligent driving has great effect on the economic and technological development of various countries and the comprehensive national force promotion. Unmanned driving is the direction of future development of the automobile industry, and has great significance as a core of intelligent driving. Unmanned is a technology for sensing and judging the surrounding environment of an automobile in running by carrying various sensing devices such as advanced sensors, so as to obtain the state of the automobile and the surrounding environment information, automatically plan a driving route and control the automobile to reach a destination.
In the existing automatic driving of the vehicle, the auxiliary driving function of the vehicle is lacking based on the road driving environment, so that the vehicle cannot automatically drive at different speeds in different road driving environments, and safety is lacking.
Disclosure of Invention
The invention aims to provide an auxiliary control method of an automatic driving vehicle based on artificial intelligence, which is characterized in that road vehicle speed data, road vehicle type data, road history data and road environment data of a road where a vehicle is located are analyzed, namely, real-time dynamic information of a current vehicle driving road is obtained through real-time road vehicle data and speed data, the real-time dynamic information reflects the speed and congestion condition of the current road, meanwhile, the history warning information of the current vehicle driving road is obtained through the actual road condition data of the current road and the history accident data of the current road, the history warning information reflects the actual road surface information and the road accident information, namely, the driving environment of the vehicle road is identified through comprehensive processing of other vehicles, road surface information and history accident information, and speed limiting control and voice reminding in the driving process are carried out on the driving speed of the vehicle based on the driving environment of the vehicle road, so that auxiliary control on the driving process of a vehicle owner is realized.
The aim of the invention can be achieved by the following technical scheme:
an auxiliary control method of an automatic driving vehicle based on artificial intelligence comprises the following steps:
w1: positioning the vehicle position to obtain the position information of the vehicle, and obtaining the running environment information of the vehicle based on the position information of the vehicle;
w2: and obtaining a road safety signal according to the vehicle running environment information, and sending out warning reminding and vehicle speed limitation in the vehicle running process based on the road safety signal, so that auxiliary control of the vehicle is achieved.
As a further scheme of the invention: in W1, the vehicle position is positioned by a Beidou navigation system or a GPS positioning navigation system.
As a further scheme of the invention: the vehicle travel environment information includes road vehicle speed data, road vehicle type data, road history data, and road environment data.
As a further scheme of the invention: acquiring a real-time running value of a vehicle running road through road vehicle speed data and road vehicle type data, and marking the real-time running value as VD;
acquiring a road warning value of a vehicle running road through the road history data and the road environment data, and marking the road warning value as RU;
processing the real-time driving value VD and the road warning value RU of the driving road of the vehicle, namely, by a formula
Obtaining a vehicle running dynamic value Bi, wherein a1, a2 and a3 are preset proportion coefficients, and Ti is a deviation value of the tire pressure of the vehicle;
the acquisition process of the deviation value Ti of the tire pressure of the vehicle comprises the following steps: the tire pressure of each tire of the vehicle is obtained through the tire pressure sensor, and the variance value of the tire pressure of the vehicle, namely the deviation value of the tire pressure of the vehicle, is obtained through a variance calculation formula.
As a further scheme of the invention: the limit of the preset vehicle running dynamic value threshold is Bi1 and Bi2, wherein Bi1< Bi2:
when Bi is less than Bi1, the vehicle has good running road environment, and generates a road safety high signal;
when Bi1< Bi < Bi2, the vehicle is general in driving road environment, and a road safety general signal is generated;
when Bi > Bi2, the vehicle is poor in road environment, and a road safety poor signal is generated.
As a further scheme of the invention: and when the obtained road safety high signal of the vehicle is transmitted, a green safety instruction is sent to the vehicle display terminal.
As a further scheme of the invention: when a road safety general signal for the vehicle to run is obtained, a yellow prompt instruction is sent to a vehicle display terminal, primary control information is sent to a driving motor of the vehicle based on the yellow prompt instruction, the driving motor receives the primary control information, and the output current value of the driving motor is controlled to realize vehicle speed control.
As a further scheme of the invention: when a road safety difference signal of the vehicle running is obtained, a red alarm instruction is sent to a vehicle display terminal, secondary control information is sent to a driving motor of the vehicle based on the red alarm instruction, the driving motor receives the secondary control information, and the output current value of the driving motor is controlled to realize vehicle speed control.
As a further scheme of the invention: the real-time driving value of the vehicle driving road is obtained by the following steps:
acquiring a speed factor V of a vehicle driving road through road vehicle speed data, and acquiring a congestion factor D of the vehicle driving road through road vehicle type data;
using the formula
Acquiring a real-time driving value VD, < > of a driving road of a vehicle>
For a specific proportionality coefficient>
1.5682467.
As a further scheme of the invention: the road warning value of the vehicle driving road is obtained by the following steps:
acquiring an accident factor R of a vehicle driving road through road history data, and acquiring a road factor U of the vehicle driving road through road environment data;
the accident factor R and the road factor U of the vehicle driving road are weighted, and the weight ratio of the accident factor R is divided into
The weight ratio of the road factor U is divided into +.>
Wherein K1 and K2 are both greater than 0, and K2 is greater than K1;
by the formula ru=r =
+U*/>
A road warning value RU of a vehicle running road is acquired.
The invention has the beneficial effects that: the invention analyzes the road vehicle speed data, the road vehicle type data, the road history data and the road environment data of the road where the vehicle is located, namely, acquires the real-time dynamic information of the current vehicle running road through the real-time road vehicle data and the speed data, the real-time dynamic information shows the speed and the congestion condition of the current road, and meanwhile, acquires the history warning information of the current vehicle running road through the actual road condition data of the current road and the current road history accident data, and shows the actual road surface information and the road accident information, namely, identifies the running environment of the vehicle road through the comprehensive treatment of other vehicles, the road surface information and the history accident information, so that the obtained running environment of the vehicle road is more real and effective;
and the speed limit control and the voice reminding in the driving process are carried out on the driving speed of the vehicle based on the driving environment of the vehicle road so as to realize auxiliary control on the driving process of the vehicle owner.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of the present invention.
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.
Referring to fig. 1, the invention provides an auxiliary control method for an automatic driving vehicle based on artificial intelligence, comprising the following steps:
w1: positioning the vehicle position to obtain the position information of the vehicle, and obtaining the running environment information of the vehicle based on the position information of the vehicle;
w2: and obtaining a road safety signal according to the vehicle running environment information, and sending out warning and reminding in the vehicle running process based on the road safety signal, so that auxiliary control of the vehicle is achieved.
In W1, the vehicle position is positioned by a Beidou navigation system or a GPS positioning navigation system;
in W1, the vehicle running environment information includes road vehicle speed data, road vehicle type data, road history data, and road environment data;
the road vehicle speed data includes a speed of a vehicle immediately in front of the target vehicle, a speed of a vehicle immediately behind the target vehicle, and a vehicle speed of a lane beside the target vehicle;
the speed of the vehicle right ahead of the target vehicle is recorded as the front vehicle speed, the speed of the vehicle right behind the target vehicle is recorded as the rear vehicle speed, and the speed of the vehicle beside the target vehicle in the same direction as the side lane speed;
the road vehicle type data includes the number of large vehicles, the number of medium vehicles, and the number of small vehicles in the traveling direction of the target vehicle;
the road history data includes the total number of road traffic accidents occurring on the road where the target vehicle is traveling, the time period during which the road traffic accidents occur, and the level at which the road traffic accidents occur;
the road environment data includes the number of lanes of the target vehicle running road, the number of curves of the target vehicle running road, and the road surface quality index of the target vehicle running road.
In W2, the front vehicle speed is marked as V1, the rear vehicle speed is marked as V2 and the side lane speed is marked as V3 in the vehicle running environment information,
acquiring a speed factor V of a vehicle driving road, wherein ∈>
Is a specific proportionality coefficient, and->
；
In W2, the number of large vehicles is marked as D1, the number of medium vehicles is marked as D2, the number of small vehicles is marked as D3 in the vehicle running environment information, and the formula is adopted
Acquiring a congestion factor D of a vehicle driving road, wherein +.>
Is a specific proportionality coefficient;
using the formula
Acquiring a real-time driving value VD, < > of a driving road of a vehicle>
For a specific proportionality coefficient>
Taking 1.5682467;
in W2, the total number of times of occurrence of road traffic accident in the vehicle running environment information is marked as R1, the time period of occurrence of road traffic accident is marked as R2, the grade of occurrence of road traffic accident is marked as R3, and the formula is passed
Obtaining accident factor R of vehicle driving road, wherein ∈>
Is a specific proportionality coefficient, and
；
in W2, the number of lanes of the target vehicle running road in the vehicle running environment information is denoted by Wt, the number of curves of the target vehicle running road is denoted by St, the road surface quality index of the target vehicle running road is denoted by PQI, and the following formulas are used to calculate the road quality index of the target vehicle running road
Acquiring a road factor U of a vehicle driving road, wherein ∈>
Is a preset proportionality coefficient;
weighting the accident factor R and the road factor U of the vehicle driving road, and weighting the accident factor RThe weight ratio is divided into
The weight ratio of the road factor U is divided into +.>
Wherein K1 and K2 are both greater than 0, and K2 is greater than K1;
by the formula ru=r =
+U*/>
Acquiring a road warning value RU of a vehicle running road;
the road surface quality index pqi=s1xp1+s2xp2+s3xp3+s4xp4, wherein S1, S2, S3, S4 respectively represent the road surface flatness, damage condition, bearing capacity and fraction occupied by the anti-skid capacity index; p1, P2, P3 and P4 respectively represent weight values occupied by indexes of road surface evenness, damage condition, bearing capacity and anti-skid capacity.
Processing the real-time driving value VD and the road warning value RU of the driving road of the vehicle, namely, by a formula
Obtaining a vehicle running dynamic value Bi, wherein a1, a2 and a3 are preset proportion coefficients, and Ti is a deviation value of the tire pressure of the vehicle;
the acquisition process of the deviation value Ti of the tire pressure of the vehicle comprises the following steps: the tire pressure of each tire of the vehicle is obtained through a tire pressure sensor, and the variance value of the tire pressure of the vehicle, namely the deviation value of the tire pressure of the vehicle, is obtained through a variance calculation formula;
the limit of the preset vehicle running dynamic value threshold is Bi1 and Bi2, wherein Bi1< Bi2:
when Bi is less than Bi1, the vehicle has good running road environment, and generates a road safety high signal;
when Bi1< Bi < Bi2, the vehicle is general in driving road environment, and a road safety general signal is generated;
when Bi > Bi2, the vehicle is poor in driving road environment, and a road safety poor signal is generated;
when a road safety high signal for vehicle running is obtained, a green safety instruction is sent to a vehicle display terminal, and running environment safety voice prompt information is sent to a vehicle voice terminal based on the green safety instruction;
when the road safety general signal of the vehicle running is obtained, a yellow prompt instruction is sent to the vehicle display terminal, and a voice prompt message of the running environment general is sent to the vehicle voice terminal based on the yellow prompt instruction,
meanwhile, primary control information is sent to a driving motor of the vehicle based on a yellow prompt instruction, the driving motor receives the primary control information and controls the output current value of the driving motor, so that the vehicle speed corresponding to the maximum output current of the driving motor is not more than 60km/h;
when a road safety difference signal of the vehicle running is obtained, a red alarm instruction is sent to a vehicle display terminal, a running environment difference voice prompt message is sent to a vehicle voice terminal based on the red alarm instruction, and secondary control information is sent to a driving motor of the vehicle;
meanwhile, based on the red prompt instruction, secondary control information is sent to a driving motor of the vehicle, the driving motor receives the secondary control information, and the output current value of the driving motor is controlled, so that the vehicle speed corresponding to the maximum output current of the driving motor is not more than 50km/h.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (6)
1. An auxiliary control method of an automatic driving vehicle based on artificial intelligence is characterized by comprising the following steps:
w1: positioning the vehicle position to obtain the position information of the vehicle, and obtaining the running environment information of the vehicle based on the position information of the vehicle;
w2: obtaining a road safety signal according to the vehicle running environment information, and sending out warning reminding and vehicle speed limitation in the vehicle running process based on the road safety signal, so that auxiliary control of the vehicle is achieved;
the vehicle running environment information includes road vehicle speed data, road vehicle type data, road history data, and road environment data;
acquiring a real-time running value of a vehicle running road through road vehicle speed data and road vehicle type data, and marking the real-time running value as VD;
acquiring a road warning value of a vehicle running road through the road history data and the road environment data, and marking the road warning value as RU;
processing the real-time driving value VD and the road warning value RU of the driving road of the vehicle, namely, by a formula
Obtaining a vehicle running dynamic value Bi, wherein a1, a2 and a3 are preset proportion coefficients, and Ti is a deviation value of the tire pressure of the vehicle;
the acquisition process of the deviation value Ti of the tire pressure of the vehicle comprises the following steps: the tire pressure of each tire of the vehicle is obtained through a tire pressure sensor, and a variance value of the tire pressure of the vehicle, namely the deviation of the tire pressure of the vehicle, is obtained through a variance calculation formula;
the real-time driving value of the vehicle driving road is obtained by the following steps:
wherein, the front vehicle speed is marked as V1, the rear vehicle speed is marked as V2 and the side lane speed is marked as V3 in the vehicle running environment information,
acquiring a speed factor V of a vehicle driving road, wherein ∈>
Is a specific proportionality coefficient, and->
；
The speed of the front vehicle is the speed of the vehicle right in front of the target vehicle, the speed of the rear vehicle is the speed of the vehicle right behind the target vehicle, and the speed of the side lane is the speed of the vehicle in the same direction beside the target vehicle;
the number of large vehicles is marked as D1, the number of medium vehicles is marked as D2, the number of small vehicles is marked as D3 in the vehicle running environment information, and the formula is passed
Acquiring a congestion factor D of a vehicle driving road, wherein +.>
Is a specific proportionality coefficient;
using the formula
Acquiring a real-time driving value VD, < > of a driving road of a vehicle>
For a specific proportionality coefficient>
Taking 1.5682467;
the road warning value of the vehicle driving road is obtained by the following steps:
the total times of occurrence of road traffic accidents in the vehicle running environment information is marked as R1, the time period of occurrence of the road traffic accidents is marked as R2, the grade of occurrence of the road traffic accidents is marked as R3, and the formula is passed
Obtaining accident factor R of vehicle driving road, wherein ∈>
Is a specific proportionality coefficient, and->
；
In the vehicle running environmentThe number of lanes of the target vehicle driving road in the information is marked as Wt, the number of curves of the target vehicle driving road is marked as St, the road surface quality index of the target vehicle driving road is marked as PQI, and the formula is adopted
Acquiring a road factor U of a vehicle driving road, wherein ∈>
Is a preset proportionality coefficient;
the accident factor R and the road factor U of the vehicle driving road are weighted, and the weight ratio of the accident factor R is divided into
The weight ratio of the road factor U is divided into +.>
Wherein n1 and n2 are both greater than 0, and n2 is greater than n1;
by the formula
Acquiring a road warning value RU of a vehicle running road;
the road surface quality index pqi=s1xp1+s2xp2+s3xp3+s4xp4, wherein S1, S2, S3, S4 respectively represent the road surface flatness, damage condition, bearing capacity and fraction occupied by the anti-skid capacity index; p1, P2, P3 and P4 respectively represent weight values occupied by indexes of road surface evenness, damage condition, bearing capacity and anti-skid capacity.
2. The auxiliary control method for an automatic driving vehicle based on artificial intelligence according to claim 1, wherein in W1, the vehicle position is located by a beidou navigation system or a GPS positioning navigation system.
3. The auxiliary control method for an automatic driving vehicle based on artificial intelligence according to claim 1, wherein the limit of the preset vehicle running dynamic value threshold is Bi1 and Bi2, wherein Bi1< Bi2:
when Bi is less than Bi1, the vehicle has good running road environment, and generates a road safety high signal;
when Bi1< Bi < Bi2, the vehicle is general in driving road environment, and a road safety general signal is generated;
when Bi > Bi2, the vehicle is poor in road environment, and a road safety poor signal is generated.
4. An artificial intelligence based auxiliary control method for an automatically driven vehicle according to claim 3, wherein a green safety command is sent to a vehicle display terminal when a road safety high signal is obtained for the vehicle to travel.
5. The auxiliary control method for the automatic driving vehicle based on the artificial intelligence according to claim 4, wherein when a road safety general signal for the vehicle to run is obtained, a yellow prompt command is sent to a vehicle display terminal, primary control information is sent to a driving motor of the vehicle based on the yellow prompt command, the driving motor receives the primary control information, and the output current value of the driving motor is controlled to realize vehicle speed control.
6. The auxiliary control method for the automatic driving vehicle based on artificial intelligence according to claim 5, wherein when a road safety difference signal of the vehicle running is obtained, a red warning command is sent to a vehicle display terminal, secondary control information is sent to a driving motor of the vehicle based on the red warning command, the driving motor receives the secondary control information, and the output current value of the driving motor is controlled to realize vehicle speed control.
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| KR20130119751A (en) * | 2012-04-24 | 2013-11-01 | 주식회사대성엘텍 | Driving safety support device and method that use communications module |
| CN105243854B (en) * | 2015-09-24 | 2017-11-03 | 侯文宇 | A kind of method and device detected to vehicle flowrate in road |
| CN106530720B (en) * | 2016-12-28 | 2018-12-14 | 吉林大学 | A kind of identification of Expressway Road traffic safety stain section and method for early warning |
| CN108898834A (en) * | 2018-07-12 | 2018-11-27 | 安徽电信工程有限责任公司 | A kind of intellectual traffic control method monitoring traffic accident at intersection |
| CN111071259B (en) * | 2019-12-27 | 2020-09-29 | 清华大学 | Vehicle speed prediction method, vehicle speed prediction device, vehicle control device, and storage medium |
| CN112319491B (en) * | 2020-10-28 | 2022-07-29 | 惠州市德赛西威汽车电子股份有限公司 | Safe driving early warning method and system |
| CN112428952A (en) * | 2020-11-04 | 2021-03-02 | 守门狗(杭州)科技服务有限公司 | Vehicle safety early warning system based on Internet of things |
| KR102453688B1 (en) * | 2021-04-28 | 2022-10-11 | 아주대학교산학협력단 | System for assessmenting potential risk of traffic accident and method thereof |
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