CN114944061A - Big data-based unmanned road and vehicle flow speed monitoring system - Google Patents
Big data-based unmanned road and vehicle flow speed monitoring system Download PDFInfo
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Abstract
The invention discloses an unmanned road vehicle flow speed monitoring system based on big data, which comprises a data acquisition unit, a data processing unit and a data evaluation unit, wherein the data acquisition unit, the data processing unit and the data evaluation unit are electrically connected with each other in sequence; the data acquisition unit is used for respectively acquiring the vehicle speed before and after the current vehicle driving lane at each moment in the current time period and whether the adjacent lane vehicle deviates and sending the acquired data of whether the current driving lane and the adjacent lane vehicle deviate to the data processing unit; and the data processing unit is used for comparing the speeds of the front and rear vehicles with the driving speed of the vehicle to obtain a deviation value, calculating a theoretical correction index of the speeds of the front and rear vehicles according to the deviation value, and correcting the driving speed of the vehicle by using the theoretical correction index to obtain a corrected driving speed. Through the whole structure of the equipment, the pre-judged vehicle speeds of the front and the rear vehicles and the vehicles on the adjacent lanes can be more accurately changed or deviated.
Description
Technical Field
The invention relates to the technical field of unmanned driving based on big data, in particular to an unmanned road vehicle flow speed monitoring system based on big data.
Background
The unmanned technology is a complex of multiple leading-edge subjects such as sensors, computers, artificial intelligence, communication, navigation positioning, mode recognition, machine vision, intelligent control and the like, and a train of the unmanned system runs completely under a control system based on communication and comprises operations of train awakening in a vehicle section (including a parking lot, the same below), station preparation, people entering and line entering service, line train running, turning back at a turning-back station, exiting of the line entering service, section entering, car washing, sleeping and the like. The control of the starting, traction, cruising, coasting and braking of the train, the opening and closing of the car door and the shielding door, the station, the vehicle-mounted broadcasting and the like is automatically operated in an unmanned state.
The unmanned system is a mature technology, has gained abundant experience in the aspects of design, construction, equipment manufacturing and the like, and is used as an advanced passenger traffic system to guide the development trend of urban rail traffic. However, when the existing unmanned technology is applied, the speed of vehicles before and after the vehicle is predicted and the lane change or deviation of vehicles in adjacent lanes are not accurate enough, so an unmanned road vehicle flow speed monitoring system based on big data is provided.
Disclosure of Invention
The invention aims to provide an unmanned road vehicle flow speed monitoring system based on big data, which can more accurately judge the vehicle speed of vehicles before and after pre-judgment and the lane change or deviation of vehicles in adjacent lanes.
The invention discloses a big data-based unmanned road vehicle flow speed monitoring system which comprises a data acquisition unit, a data processing unit and a data evaluation unit, wherein the data acquisition unit, the data processing unit and the data evaluation unit are electrically connected with each other in sequence;
the data acquisition unit is used for respectively acquiring the vehicle speed before and after the current vehicle driving lane at each moment in the current time period and whether the adjacent lane vehicle deviates and sending the acquired data of whether the current driving lane and the adjacent lane vehicle deviate to the data processing unit;
the data processing unit is used for comparing the speeds of the front and the rear vehicles with the driving speed of the vehicle to obtain a deviation value, calculating a theoretical correction index of the speeds of the front and the rear vehicles according to the deviation value, and correcting the driving speed of the vehicle by using the theoretical correction index to obtain a corrected driving speed;
whether the current running vehicle has an emergency avoidance instruction or not is obtained based on the corrected running speed and whether the adjacent lane vehicle deviates or not, and then an avoidance instruction sequence of the current time period is obtained, and the data processing unit sends the avoidance instruction to the data evaluation unit;
and the data evaluation unit is used for comparing each avoidance instruction with a set standard, and when the avoidance instruction is greater than or equal to the set standard, alarming the current running vehicle running in the current time period.
Preferably, the data acquisition unit is provided with high-definition cameras at the front end, the rear end and the side surface of the current running vehicle to monitor the running speeds of the front and rear vehicles in the current running lane and whether the vehicles in the adjacent lane deviate from the running lane, so that a sequence of the running speeds of the front and rear vehicles is obtained, wherein the sequence of the running speeds of the front and rear vehicles is { A ═ A { (A) } 1 ,..,A i }。
As a preferable scheme, the front and rear vehicle speeds may be obtained by fitting the speed values according to the front and rear vehicle speed values in a period of time in the collected historical data to obtain a trend function, and performing partial linear analysis on the front and rear vehicle running speed sequence a:
f＝{1,…,i},A＝{A 1 ,..,A i }
where B is the slope of the change straight line of the front-rear vehicle speed sequence a, and B is smaller than 0, it means that the change of the front-rear vehicle speed sequence a on the time line is time-negatively correlated.
Preferably, the gradient B of the change straight line of the preceding and following vehicle speed sequences a is obtained by obtaining an intercept c by a undetermined coefficient method, and obtaining a fitting straight line function representation y ═ Bf + c.
Based on the obtained fitting function, by substituting f into {1, …, i }, y can be obtained as { y ═ y } 1 ,…,y i }；
Loss_i＝|A i -y i |
The loss function is the absolute value of the difference between the actual value and the fitted straight line; the closer the Loss _ i is to 0, the higher the degree of fit proves, and vice versa.
As a preferred scheme, the Loss _ i is normalized, and a theoretical correction index is calculated:
d i ＝1/(1+Loss_i)
wherein d is i A theoretical correction value of the running speed of the vehicle at the moment i;
the corrected vehicle running speed is:
e i ＝d i *A i
it should be noted that the deviation of the vehicle in the adjacent lane is inversely related to the running speed of the vehicle, that is, the larger the deviation of the vehicle in the adjacent lane, the smaller the running speed of the vehicle, so as to correct the avoidance indication condition that can express the running speed of the vehicle.
Preferably, the avoidance indication condition is as follows:
g i ＝e i *{e- STD(S) /[1+(max(S)-mean(S))]}
wherein (max (S) -mean (S)) is the extreme fluctuation evaluation at time i; the larger the difference, the larger the extreme fluctuation; e.g. of the type -STD(S) The stability evaluation is a stability evaluation of whether the new adjacent lane vehicle at the current time has a sequence of the deviated traveling data, and the higher the value is, the stronger the stability is.
As a preferred scheme, the data acquisition unit is further configured to acquire the number of obstacles and the distance sequence from the obstacle in the current vehicle driving lane in the current time period, the data processing unit performs density clustering on the number of obstacles and the distance sequence from the obstacle in the current time period to obtain the number of obstacles in different categories and the number of obstacles in different levels from the obstacle distance level, and the weight corresponding to the distance level from the obstacle, and adjusts the avoidance instruction by using the weight corresponding to each category to obtain the avoidance instruction sequence corresponding to the current time period.
Preferably, the system for monitoring road traffic flow rate based on big data and unmanned road traffic flow rate is stored in an application program of a computer framework and driven to run by a burning program, and further comprises a bus framework, a storage and a bus interface, wherein the bus framework can comprise any number of interconnected buses and bridges, the bus framework links various circuits including one or more processors represented by the processors and the storage represented by the storage, the bus framework can also connect various other circuits such as peripheral equipment, voltage regulators, power management circuits and the like, the bus interface provides an interface between the bus framework and a receiver and a transmitter, and the receiver and the transmitter can be the same element, namely a transceiver, and provides a unit for communicating with various other systems on a transmission medium.
The invention discloses a big data-based driverless road traffic flow speed monitoring system, which has the beneficial effects that:
through the whole structure of the equipment, the number of obstacles and the distance sequence from the obstacles to the current vehicle driving lane in the current time period can be collected, the data processing unit carries out density clustering on the number of obstacles and the distance sequence from the obstacles in the current time period to obtain the number of obstacles and the distance level from the obstacles in different categories and set different numbers of obstacles and weights corresponding to the distance levels from the obstacles, the avoidance indication is adjusted by utilizing the weights corresponding to the categories to obtain the avoidance indication sequence corresponding to the current time period, and therefore the speed of the vehicles before and after the pre-judgment and the lane change or deviation of the vehicles in the adjacent lanes can be more accurate.
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Fig. 1 is a schematic block diagram of an embodiment of a method for detecting movement of electric power supplies according to the present invention.
Detailed Description
The invention will be further elucidated and described with reference to the embodiments and drawings of the specification:
referring to fig. 1, the present invention: a big data-based unmanned road vehicle flow speed monitoring system comprises a data acquisition unit, a data processing unit and a data evaluation unit, wherein the data acquisition unit, the data processing unit and the data evaluation unit are electrically connected with one another in sequence;
the data acquisition unit is used for respectively acquiring the vehicle speed before and after the current vehicle driving lane at each moment in the current time period and whether the adjacent lane vehicle deviates and sending the acquired data of whether the current driving lane and the adjacent lane vehicle deviate to the data processing unit;
the data processing unit is used for comparing the speeds of the front and the rear vehicles with the driving speed of the vehicle to obtain a deviation value, calculating a theoretical correction index of the speeds of the front and the rear vehicles according to the deviation value, and correcting the driving speed of the vehicle by using the theoretical correction index to obtain a corrected driving speed;
obtaining whether the current running vehicle has an emergency avoidance instruction or not based on the corrected running speed and whether the adjacent lane vehicle has deviated running or not, further obtaining an avoidance instruction sequence of the current time period, and sending the avoidance instruction to a data evaluation unit by the data processing unit;
and the data evaluation unit is used for comparing each avoidance instruction with a set standard, and when the avoidance instruction is greater than or equal to the set standard, alarming the current running vehicle running in the current time period.
The data acquisition unit is used for monitoring the running speeds of the vehicles in the front and the back of the current running lane and whether the vehicles in the adjacent lanes deviate or not by installing high-definition cameras at the front end, the back end and the side surfaces of the current running vehicle, so that a running speed sequence A of the vehicles in the front and the back is obtained 1 ,..,A i }。
The speed of the front and rear vehicles can be obtained according to the speed values of the front and rear vehicles in a period of time in the collected historical data, the speed values are fitted to obtain a trend function, and the running speed sequence A of the front and rear vehicles is subjected to partial linear analysis:
f＝{1,…,i},A＝{A 1 ,..,A i }
where B is the slope of the change straight line of the front-rear vehicle speed sequence a, and B is smaller than 0, it means that the change of the front-rear vehicle speed sequence a on the time line is time-negatively correlated.
Calculating the intercept c by using an undetermined coefficient method according to the slope B of the change straight line of the front and rear vehicle speed sequence A; the fitted straight-line function representation y ═ Bf + c was obtained.
Based on the obtained fitting function, by substituting f ═ {1, …, i }, y ═ y can be obtained 1 ,…,y i }；
Loss_i＝|A i -y i |
The loss function is the absolute value of the difference between the actual value and the fitted straight line; the closer the Loss _ i is to 0, the higher the degree of fit proves, and vice versa.
And normalizing the Loss _ i, and calculating a theoretical correction index:
d i ＝1/(1+Loss_i)
wherein d is i Is a theoretical correction value of the running speed of the vehicle at the time point i.
The corrected vehicle running speed is:
e i ＝d i *A i
it should be noted that the deviation of the vehicle in the adjacent lane is inversely related to the running speed of the vehicle, that is, the larger the deviation of the vehicle in the adjacent lane, the smaller the running speed of the vehicle, so as to correct the avoidance indication condition that can express the running speed of the vehicle.
The avoidance indication condition is as follows:
g i ＝e i *{e -STD(S) /[1+(max(S)-mean(S))]}
wherein (max (S) -mean (S)) is the extreme fluctuation evaluation at time i; the larger the difference, the larger the extreme fluctuation; e.g. of the type -STD(S) The stability evaluation is a stability evaluation of whether the new adjacent lane vehicle at the current time has a sequence of the deviated traveling data, and the higher the value is, the stronger the stability is.
The data acquisition unit is further used for acquiring the number of obstacles and the distance sequence from the obstacles in the current vehicle driving lane in the current time period, the data processing unit is used for performing density clustering on the number of obstacles and the distance sequence from the obstacles in the current time period to obtain the number of obstacles in different categories and the distance level from the obstacles, setting different barrier numbers and weights corresponding to the distance levels from the obstacles, and adjusting avoidance instructions by using the weights corresponding to the levels to obtain the avoidance instruction sequence corresponding to the current time period.
The density cluster in the scheme is M, the types of the density cluster are 6, and the mathematical calculation basis of M is to make a difference along the time axis.
Specifically, the scheme sets that: a first cluster classified as primary; a second cluster, classified as light; a third cluster, classified as a normal level; a fourth cluster, classified as a heavy level; a fifth cluster, classified as an emphasis level; and a sixth cluster, classified as a super heavy level.
Secondly, based on the above, the clustering result is subjected to weight distribution in the scheme:
the primary corresponding weight is d 1 1 is ═ 1; the weight corresponding to the light level is d 2 2; the weight corresponding to the ordinary level is d / 3; the weight corresponding to the heavy level is d 4 The weighting level corresponds to a weight d of 4 4 For a super heavy level, d is the weight 4 ＝6。
The final evaluation value of the avoidance instruction is as follows:
j i ＝g i *d j
wherein j is i Evaluation value for avoidance indication, d j Is the weight of the jth class.
In the above, j i Is the final evaluation of the avoidance indication for time i; the higher the value, the higher the degree of avoidance.
Further, the data evaluation unit is also used for predicting the smoothness performance of the current vehicle driving lane:
and inputting the avoidance indication evaluation sequence corresponding to the current time period into the trained time sequence convolution neural network model, and outputting the prediction avoidance indication evaluation sequence of the next time period.
Wherein, the loss function of the time sequence convolution neural network model is as follows:
calculating the overall difference index of the avoidance indication evaluation in any avoidance indication evaluation sequence,
and (3) taking the integral difference index as a correction value of the mean square error loss function, and calculating to obtain a new loss function:
wherein C is z Is the normalized overall difference index as the loss weight, L is the loss function of the sample,
to predict a sample, y i The purpose of the above loss function is to ensure convergence of the L function, and to make L smaller by continuous training, the predicted trend is accurate.
The overall difference index in the above is:
p i ＝1/(1+∑q(s i .t))
wherein p is i Is an overall difference index, s, at the ith time i The evaluation is the avoidance indication evaluation at the ith moment in the current time period, t is an avoidance indication evaluation sequence, and a q () function represents a difference function.
A big data based unmanned road vehicle flow speed monitoring system is stored in the application program of computer frame, driven by burning program, and includes bus frame, memory and bus interface, the bus frame includes any number of interconnected buses and bridges, the bus frame links various circuits including one or more processors represented by processors and memory, the bus frame can also link various other circuits such as peripheral equipment, voltage stabilizer and power management circuit, the bus interface provides interface between the bus frame and receiver and transmitter, the receiver and transmitter can be the same element, i.e. transceiver, provides unit for communicating with various other systems on transmission medium.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (8)
1. The utility model provides an unmanned road vehicle flow velocity monitoring system based on big data which characterized in that: the system comprises a data acquisition unit, a data processing unit and a data evaluation unit, wherein the data acquisition unit, the data processing unit and the data evaluation unit are electrically connected with each other in sequence;
the data acquisition unit is used for respectively acquiring the vehicle speed before and after the current vehicle driving lane at each moment in the current time period and whether the adjacent lane vehicle deviates and sending the acquired data of whether the current driving lane and the adjacent lane vehicle deviate to the data processing unit;
the data processing unit is used for comparing the speeds of the front and the rear vehicles with the driving speed of the vehicle to obtain a deviation value, calculating a theoretical correction index of the speeds of the front and the rear vehicles according to the deviation value, and correcting the driving speed of the vehicle by using the theoretical correction index to obtain a corrected driving speed;
obtaining whether the current running vehicle has an emergency avoidance instruction or not based on the corrected running speed and whether the adjacent lane vehicle has deviated running or not, further obtaining an avoidance instruction sequence of the current time period, and sending the avoidance instruction to a data evaluation unit by the data processing unit;
and the data evaluation unit is used for comparing each avoidance indication with a set standard, and when the avoidance indication is greater than or equal to the set standard, alarming the current running vehicle running in the current time period.
2. The big data based driverless road traffic flow velocity monitoring system according to claim 1, wherein: the data acquisition unit is used for monitoring the running speeds of vehicles around the current running lane and whether the vehicles on the adjacent lanes deviate from running or not by mounting high-definition cameras at the front end, the rear end and the side surfaces of the current running vehicle, so that the front and the rear of the vehicle are obtainedVehicle driving speed sequence a ═ { a ═ a 1 ,..,A i }。
3. The big data based driverless road traffic flow velocity monitoring system according to claim 1, wherein: the speed of the front and rear vehicles can be obtained according to the speed values of the front and rear vehicles in a period of time in the collected historical data, the speed values are fitted to obtain a trend function, and the running speed sequence A of the front and rear vehicles is subjected to partial linear analysis:
f＝{1,...,i},A＝{A 1 ,..,A i }
where B is the slope of the change straight line of the front-rear vehicle speed sequence a, and B is smaller than 0, it means that the change of the front-rear vehicle speed sequence a on the time line is time-negatively correlated.
4. The big data based driverless road traffic flow velocity monitoring system according to claim 3, wherein: obtaining an intercept c by using a pending coefficient method according to the slope B of the change straight line of the front and rear vehicle speed sequences A to obtain a fitting straight line function representation y ═ Bf + c;
based on the obtained fitting function, f ═ 1.. multidot.i } is substituted, and y ═ y · is obtained 1 ,....,y i }；
Loss_i＝|A i -y i |
The loss function is the absolute value of the difference between the actual value and the fitted straight line; the closer the Loss _ i is to 0, the higher the degree of fit proves, and vice versa.
5. The big-data-based driverless road-traffic flow velocity monitoring system of claim 4, wherein: and normalizing the Loss _ i, and calculating a theoretical correction index:
d i ＝1/(1+Loss_i)
wherein d is i A theoretical correction value of the running speed of the vehicle at the moment i;
the corrected vehicle running speed is:
e i ＝d i *A i
it should be noted that the deviation of the vehicle in the adjacent lane is inversely related to the running speed of the vehicle, that is, the larger the deviation of the vehicle in the adjacent lane, the smaller the running speed of the vehicle, so as to correct the avoidance indication condition that can express the running speed of the vehicle.
6. The big data based driverless road traffic flow velocity monitoring system according to claim 5, wherein: the avoidance indication condition is as follows:
g i ＝e i *{e -STD(S) /[1+(max(S)-mean(S))]}
wherein (max (S) -mean (S)) is the extreme fluctuation evaluation at time i; the larger the difference, the larger the extreme fluctuation; e.g. of a cylinder -STD(S) The stability evaluation is a stability evaluation of whether the new adjacent lane vehicle at the current time has a sequence of the deviated traveling data, and the higher the value is, the stronger the stability is.
7. The big data based driverless road traffic flow velocity monitoring system according to claim 1, wherein: the data acquisition unit is further used for acquiring the number of obstacles and the distance sequence from the obstacles in the current vehicle driving lane in the current time period, the data processing unit performs density clustering on the number of obstacles and the distance sequence from the obstacles in the current time period to obtain the number of different types of obstacles and the distance level from the obstacles, sets different numbers of obstacles and weights corresponding to the distance levels from the obstacles, and adjusts the avoidance indication by using the weights corresponding to the levels to obtain the avoidance indication sequence corresponding to the current time period.
8. The big data based driverless road traffic flow velocity monitoring system according to claim 1, wherein; a big data based unmanned road vehicle flow speed monitoring system is stored in the application program of computer frame, driven by burning program, and includes bus frame, memory and bus interface, the bus frame includes any number of interconnected buses and bridges, the bus frame links various circuits including one or more processors represented by processors and memory, the bus frame can also link various other circuits such as peripheral equipment, voltage stabilizer and power management circuit, the bus interface provides interface between the bus frame and receiver and transmitter, the receiver and transmitter can be the same element, i.e. transceiver, provides unit for communicating with various other systems on transmission medium.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115195791A (en) * | 2022-09-19 | 2022-10-18 | 上海伯镭智能科技有限公司 | Unmanned driving speed control method and device based on big data |
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| CN115195791A (en) * | 2022-09-19 | 2022-10-18 | 上海伯镭智能科技有限公司 | Unmanned driving speed control method and device based on big data |
| CN115195791B (en) * | 2022-09-19 | 2023-01-03 | 上海伯镭智能科技有限公司 | Unmanned driving speed control method and device based on big data |
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