CN105976293A - Driving active service evaluation system and method under Internet-of-Vehicles environment - Google Patents

Abstract

The invention provides a driving active service evaluation system and method under an Internet-of-Vehicles environment. Evaluation is carried out from the aspects of robustness, expansibility, adaptability, autonomy and real-time performance; a dynamic evaluation parameter formula can be obtained through the five characteristic evaluation indexes; dynamic evaluation parameter are introduced into a comprehensive evaluation formula, so that an evaluation index fcost can be solved; and the level of services is judged according to judgment criteria and the evaluation indexes. According to the system and method of the invention, different services of vehicles with different needs are evaluated by using the driving active service evaluation system and by means of the Internet-of-Vehicles environment, so that a push method can be optimized, the quality of the services as well as the robustness, expansibility, adaptability, autonomy and real-time performance of the system can be improved.

Description

A kind of car networked environment down train is taken the initiative in offering a hand evaluation system and method

Technical field

The present invention relates to a kind of car networked environment down train take the initiative in offering a hand evaluation system and method.

Background technology

Traditional driving service system service mode is that the Passive Mode of " request-response ", i.e. driver are asked required Service, background server just can provide corresponding service according to the demand of driver, it is impossible to enough meets vehicle the highest Security requirement, and the service that background server provides cannot be carried out effectively evaluating.In this case it is proposed car Driving Active Service System under the networked environment i.e. service mode of " automatic identification-active push " the service that it is provided Carry out effective and feasibility assessment.

In the present circumstance, the method being also formed without driving Active Service System service quality is evaluated, it is impossible to Taking the initiative in offering a hand of completing to be provided vehicle is evaluated, it is impossible to determine the quality of the quality of taking the initiative in offering a hand being provided vehicle.

The present invention utilizes driving Active Service System to carry out, by car networked environment, the service that different demand vehicles are different Evaluate, optimize the method pushed, improve service quality and the robustness of system, autgmentability, adaptivity, autonomy and in real time Property.

Summary of the invention

In order to realize vehicle is actively provided the evaluation of service, the invention provides a kind of car networked environment down train actively Service evaluation system and method, be evaluated from robustness, autgmentability, adaptivity, autonomy and real-time, by five spies Property evaluation index obtain dynamic evaluation parameter, finally bring overall merit formula into, seek f_cost, and given system value term of reference R, contrasts.

The present invention realizes above-mentioned technical purpose by techniques below means.

A kind of car networked environment down train is taken the initiative in offering a hand evaluation system, it is characterised in that include board units (OBU), road Side apparatus (RSU), center, the 4G public network of taking the initiative in offering a hand of driving a vehicle；

Board units (OBU) is used for detecting and gather climatic information and vehicle internal information；

Roadside device (RSU) is used for detecting and gather transport information, accident information, road information, vehicle receive service After time, the vehicle fleet serviced, the vehicle fleet that serviced by emergency made a response and serviced by generality Vehicle fleet；

The driving center of taking the initiative in offering a hand includes detection module, processing module, computing module, evaluation module, detection module, detection Module quantity of information inside detection and collecting vehicle carrier unit, quantity of information, board units and the trackside inside roadside device set For quantity of information, system senses time and system service time after being interacted by 802.11P agreement；Processing module is used for Detection module detection and the information collected are filtered；Computing module be used for calculating robustness parameter r, autgmentability parameter e, Adaptivity parameter a, autonomy parameter h and real-time parameter t, and real-time parameter is normalized, after normalization Real-time parameter t^*, according to the judgement schematics f (r, e, a, h, the t that have built^*) Calculation Estimation parameter f, take in the period Minima f_cost=argminf (r, e, a, h, t^*) as evaluation index；Evaluation module is for according to evaluation index and systemic presupposition Evaluation criterion in the period service grade be evaluated.

A kind of car networked environment down train is taken the initiative in offering a hand evaluation methodology, it is characterised in that comprise the following steps:

(1) information gathering: board units (OBU) gathers climatic information and vehicle internal information amount；Roadside device (RSU) is used In detection and gather transport information, accident information, road information, vehicle receive make a response after service time, serviced Vehicle fleet, the vehicle fleet serviced by emergency and by the vehicle fleet that services of generality；During driving is taken the initiative in offering a hand The information that heart detection module equipment collecting vehicle carrier unit (OBU), roadside device (RSU) will gather, and calculate quantity of information；

(2) processing module carries out filtration treatment to the information of detection equipment collection, filters unwanted information；Computing module Robustness parameter r, autgmentability parameter e, adaptivity parameter a, autonomy parameter h and real-time parameter is calculated according to remaining information T,

Robustness variable parameter r=CR (1)

Autgmentability parameter

Adaptivity parameter

Autonomy parameter

Real-time parameter t=t₁+t₂+t₃ (11)

In formula, CR is random concordance ratio；Q is gross information content, T be total Mission Time, Q ＇ be controlled quentity controlled variable, when T ＇ is for controlling Between, m+n=1, C_nFor task coefficient of association Q₁For board units detection and collect quantity of information, Q₂For roadside device detection and The quantity of information collected, Q₃Quantity of information after interacting for board units and roadside device, t₁For system senses time, t₂ For system operations time, t₃For system service time, t₄The time made a response after service is received for vehicle；N is serviced Vehicle fleet, N₁Vehicle fleet, N for emergency service₂For the vehicle fleet serviced by generality.

And real-time parameter t is normalized,

$t^{*}=\frac{t-\mathrm{\μ}}{\mathrm{\σ}}---\left(12\right)$

To the real-time after robustness parameter r, autgmentability parameter e, adaptivity parameter a, autonomy parameter h and normalization Parameter t^*Give weight k respectively₁、k₂、k₃、k₄And k₅, build judgement schematics f (r, e, a, h, t^*) Calculation Estimation parameter f:

F (r, e, a, h,^t*)=k₁r+k₂e+k₃a+k₄h+k₅t^* (13)

Wherein k₁+k₂+k₃+k₄+k₅=1；

By robustness parameter r obtained in real time, autgmentability parameter e, adaptivity parameter a, autonomy parameter h and normalization After real-time parameter t* bring judgement schematics into, and to seek the minima of period inner evaluation parameter f be evaluation index,

f_cast=argminf (r, e, a, h, t^*) (14)

(3) in evaluation module, evaluation criterion is preset, according to evaluation index f_costWith the grade that evaluation criterion draws service.

Preferably, described weight k₁、k₂、k₃、k₄And k₅Value be k₁>=0.3, k₂+k₄=0.4, k₅≤0.1。

Preferably, the random concordance computational methods than CR are:

Drawn corresponding degree of membership by affecting robustness factor, set up corresponding matrix A, then obtain according to degree of membership Weight vector W and eigenvalue of maximum λ_max, specifically calculate process:

AW=λ max (2)

$W_i=\stackrel{\‾}{W_i}/\ΣW_i,(i=1,2,\mathrm{...}n),\stackrel{\‾}{W_i}={(\Πa_{ii})}^{1/n}---\left(3\right)$

λ_max=Σ (AW)_i/nW_i, [AW]=[a] [W]^T (4)

CI=(λ_max-n)/(n-1) (5)

$CR=\frac{CI}{RI}---\left(6\right)$

Wherein, CI is Aver-age Random Consistency Index, and RI is same order Aver-age Random Consistency Index.

Preferably, climatic information includes visibility, ice-patch surface, temperature, rain；Transport information includes vehicle density, headstock Spacing, vehicle ratio, counter flow amount；Accident factor includes duration of fault, incident road section capacity；Road conditions bag Include that section is linear, section view, section pavement behavior.

Preferably, described evaluation criterion is: described evaluation criterion is: when evaluation index f_costTime between 0～0.02, The grade of service is " excellent "；When evaluation index f_costTime between 0.02～0.04, the grade of service is " good "；Work as evaluation index f_costTime between 0.04～0.06, the grade of service is " typically "；When evaluation index f_costTime between 0.06～0.08, The grade of service is " poor "；When evaluation index f_costTime between 0.08～0.1, the grade of service is " poor ".

Driving Active Service System offer is taken the initiative in offering a hand and is evaluated, can preferably identify the clothes required for vehicle Business, and this service of active push, optimize the method pushed, and improves service quality and the robustness of system, autgmentability, adaptive Ying Xing, autonomy and real-time.

Accompanying drawing explanation

Fig. 1 is that the car networked environment down train of the present invention is taken the initiative in offering a hand evaluation rubric figure.

Fig. 2 is that the car networked environment down train of the present invention is taken the initiative in offering a hand the evaluation index figure of evaluation system.

Fig. 3 is the classification of service figure of the present invention.

Detailed description of the invention

Below in conjunction with the accompanying drawings and specific embodiment the present invention is further illustrated, but protection scope of the present invention is also It is not limited to this.

Car networked environment down train of the present invention is taken the initiative in offering a hand evaluation system, it is characterised in that include board units (OBU), roadside device (RSU), center, the 4G public network of taking the initiative in offering a hand of driving a vehicle；Board units (OBU) is used for detecting and gathering gas Time information and vehicle internal information.Roadside device (RSU) is used for detecting and gather transport information, accident information, road information, car Receive the time made a response after service, the vehicle fleet serviced, the vehicle fleet serviced by emergency and be subject to The vehicle fleet of general service.The driving center of taking the initiative in offering a hand includes detection module, processing module, computing module, evaluation module, Detection module, detection module quantity of information inside detection and collecting vehicle carrier unit, quantity of information inside roadside device, vehicle-mounted Unit and roadside device interacted by 802.11P agreement after quantity of information, system senses time and system service time. Processing module is for filtering detection module detection and the information collected；Computing module be used for calculating robustness parameter r, Autgmentability parameter e, adaptivity parameter a, autonomy parameter h and real-time parameter t, and real-time parameter is normalized place Reason, real-time parameter t after normalization^*, according to the judgement schematics f (r, e, a, h, the t that have built^*) Calculation Estimation parameter f, take Minima f when one_cost=argminf (r, e, a, h, t^*) as evaluation index；Evaluation module for according to evaluation index and The grade of service in one period is evaluated by the evaluation criterion of systemic presupposition.

The method being evaluated as it is shown in figure 1, car networked environment down train is taken the initiative in offering a hand by described evaluation system, its feature It is, comprises the following steps:

(1) information gathering: board units (OBU) gathers climatic information and vehicle internal information amount；Roadside device (RSU) is used In detection and gather transport information, accident information, road information, vehicle receive make a response after service time, serviced Vehicle fleet, the vehicle fleet serviced by emergency and by the vehicle fleet that services of generality；During driving is taken the initiative in offering a hand The information that heart detection module equipment collecting vehicle carrier unit (OBU), roadside device (RSU) will gather, and calculate quantity of information；

(2) processing module carries out filtration treatment to the information of detection equipment collection, filters unwanted information；Computing module Robustness parameter r, autgmentability parameter e, adaptivity parameter a, autonomy parameter h and real-time parameter is calculated according to remaining information t。

The evaluation index affecting robust performance mainly considers that it is affected by external factor, specifically includes that climate condition, traffic Factor, accident factor, road conditions.The evaluation index affecting scalability and autonomous performance is mainly gross information content, general assignment Time, controlled quentity controlled variable and control time.Type i that evaluation index is service affecting adaptive performance and the vehicle fleet serviced N.Calculate for convenience, according to the degree of danger classification to vehicle, service can be divided into emergency service and general service.Shadow The evaluation index ringing real-time performance is system senses time, system operations time and system service time.The car networking of the present invention Environment down train take the initiative in offering a hand evaluation system evaluation index as shown in Figure 2.

Robustness variable parameter r=CR (1)

In formula, CR is random concordance ratio.Owing to robustness variable parameter result of calculation is a ratio decimal, it is not necessary to enter Row normalized.

The random concordance computational methods than CR are:

The degree of membership value affecting system robustness factor corresponding is as shown in table 1.Phase is drawn by affecting robustness factor Corresponding degree of membership, sets up corresponding matrix A, then obtains weight vector W and eigenvalue of maximum λ according to degree of membership_max, specifically count Calculation process:

AW=λ max (2)

$W_i=\stackrel{\‾}{W_i}/\ΣW_i,(i=1,2,\mathrm{...}n),\stackrel{\‾}{W_i}={(\Πa_{ii})}^{1/n}---\left(3\right)$

λ_max=Σ (AW)_i/nW_i, [AW]=[a] [W]^T (4)

CI=(λ_max-n)/(n-1) (5)

$CR=\frac{CI}{RI}---\left(6\right)$

Wherein, CI is Aver-age Random Consistency Index, and RI is same order Aver-age Random Consistency Index.

Table 1

The evaluation index affecting scalability is mainly gross information content Q, total Mission Time T, controlled quentity controlled variable Q ＇ and control time T ＇.Wherein, gross information content Q is board units detection and the quantity of information Q collected₁, roadside device detection and the information that collects Amount Q₂And board units and roadside device interact after quantity of information Q₃Three's sum.Total Mission Time T is mainly system Detecting period t₁, system operations time t₂With system service time t₃And vehicle receives the time t made a response after service₄ Sum.Controlled quentity controlled variable Q ＇ be driving take the initiative in offering a hand central processing module process after quantity of information.Control time T ＇ is system service Time t₃And vehicle receives the time t made a response after service₄Sum.

Quantity of information Q₁Detected by board units and gather, mainly including car internal information: engine speed, air throttle are opened Degree, gas pedal aperture etc.；Quantity of information Q₂Detected by roadside device and gather, including traffic factor, accident factor and road travel permit Part etc.；Quantity of information Q₃Quantity of information after being interacted by 802.11P agreement for board units and roadside device；Climatic information Including visibility, ice-patch surface, temperature, rain；Transport information includes vehicle density, space headway, vehicle ratio, counter flow Amount.Accident factor includes duration of fault, incident road section capacity；Road conditions includes that section is linear, section view, road Road section surface situation.System senses time t₁With system service time t₃By driving take the initiative in offering a hand Spot detection module detection；System is transported Evaluation time t₂Calculated by take the initiative in offering a hand center computing module of driving；Vehicle receives the time t made a response after service₄By trackside Equipment detects；Controlled quentity controlled variable Q ＇ is carried out processing controls by driving central processing module of taking the initiative in offering a hand.

Autgmentability parameter

Wherein, C_nFor task coefficient of association, Q is gross information content, and Q ＇ is controlled quentity controlled variable, T be total Mission Time, T ＇ for control time Between, m+n=1, Q₁For board units detection and collect quantity of information, Q₂The quantity of information detected for roadside device and collect, Q₃ Quantity of information after interacting for board units and roadside device.Owing to result of calculation is a ratio decimal, it is not necessary to carry out Normalized.

Type i that evaluation index is service affecting adaptive performance and the vehicle fleet N serviced.Count for convenience Calculate, according to the degree of danger classification to vehicle, service can be divided into emergency service and general service.Emergency service is divided into The collision of rear-end impact, emergency brake of vehicle, lateral direction of car, vehicle flat tire etc.；General service is divided into vehicle lane-changing, vehicle to turn Curved, vehicle turns around.The vehicle fleet N serviced by emergency₁, by the vehicle fleet N that services of generality₂With serviced Vehicle fleet N is detected by roadside device.

Adaptivity parameter

N is the vehicle fleet serviced, N₁Vehicle fleet, N for emergency service₂For the vehicle serviced by generality Sum.Owing to result of calculation is a ratio decimal, it is not necessary to be normalized.

Affect the evaluation index of autonomy parameter when being mainly gross information content Q, total Mission Time T, controlled quentity controlled variable Q ＇ and control Between T ＇.

Autonomy parameter

Owing to result of calculation is a ratio decimal, it is not necessary to be normalized.

The evaluation index affecting real-time performance is system senses time t₁, system operations time t₂With system service time t₃。

Real-time parameter t=t₁+t₂+t₃ (11)

Owing to the unit of real-time parameter t result of calculation is s, need to be normalized, practical Z-score standardization Method i.e. gives the average of initial data and standard deviation carries out the standardization of data.Treated data fit standard normal is divided Cloth, i.e. average are 0, and standard deviation is 1, convert function and are:

$t^{*}=\frac{t-\mathrm{\μ}}{\mathrm{\σ}}---\left(12\right)$

To the real-time after robustness parameter r, autgmentability parameter e, adaptivity parameter a, autonomy parameter h and normalization Parameter t^*Give weight k respectively₁、k₂、k₃、k₄And k₅, build judgement schematics f (r, e, a, h, t^*) Calculation Estimation parameter f:

F (r, e, a, h, t^*)=k₁r+k₂e+k₃a+k₄h+k₅t^* (13)

Owing to evaluation system is an entirety, the value giving five kinds of weights should be 1, i.e. k₁+k₂+k₃+k₄+k₅=1.

According to different scenes, the value of five weighted values is different, but affect the robust performance of evaluation system because of Complicated for influence factor, and bigger value should be given, could proof system strong robustness；Requirement of real-time system is from perception The shortest to service ending time, minima should be given；Autonomy and autgmentability have certain relatedness.Therefore, five kinds of weights Value is allocated as follows:

k₁>=0.3, k₂+k₄=0.4, k₅≤0.1

By robustness parameter r obtained in real time, autgmentability parameter e, adaptivity parameter a, autonomy parameter h and normalization After real-time parameter t* bring judgement schematics into, and to seek the minima of period inner evaluation parameter f be evaluation index,

f_cost=argminf (r, e, a, h, t^*) (14)

(3) in evaluation module, evaluation criterion is preset as shown in table 2, according to evaluation index f_costClothes are drawn with evaluation criterion The grade of business.

Table 2

Evaluating fcost	The grade of service
		0～0.02	Excellent
0.02～0.04	Good
		0.04～0.06	Typically
0.06～0.08	Poor
		0.08～0.1	Difference

Described embodiment be the present invention preferred embodiment, but the present invention is not limited to above-mentioned embodiment, not In the case of deviating from the flesh and blood of the present invention, any conspicuously improved, the replacement that those skilled in the art can make Or modification belongs to protection scope of the present invention.

Claims (6)

1. a car networked environment down train is taken the initiative in offering a hand evaluation system, it is characterised in that include board units (OBU), trackside Equipment (RSU), center, the 4G public network of taking the initiative in offering a hand of driving a vehicle；

Board units (OBU) is used for detecting and gather climatic information and vehicle internal information；

Roadside device (RSU) is used for detecting and gather transport information, accident information, road information, vehicle do after receiving service Go out the time of reaction, the vehicle fleet serviced, the vehicle fleet serviced by emergency and the vehicle serviced by generality Sum；

The driving center of taking the initiative in offering a hand includes detection module, processing module, computing module, evaluation module, detection module, detection module Quantity of information inside detection and collecting vehicle carrier unit, quantity of information, board units and roadside device inside roadside device lead to Spend the quantity of information after 802.11P agreement interacts, system senses time and system service time；Processing module is for inspection Survey module detection and the information collected filters；Computing module is used for calculating robustness parameter r, autgmentability parameter e, adaptive Answering property parameter a, autonomy parameter h and real-time parameter t, and real-time parameter is normalized, the reality after normalization Time property parameter t^*, according to the judgement schematics f (r, e, a, h, the t that have built^*) Calculation Estimation parameter f, take the minimum in the period Value f_cost=argminf (r, e, a, h, t^*) as evaluation index；Evaluation module is for commenting according to evaluation index and systemic presupposition Price card is accurate to be evaluated the grade of service in the period.

2. a car networked environment down train is taken the initiative in offering a hand evaluation methodology, it is characterised in that comprise the following steps:

(1) information gathering: board units (OBU) gathers climatic information and vehicle internal information amount；Roadside device (RSU) is used for examining Survey and collection transport information, accident information, road information, vehicle receive the time made a response after service, the car serviced Sum, the vehicle fleet serviced by emergency and the vehicle fleet serviced by generality；Driving take the initiative in offering a hand center inspection The information that survey module device collecting vehicle carrier unit (OBU), roadside device (RSU) will gather, and calculate quantity of information；

(2) processing module carries out filtration treatment to the information of detection equipment collection, filters unwanted information；Computing module according to Remaining information calculates robustness parameter r, autgmentability parameter e, adaptivity parameter a, autonomy parameter h and real-time parameter t,

Robustness variable parameter r=CR (1)

Autgmentability parameter

Adaptivity parameter

Autonomy parameter

Real-time parameter t=t₁+t₂+t₃ (11)

In formula, CR is random concordance ratio；Q is gross information content, T be total Mission Time, Q ＇ be controlled quentity controlled variable, T ＇ is the control time, m+ N=1, C_nFor task coefficient of association, Q₁For board units detection and collect quantity of information, Q₂For roadside device detection and collection The quantity of information arrived, Q₃Quantity of information after interacting for board units and roadside device, t₁For system senses time, t₂For being System operation time, t₃For system service time, t₄The time made a response after service is received for vehicle；N is the vehicle serviced Sum, N₁Vehicle fleet, N for emergency service₂For the vehicle fleet serviced by generality；

And real-time parameter t is normalized,

$t*=\frac{t-\mathrm{\μ}}{\mathrm{\σ}}---\left(12\right)$

To the real-time parameter after robustness parameter r, autgmentability parameter e, adaptivity parameter a, autonomy parameter h and normalization t^*Give weight k respectively₁、k₂、k₃、k₄And k₅, build judgement schematics f (r, e, a, h, t^*) Calculation Estimation parameter f:

F (r, e, a, h, t^*)=k₁r+k₂e+k₃a+k₄h+k₅t^* (13)

Wherein k₁+k₂+k₃+k₄+k₅=1；

After robustness parameter r obtained in real time, autgmentability parameter e, adaptivity parameter a, autonomy parameter h and normalization Real-time parameter t* brings judgement schematics into, and to seek the minima of period inner evaluation parameter f be evaluation index,

f_cost=argminf (r, e, a, h, t^*) (14)

(3) in evaluation module, evaluation criterion is preset, according to evaluation index f_costWith the grade that evaluation criterion draws service.

Car networked environment down train the most according to claim 1 is taken the initiative in offering a hand evaluation methodology, it is characterised in that described weight k₁、k₂、k₃、k₄And k₅Value be k₁>=0.3, k₂+k₄=0.4, k₅≤0.1。

Car networked environment down train the most according to claim 1 is taken the initiative in offering a hand evaluation methodology, it is characterised in that the most consistent The property computational methods than CR are:

Drawn corresponding degree of membership by affecting robustness factor, set up corresponding matrix A, then according to degree of membership obtain power to Amount W and eigenvalue of maximum λ_max, specifically calculate process:

AW=λ_max (2)

$W_i=\stackrel{\‾}{W_i}/{\mathrm{\ΣW}}_i(i=1,2,\mathrm{...},n),\stackrel{\‾}{W_i}={(\Πa_{ii})}^{1/n}---\left(3\right)$

λ_max=Σ (AW)_i/nW_i, [AW]=[a] [W]^T (4)

CI=(λ_max-n)/(n-1) (5)

$CR=\frac{CI}{RI}---\left(6\right)$

Wherein, CI is Aver-age Random Consistency Index, and RI is same order Aver-age Random Consistency Index.

Car networked environment down train the most according to claim 1 is taken the initiative in offering a hand evaluation methodology, it is characterised in that climatic information Including visibility, ice-patch surface, temperature, rain；Transport information includes vehicle density, space headway, vehicle ratio, counter flow Amount；Accident factor includes duration of fault, incident road section capacity；Road conditions includes that section is linear, section view, road Road section surface situation.

Car networked environment down train the most according to claim 1 is taken the initiative in offering a hand evaluation methodology, it is characterised in that described evaluation Standard is: when evaluation index f_costTime between 0～0.02, the grade of service is " excellent "；When evaluation index f_costBetween 0.02～ Time between 0.04, the grade of service is " good "；When evaluation index f_costTime between 0.04～0.06, the grade of service is " one As "；When evaluation index f_costTime between 0.06～0.08, the grade of service is " poor "；When evaluation index f_costBetween 0.08 ～time between 0.1, the grade of service is " poor ".