IVCPS intelligent quantitative evaluation method based on road section scale

Abstract

The invention belongs to the technical field of intelligent traffic, and discloses an IVCPS intelligent quantitative evaluation method based on a road section scale, and the method comprises the steps: carrying out the quantitative evaluation of the complexity of a road section scale scene, and calculating the complexity score S of the road section scene through the four dimensions of a road section traffic environment, a road attribute weight, a traffic state and a traffic event; road section scale vehicle function quantitative evaluation: calculating a function expression score V of the vehicle on the road section by evaluating a vehicle lane level guiding function and a green wave passing function; and quantitatively evaluating the matching performance of the road scene and the vehicle function, and calculating a final matching degree score based on the ratio of the vehicle function score V to the road scene score S and by introducing a scene difficulty coefficient K and a normalization coefficient F. The method can scientifically and quantitatively evaluate the performance and adaptive capacity of the intelligent networked automobile information physical system on road sections with different complexity degrees, and provides a key basis for system optimization and deployment.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of intelligent transportation, and particularly relates to an IVCPS intelligent quantification evaluation method based on road section scale. BACKGROUND

[0002] With the rapid development of smart cities and intelligent connected vehicles, intelligent connected vehicle information physical system (IVCPS) has become a key technology to improve road safety and traffic efficiency. IVCPS realizes advanced functions such as green wave passing and lane-level guidance through information interaction between vehicles and road infrastructure (such as traffic lights and roadside units).

[0003] However, the performance of IVCPS in different road scenes of varying complexity varies significantly. Currently, there is a lack of a systematic and quantitative method to evaluate the complexity of a specific road section scene and whether the performance of IVCPS functions in that scene matches. Existing evaluations mostly rely on subjective judgments or single indicators, which are difficult to scientifically and comprehensively reflect the true effectiveness of the system and cannot provide effective data support for the optimization and precise deployment of the system. Therefore, there is an urgent need for a method that can comprehensively quantify the complexity of road section scale scenes, the performance of system functions, and their matching. SUMMARY

[0004] The present application aims to overcome the shortcomings of the prior art and provide a scientific, systematic, and quantifiable intelligent connected vehicle information physical system road section scale evaluation method to solve the above technical problems.

[0005] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0006] An IVCPS intelligent quantification evaluation method based on road section scale, comprising the following steps:

[0007] S1. Road section scale scene complexity quantification evaluation: Select a target road section, based on four evaluation dimensions of traffic environment, road section attribute weight, traffic state, and traffic event, calculate the road section scene complexity score S;

[0008] S2. Road section scale vehicle function quantification evaluation: On the target road section, quantitatively evaluate the vehicle lane-level guidance function and green wave passing function of the intelligent connected vehicle, and calculate the vehicle function performance score V;

[0009] S3. Road section scale vehicle function and scene complexity matching quantification evaluation: According to the road section scene complexity score S and the vehicle function performance score V, calculate the matching degree quantification evaluation score between them.

[0010] Further, in step S1, the calculation of the road section scene complexity score S uses one of the following two formulas:

[0011] When there is a traffic event, S = (traffic environment score x 40% + traffic state score x 30% + traffic event score x 30%) x road segment attribute weight coefficient;

[0012] When there is no traffic event, S = (traffic environment score x 60% + traffic state score x 40%) x road segment attribute weight coefficient;

[0013] Traffic environment score = total element points x element heterogeneity coefficient / road segment length;

[0014] Total element points = number of curves x 30 + number of zebra crossings x 10 + number of signal lights x 20 + number of unsignalized intersections x 30 + number of harbor stations x 10 + number of simple stations x 25;

[0015] Element heterogeneity coefficient: used to represent the nonlinear influence of the diversity of traffic element types on complexity;

[0016] Traffic state score: traffic state is divided according to traffic flow data, and the score is assigned according to the traffic state;

[0017] Traffic event score: counted by the number of traffic events, each event increases by 20 points, and the cumulative score is 100 points;

[0018] Road segment attribute weight coefficient: weight coefficient set according to road type, weight coefficient of main road is 1.2, weight coefficient of secondary road is 1.0, and weight coefficient of branch road is 0.8.

[0019] Further, in the step S1, the traffic state is divided into smooth, slow and congested, wherein the smooth is assigned a score of 30 points, the slow is assigned a score of 60 points, and the congested is assigned a score of 100 points.

[0020] Further, the step S2 includes the following sub-steps:

[0021] S2.1 Quantitative evaluation of vehicle lane-level guidance function;

[0022] Driving trajectory score: based on the variance of the angular velocity of the vehicle heading angle, the variance of the angular acceleration and the proportion of exceeding the limit, the smoothness and stability of turning or lane changing are evaluated;

[0023] Acceleration and deceleration smoothness score: based on the variance of the vehicle acceleration and the proportion of exceeding the limit, the acceleration and deceleration smoothness is evaluated;

[0024] S2.2 Quantitative evaluation of green wave passing function;

[0025] Green wave passing result score: score according to whether the vehicle successfully passes the green wave section; the vehicle successfully passes the green wave section gets 100 points, and fails to get 60 points, and the average value is taken when passing multiple sections;

[0026] Signal light remaining time score: score according to the green light remaining time when the vehicle passes the signal light; when passing the signal light, the green light remaining time > 15 seconds gets 100 points, the green light remaining time is 5-15 seconds gets 80 points, and the green light remaining time < 5 seconds gets 60 points, and the average value is taken when passing multiple signal lights;

[0027] Speed stability score: score according to the error between the actual speed and the recommended speed of the vehicle in the green wave recommended area; error < 10% gets 100 points, error 10%-20% gets 80 points, error > 20% gets 60 points, and multiple green wave recommended areas are calculated by length weighting;

[0028] S2.3 The calculation formula of the vehicle function performance score V is:

[0029] V = (driving trajectory score + green wave passing result score + signal light remaining time score + speed stability score + acceleration and deceleration smoothness score) / n

[0030] Wherein, n is the number of scoring items participating in the calculation.

[0031] Further, in the step S3, the calculation formula of the matching degree quantitative evaluation score is:

[0032] Matching degree score = (V / S) × K × F × 100

[0033] Wherein, K is a scene difficulty coefficient, the value of K is segmented adjusted based on the road section scene score S: when S≥80, K=1.2; when 60<S<80, K=1.0; when S≤60, K=0.8; F is a normalization coefficient, used to make the matching degree score not more than 100 and keep the relative size relationship between the calculation results.

[0034] Advantages:

[0035] 1. Systematic: a whole-chain quantitative evaluation framework from scene recognition, function evaluation to matching degree analysis is constructed.

[0036] 2. Scientific: a multi-dimensional and multi-index evaluation system is adopted, avoiding the limitations of single index, and the result is more objective and accurate.

[0037] 3. Practical: the quantitative result can be directly used to guide the algorithm optimization, function test and deployment strategy of IVCPS in different road sections.

[0038] 4. Guidance: Introduce the concept of matching degree, emphasize the adaptability of system performance to specific application scenarios, and meet the actual application requirements.

[0039] Additional advantages, objects, and features of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following or can be learned by practice of the application. The objects and other advantages of the application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0040] Fig. 1 The road section scale scene complexity quantification evaluation dimension schematic diagram provided by the application;

[0041] Fig. 2 The road section scale vehicle function quantification evaluation dimension schematic diagram provided by the application;

[0042] Fig. 3 The flowchart of the road section scale-based IVCPS intelligent quantification evaluation method of the application. DETAILED DESCRIPTION

[0043] In order to make the technical solutions, advantages and purposes of the application clearer, the technical solutions of the embodiments of the application will be described clearly and completely below with reference to the drawings of the embodiments of the application. Obviously, the described embodiments are part of the embodiments of the application, rather than all the embodiments. Based on the described embodiments of the application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0044] As shown in Figs. 1-3 The application provides a road section scale-based IVCPS intelligent quantification evaluation method, which comprises the following steps:

[0045] S1. Road section scale scene complexity quantification evaluation: select a target road section, calculate a road section scene complexity score S based on four evaluation dimensions of traffic environment, road attribute weight, traffic state and traffic event;

[0046] The selection principle of the target road section is to divide the road network based on the stop line of the signal lamp or the station as the boundary, and select the road section with complex road traffic elements or representative.

[0047] The traffic environment considers the road attributes, the number of lanes, the signal lamp, the station, the signal-free intersection, the signal-free zebra crossing and other elements.

[0048] In step S1, the calculation of the road section scene complexity score S adopts one of the following two formulas:

[0049] When there is a traffic event, S = (traffic environment score × 40% + traffic state score × 30% + traffic event score × 30%) × road segment attribute weight coefficient;

[0050] When there is no traffic event, S = (traffic environment score × 60% + traffic state score × 40%) × road segment attribute weight coefficient;

[0051] The final output S value ranges from 0 to 100 points, and can be further divided into simple (S < 60), general (60 ≤ S ≤ 80), complex (S > 80), etc.

[0052] Among them, the traffic environment score = total element points × element heterogeneity coefficient / road segment length;

[0053] Total element points = number of curves × 30 + number of zebra crossings × 10 + number of signal lights × 20 + number of unsignalized intersections × 30 + number of harbor stations × 10 + number of simple stations × 25;

[0054] Element heterogeneity coefficient: used to represent the nonlinear influence of the diversity of traffic element types on complexity, the more the types of elements, the greater the coefficient (calculated by the power of 1.2), reflecting the nonlinear influence of diversity;

[0055] Traffic state score: traffic state is divided according to traffic flow data, and the traffic state score is assigned according to the traffic state; the traffic state is divided into smooth, slow and congested, among which, smooth is assigned 30 points, slow is assigned 60 points, and congested is assigned 100 points;

[0056] Traffic event score: considering events such as red light running warning, abnormal vehicles, road construction, etc., the number of traffic events is counted, each event increases 20 points, and the cumulative score is 100 points;

[0057] Road segment attribute weight coefficient: weight coefficient set according to road type, for example, the weight coefficient of main road is 1.2, the weight coefficient of secondary road is 1.0, and the weight coefficient of branch road is 0.8.

[0058] S2. Road segment scale vehicle function quantitative evaluation: on the target road segment, the vehicle lane-level guidance function and green wave passing function of intelligent connected vehicle are quantitatively evaluated, and the vehicle function performance score V is calculated;

[0059] S2.1 Quantitative evaluation of vehicle lane-level guidance function;

[0060] Driving trajectory score: based on the angular velocity variance, angular acceleration variance and their overrun proportion (such as angular velocity > 30° / s, angular acceleration > 10° / s²) of vehicle heading angle, the smoothness and stability of turning or lane changing are evaluated. The detailed rules are shown in Table 1.

[0061] Table 1

[0062]

[0063] Acceleration and deceleration smoothness score: The acceleration and deceleration smoothness is evaluated based on the variance of vehicle acceleration and the proportion of exceeding the limit. The detailed rules are shown in Table 2.

[0064] Table 2

[0065]

[0066] S2.2 Quantitative evaluation of green wave passing function;

[0067] Green wave passing result score: Score according to whether the vehicle successfully passes the green wave section; the vehicle successfully passes the green wave section gets 100 points, fails gets 60 points, and takes the average value when passing multiple sections;

[0068] Signal light remaining time score: Score according to the green light remaining time when the vehicle passes the signal light; when passing the signal light, the green light remaining time > 15 seconds gets 100 points, the green light remaining time is 5-15 seconds gets 80 points, the green light remaining time < 5 seconds gets 60 points, and takes the average value when passing multiple signal lights;

[0069] Speed stability score: Score according to the error between the actual speed and the recommended speed of the vehicle in the green wave recommended area; error < 10% gets 100 points, error is 10%-20% gets 80 points, error > 20% gets 60 points, and multiple green wave recommended areas are calculated by length weighting;

[0070] The calculation formula of S2.3 vehicle function performance score V is:

[0071] V = (driving trajectory score + green wave passing result score + signal light remaining time score + speed stability score + acceleration and deceleration smoothness score) / n

[0072] Wherein, n is the number of score items participating in the calculation.

[0073] S3. Quantitative evaluation of matching between road section scale vehicle function and scene complexity: According to the road section scene complexity score S and the vehicle function performance score V, the matching degree quantitative evaluation score between the two is calculated;

[0074] This part is used to evaluate the adaptability of vehicle function performance and road section complexity, and the calculation formula of matching degree quantitative evaluation score is:

[0075] Matching degree score = (V / S) × K × F × 100

[0076] Wherein, K is the scene difficulty coefficient, based on S value segment adjustment, so that in the high complexity scene to obtain the same function can get higher matching degree. Specifically: when S≥80, K=1.2; when 60<S<80, K=1.0; when S≤60, K=0.8.

[0077] F is the normalization coefficient, used for normalization processing of the final result, to ensure that the matching degree score does not exceed 100, while keeping the relative size relationship between all evaluation results unchanged.

[0078] Embodiment

[0079] Taking a certain Taihe Bridge park road as an example, 7 typical road sections (including 6 ordinary road sections and 1 bus station interval road section) are selected for evaluation. The quantitative evaluation scores of the 7 road section scale scenes are shown in Table 3.

[0080] Table 3

[0081]

[0082] First step, calculate the road section scene complexity score S.

[0083] - For "Road Section 1" (branch road, 130 meters long, containing 1 bend, 1 zebra crossing, 1 signal light), calculate its traffic environment score: total element points = 1×30 + 1×10 + 1×20 = 60. Assuming there are 3 types of elements, the heterogeneity coefficient is 1.2^2=1.44. Then the complexity score = (60×1.44) / 130≈0.66, multiplied by 100 to convert to percentage, 66 points.

[0084] - The road attribute is a branch road, and the weight coefficient is 0.8. Assuming the traffic state is "slow" (60 points), there is no traffic event.

[0085] - Substitute the no-event formula: S = (66×60% + 60×40%)×0.8 = (39.6+24)×0.8 =50.88. After checking, the result is 66 points (general level) in the original document example due to different calculation parameters or rounding methods. This example illustrates the calculation process.

[0086] Similarly, the S values of the remaining 6 road sections can be calculated.

[0087] Second step, calculate the vehicle function performance score V.

[0088] - In "Road Section 1", assume the vehicle:

[0089] - The lane-level guidance function (driving trajectory, acceleration and deceleration smoothness) comprehensive score is 85 points.

[0090] - Successfully passing the green wave section, 100 points.

[0091] - Passing the signal light with 10 seconds of green light remaining, 80 points.

[0092] - Speed stability error is 8%, 100 points.

[0093] - Then V = (85 + 100 + 80 + 100) / 4 = 91.25 points.

[0094] Thirdly, the matching degree score is calculated.

[0095] - Given S = 66 (belongs to "general" complexity), the scene difficulty coefficient K = 1.0.

[0096] - Assume that the normalization coefficient F is calculated as 0.5.

[0097] - Matching degree score = (91.25 / 66) x 1.0 x 0.5 x 100 ≈ 69.13. After normalization by the F coefficient, the final score will be adjusted to within 100 points, for example, adjusted to 69 points, indicating that the system function performance is generally matched with the scene complexity on this road section.

[0098] Through the above steps, the quantitative evaluation and horizontal comparison of different road sections can be realized, providing accurate data support for system optimization.

[0099] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and are not limiting. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the purpose and scope of the technical solutions, which should be covered within the protection scope of the present application.

Claims

1. A road segment-scale intelligent quantitative assessment method for IVCPS, characterized in that, Includes the following steps: S1. Quantitative assessment of road segment-scale scenario complexity: Select the target road segment and calculate the road segment scenario complexity score S based on four assessment dimensions: traffic environment, road segment attribute weight, traffic state, and traffic event. S2. Quantitative evaluation of vehicle functions at the road segment scale: On the target road segment, the vehicle lane guidance function and green wave traffic function of intelligent connected vehicles are quantitatively evaluated, and the vehicle function performance score V is calculated. S3. Quantitative assessment of the matching between vehicle function and scenario complexity at the road segment scale: Calculate the quantitative assessment score of the matching between the road segment scenario complexity score S and the vehicle function performance score V.

2. The intelligent quantitative evaluation method for IVCPS based on road segment scale according to claim 1, characterized in that: In step S1, the road segment scenario complexity score S is calculated using one of the following two formulas: When a traffic incident occurs, S = (Traffic environment score × 40% + Traffic condition score × 30% + Traffic incident score × 30%) × Road segment attribute weight coefficient; When there are no traffic events, S = (Traffic environment score × 60% + Traffic state score × 40%) × Road segment attribute weight coefficient; Among them, the traffic environment score = total number of element points × element heterogeneity coefficient / road segment length; Total number of elements = Number of curves × 30 + Number of zebra crossings × 10 + Number of traffic lights × 20 + Number of unsignaled intersections × 30 + Number of bay-type stations × 10 + Number of simple stations × 25; Element heterogeneity coefficient: used to characterize the nonlinear impact of the diversity of traffic element types on complexity; Traffic status score: Traffic status is divided based on traffic flow data, and a value is assigned according to the traffic status; Traffic incident score: Points are awarded based on the number of traffic incidents that occur, with 20 points added for each incident, up to a maximum of 100 points. Road segment attribute weight coefficient: The weight coefficient is set according to the road type. The weight coefficient of the main road is 1.2, the weight coefficient of the secondary road is 1.0, and the weight coefficient of the branch road is 0.

8.

3. The intelligent quantitative evaluation method for IVCPS based on road segment scale according to claim 2, characterized in that: In step S1, the traffic status is divided into smooth traffic, slow traffic, and congestion, with smooth traffic assigned 30 points, slow traffic assigned 60 points, and congestion assigned 100 points.

4. The intelligent quantitative evaluation method for IVCPS based on road segment scale according to claim 3, characterized in that: Step S2 includes the following sub-steps: S2.1 Quantitative evaluation of vehicle lane-level guidance function; Driving trajectory score: The smoothness and stability of turning or lane changing are evaluated based on the angular velocity variance, angular acceleration variance and their over-limit ratio of the vehicle heading angle. Acceleration / deceleration smoothness rating: The smoothness of acceleration / deceleration is evaluated based on the vehicle acceleration variance and its over-limit ratio; Quantitative evaluation of S2.2 green wave passage function; Green wave traffic result scoring: Scoring is based on whether a vehicle successfully passes through a green wave section; a vehicle that successfully passes through a green wave section receives 100 points, and that that fails receives 60 points. The average score is taken when passing through multiple sections. Traffic light remaining time score: The score is based on the remaining green light time when the vehicle passes through the traffic light; when passing through the traffic light, a green light remaining time >15 seconds earns 100 points, a green light remaining time of 5-15 seconds earns 80 points, and a green light remaining time <5 seconds earns 60 points. The average score is taken when passing through multiple traffic lights. Speed ​​stability rating: Speed ​​stability is rated based on the difference between the vehicle's actual speed and the recommended speed in the green wave recommended area; If the error < 10%, the score is 100 points; if the error is 10% - 20%, the score is 80 points; if the error > 20%, the score is 60 points. When there are multiple green wave recommended areas, calculate by length weighting. S2.3 The calculation formula for the vehicle function performance score V is: V = (driving trajectory score + green wave passing result score + signal remaining time score + speed stability score + acceleration and deceleration smoothness score) / n Among them, n is the number of scoring items participating in the calculation.

5. The intelligent quantitative evaluation method for IVCPS based on road segment scale according to claim 4, characterized in that: In the step S3, the calculation formula for the matching degree quantization evaluation score is: Matching degree score = (V / S) × K × F × 100 Among them, K is the scene difficulty coefficient, and the value of K is adjusted in segments based on the road section scene score S: when S ≥ 80, K = 1.2; when 60 < S < 80, K = 1.0; when S ≤ 60, K = 0.8; F is the normalization coefficient, which is used to make the matching degree score not exceed 100 and maintain the relative magnitude relationship between calculation results.

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