A method and system for predicting the impact of different driving behaviors on emissions for a vehicle

Abstract

The application discloses a kind of methods and systems for predicting the influence of different driving behaviors on emissions, comprising: constructing fuzzy cellular automaton model of signal control section pedestrian crossing and establishing localized vehicle emission factor library;Further, different driving behavior scenarios are simulated to obtain driving behavior and corresponding vehicle second-by-second running parameters;Combined with the localized vehicle emission factor library, the first emission of the vehicle is calculated according to the vehicle second-by-second running parameters;According to the first emission of all vehicles, the total emission of all vehicles in the preset time period is calculated;According to the first emission and the total emission, the influence of driving behavior on emissions is predicted.The application can evaluate and predict the influence of different driving behaviors on emissions, and since fuzzy cellular automaton model and localized vehicle emission factor library are constructed for evaluation and prediction, the decision of a single driver can be considered, with high prediction accuracy and good results, and can be widely applied in the field of computer technology.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, and in particular to a method and system for predicting the influence of different driving behaviors on emissions. BACKGROUND

[0002] With the accelerating urbanization process, the number of motor vehicles continues to rise, and traffic congestion and motor vehicle pollutant emissions are increasingly prominent. The unsignalized pedestrian crossing is an area where people are concentrated, and the exhaust of motor vehicles here may directly endanger the health of pedestrians crossing the street. Therefore, in-depth understanding of the driving decision-making process before the unsignalized pedestrian crossing and the interaction between vehicles and pedestrians is beneficial to analyzing the relationship between vehicle operating state and emissions, and has certain theoretical and practical significance for reducing motor vehicle exhaust emissions.

[0003] The decision-making of the driver before the unsignalized pedestrian crossing is fuzzy and uncertain, and the existing prediction method adopts the way of user-defined pedestrian priority rules, which makes the vehicle completely yield to the pedestrian. All drivers comply with traffic rules, which is too idealized, cannot represent the decision-making process of individual drivers, and thus cannot evaluate the influence of different driving behaviors on emissions due to different degrees of courtesy to pedestrians before the unsignalized pedestrian crossing, resulting in poor prediction effect on emissions. SUMMARY

[0004] The present application aims to at least solve one of the technical problems in the related art. To this end, the present application provides a method and system for predicting the influence of different driving behaviors on emissions with good prediction effect.

[0005] In one aspect, the present application provides a method for predicting the influence of different driving behaviors on emissions, comprising:

[0006] constructing a fuzzy cellular automaton model of an unsignalized pedestrian crossing on a road section, and establishing a localized vehicle emission factor library;

[0007] using the fuzzy cellular automaton model, simulating different driving behavior scenarios to obtain driving behaviors and corresponding vehicle second-by-second operating parameters of the driving behaviors;

[0008] combining the localized vehicle emission factor library, calculating a first emission amount of the vehicle according to the vehicle second-by-second operating parameters;

[0009] calculating the total emission amount of all the vehicles on the unsignalized road section within a preset time period according to the first emission amount of all the vehicles;

[0010] predicting the influence of driving behaviors on emissions according to the first emission amount and the total emission amount.

[0011] Optionally, the fuzzy cellular automaton model of the no-signal control road section pedestrian crossing comprises:

[0012] a vehicle model, the vehicle model consisting of a vehicle courtesy decision and a vehicle motion behavior;

[0013] a pedestrian model, the pedestrian model consisting of a pedestrian crossing decision and a pedestrian motion behavior;

[0014] a membership function for determining the fuzzy filtering decision of a single driver who does not give way to pedestrians before the pedestrian crossing.

[0015] Optionally, the step of establishing a localized vehicle emission factor library comprises:

[0016] determining a measured vehicle, obtaining measured operating parameters and measured emission results of the measured vehicle;

[0017] calculating the specific power of the measured vehicle according to the measured operating parameters;

[0018] According to the specific power and the instantaneous speed of the vehicle, the vehicle operating state is divided into several working condition intervals;

[0019] According to the measured emission results, the average basic emission rate of each working condition interval is calculated;

[0020] According to the instantaneous speed of the measured vehicle and the average basic emission rate, the emission factor of the working condition interval is calculated;

[0021] All the emission factors are collected into a localized vehicle emission factor library.

[0022] Optionally, the fuzzy cellular automaton model is used to simulate different driving behavior scenarios to obtain driving behavior and vehicle second-by-second operating parameters corresponding to the driving behavior, including:

[0023] The simulation data of vehicles and pedestrians are randomly generated using normal distribution;

[0024] Based on the pedestrian dynamic gap acceptance model, the pedestrian crossing decision is determined according to the lane gap and the walking time of the pedestrian;

[0025] Based on the membership function, the fuzzy filtering decision of the driver before the pedestrian crossing is determined according to the pedestrian gap and the pedestrian speed; the motion of the vehicle is controlled according to the fuzzy filtering decision, and the operating parameters of the vehicle are recorded.

[0026] Optionally, the first emission amount of the vehicle is calculated according to the vehicle second-by-second operating parameters and the emission factor, comprising:

[0027] According to the actual instantaneous speed and instantaneous acceleration of the vehicle, the second-by-second specific power value is calculated;

[0028] matching a basic emission rate of the operating condition from a localized emission factor library according to the per-second specific power value;

[0029] calculating a first emission amount of the vehicle from entering a road section to leaving the road section according to the basic emission rate and a vehicle operating time.

[0030] Optionally, the predicting the influence of the driving behavior on the emission according to the first emission amount and the total emission amount comprises:

[0031] combining the driving condition with the relationship of each driving behavior scenario, calculating a second emission amount of each driving behavior scenario according to the first emission amount;

[0032] determining an emission difference of each emission under different driving behavior scenarios according to the second emission amount, and obtaining the influence of the driving behavior on the emission.

[0033] Optionally, the expression of the membership function is:

[0034]

[0035]

[0036] f2 = 1 - f1 - f3

[0037] wherein f1 represents a short gap and a slow pedestrian speed, f2 represents a medium gap and a medium pedestrian speed, and f3 represents a long gap and a fast pedestrian speed; parameter x c1 is the abscissa of the center of the peak of the short gap and slow speed curve, parameter x c3 is the abscissa of the center of the peak of the long gap and fast speed curve; parameter w1 is the standard deviation of the short gap and slow speed, and parameter w3 is the standard deviation of the long gap and fast speed curve; a is the abscissa at which the membership degree of the short gap and slow speed starts to be not 1, b is the abscissa at which the membership degree of the medium gap and medium speed is 1, and c is the abscissa at which the membership degree of the long gap and fast speed starts to be 1.

[0038] In another aspect, the embodiment of the present application also provides a system for predicting the influence of different driving behaviors of a vehicle on emission, comprising:

[0039] a first module configured to construct a fuzzy cellular automaton model of a pedestrian crossing of a non-signal-controlled road section, and establish a localized vehicle emission factor library;

[0040] a second module configured to simulate the pedestrian motion condition and the vehicle motion condition by using the fuzzy cellular automaton model, and obtain driving behaviors and corresponding vehicle per-second operating parameters of the driving behaviors;

[0041] a third module configured to calculate a first emission amount of the vehicle according to the second running parameter of the vehicle in combination with the localized vehicle emission factor library;

[0042] a fourth module configured to calculate a total emission amount of all the vehicles on the unsignalized road section within a preset time period according to the first emission amount of all the vehicles;

[0043] a fifth module configured to predict an impact of the driving behavior on the emission according to the first emission amount and the total emission amount.

[0044] In another aspect, an embodiment of the present application also provides an electronic device, comprising a processor and a memory, the memory is configured to store a program, and the processor is configured to execute the program to implement the method as described above.

[0045] In another aspect, an embodiment of the present application also provides a computer storage medium, which stores a program executable by a processor, and the program executable by the processor is configured to implement the method as described above when executed by the processor.

[0046] The embodiment of the present application has the following beneficial effects: the embodiment of the present application can realize evaluation and prediction of the impact of different driving behaviors on the emission by constructing a fuzzy cellular automaton model of the pedestrian crossing of the unsignalized road section, establishing a localized vehicle emission factor library, simulating different driving behavior scenarios by using the fuzzy cellular automaton model, obtaining the driving behavior and the second running parameter of the vehicle corresponding to the driving behavior, calculating the first emission amount of the vehicle according to the second running parameter of the vehicle in combination with the localized vehicle emission factor library, calculating the total emission amount of all the vehicles on the unsignalized road section within a preset time period according to the first emission amount of all the vehicles, and predicting the impact of the driving behavior on the emission according to the first emission amount and the total emission amount. BRIEF DESCRIPTION OF DRAWINGS

[0047] The accompanying drawings are used to provide a further understanding of the technical solutions of the present application, and constitute a part of the specification, and are used to explain the technical solutions of the present application together with the embodiments of the present application, and do not constitute a limitation to the technical solutions of the present application.

[0048] Figure 1 is a step diagram of the method for predicting the impact of different driving behaviors of the vehicle on the emission provided by the embodiment of the present application;

[0049] Figure 2 is a model structure diagram of the MOVES model provided by the embodiment of the present application;

[0050] Figure 3is a workflow diagram of the fuzzy cellular automata model provided by the embodiment of the present application;

[0051] Figure 4 is a structural schematic diagram of the system for predicting the influence of different driving behaviors of a vehicle on emissions provided by the embodiment of the present application;

[0052] Figure 5 is a structural schematic diagram of an electronic device provided by the embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0054] It should be noted that although the functional modules are divided in the system schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a manner different from the module division in the system or the order in the flowchart. The terms "first / S100", "second / S200" and the like in the specification and claims and the above-described drawings are used to distinguish similar objects and do not necessarily describe a specific order or sequence.

[0055] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearance of the phrase in various places in the specification does not necessarily all refer to the same embodiment, nor is it necessarily independent or alternative embodiments to other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] In view of at least one problem existing in the prior art, the embodiments of the present application provide a method and system for predicting the influence of different driving behaviors of a vehicle on emissions. First, a method for predicting the influence of different driving behaviors of a vehicle on emissions is introduced.

[0057] Reference Figure 1 , Figure 1 is a step diagram of the method for predicting the influence of different driving behaviors of a vehicle on emissions provided by the embodiment of the present application, and the method of the embodiment of the present application can include but is not limited to the following steps S100-S500.

[0058] S100, construct a fuzzy cellular automata model of a signal-free control section pedestrian crossing, and establish a localized vehicle emission factor library.

[0059] Specifically, step S100 includes the following S110-S120.

[0060] S110, construct a fuzzy cellular automaton model of a signalized crosswalk.

[0061] The embodiment of the application constructs a fuzzy cellular automaton model of a signalized crosswalk, which comprises a vehicle model, a pedestrian model and a membership function of a fuzzy inference system, the vehicle model is composed of a vehicle courtesy decision and a vehicle motion behavior; the pedestrian model is composed of a pedestrian crossing decision and a pedestrian motion behavior; and the membership function is used to determine a fuzzy filtering decision of a single driver who does not show courtesy to pedestrians in front of a crosswalk. The fuzzy cellular automaton model can simulate the decision-making process of a single driver in front of a crosswalk, and further simulate the interaction between vehicles and pedestrians at a crosswalk of a multi-lane signalized road.

[0062] The fuzzy cellular automaton model of the embodiment of the application is realized by combining a cellular automaton and a fuzzy logic inference.

[0063] For the cellular automaton, the cellular automaton of the embodiment of the application is used to update the motion information of pedestrians and vehicles, and the updating mode of vehicle motion is as follows:

[0064] (1) acceleration [v n (t+1)→min(v n (t)+1,v max )]. If the speed v of a vehicle is less than v max and the distance from the front vehicle is greater than v+1, the speed of the vehicle is increased by one unit [v→v+1].

[0065] (2) deceleration (deceleration caused by other vehicles) [v n (t+1)→min(v n (t+1)-1,d n )]. If the driver at position i sees the front vehicle at position i+j (where j≤v), the speed of the vehicle is reduced to [v→j-1].

[0066] (3) random slowing [v n (t+1)→max(v n (t+1)-1,0)]. With a probability p, the speed of each vehicle is reduced by one unit [v→v-1] until 0.

[0067] (4) vehicle motion [x n (t+1)→x n (t)+v n (t+1)]. Each vehicle advances v cells in the next time step.

[0068] For (1)-(4) above, v n is the speed of the nth vehicle, and x nis the position of the nth vehicle. At time step t, the minimum headway between the nth vehicle and the nth+1 vehicle is d n = x n+1 - x n -1. At each t+1 time step, each iteration consists of the above four consecutive steps according to the rules of the Nasch model, which is performed in parallel for all vehicles.

[0069] The update of the pedestrian is achieved in a similar way as the vehicle.

[0070] For the fuzzy logic reasoning, the fuzzy logic reasoning of the embodiment of the present application is used to reason the filtering decision of the driver who does not comply with the traffic rules of yielding to pedestrians at the pedestrian crossing. The fuzzy logic refers to the uncertain concept judgment, reasoning mode of the human brain, the description system which is unknown or cannot be determined, and the control object with strong nonlinearity and large hysteresis. The fuzzy set and fuzzy rule are applied to reasoning, expressing the transitional boundary or qualitative knowledge and experience, simulating the human brain mode, and implementing fuzzy comprehensive judgment.

[0071] In the dynamic interaction of vehicles and pedestrians at the pedestrian crossing of the signal control section, there are two factors related to pedestrians at the pedestrian crossing which affect the filtering decision of the driver whether to pass the pedestrian crossing: the pedestrian gap and the pedestrian speed, wherein the pedestrian gap refers to the gap between the vehicle and the potential conflict area passing through the pedestrian, that is, if the gap between the crossing pedestrian and the conflict area is limited, or the pedestrian speed is fast, there is a potential risk of collision when the vehicle passes through the pedestrian crossing. In the present application, the pedestrian gap is divided into short gap, intermediate gap and long gap, which are as follows:

[0072] (1) Short gap: if the driver considers that the pedestrian gap is too small to pass, then the pedestrian gap will be determined as a short gap.

[0073] (2) Intermediate gap: that is, the gap with medium length, some drivers choose to filter through, and some drivers will choose to stop, and these gaps are determined as an intermediate gap.

[0074] (3) Long gap: if the driver determines that the pedestrian gap is safe enough to filter through, then the gap will be identified as a long gap.

[0075] The pedestrian speed can also be divided into three levels: slow, medium and fast. When fuzzy reasoning is performed, the input variables are the pedestrian gap and the pedestrian speed. The output variable is the filtering decision of the driver at the signal control section, that is, to pass or to wait.

[0076] Based on this, the fuzzy logic reasoning method of the embodiment of the present application is:

[0077] (1) When the pedestrian gap is medium, the driver's decision is affected by both the pedestrian speed and the pedestrian gap. If the pedestrian speed is considered slow, the driver's filtering decision before the crosswalk is to pass, and if the pedestrian speed is judged to be medium or fast, the driver's filtering decision before the crosswalk is to wait.

[0078] (2) When the pedestrian gap is short or long, the driver's decision depends only on the length of the pedestrian gap. If the driver considers the gap to be short, the driver will stop and wait for the pedestrian to pass before the crosswalk, and if the driver judges the gap to be long, the driver's filtering decision is to pass.

[0079] In the pedestrian model, the pedestrian crossing decision is determined by the pedestrian dynamic gap acceptance (PDGA) model. For example, when a pedestrian arrives at a two-way six-lane crosswalk, the pedestrian will face three lanes in the current direction to start crossing the road. First, the pedestrian will judge whether the gap in the first lane is acceptable. If the pedestrian refuses the gap, the pedestrian will wait for the next gap in the first lane until an acceptable gap appears. Then, the pedestrian will determine whether the maximum gap of the second lane and the maximum gap of the third lane are acceptable when the pedestrian crosses the first lane according to the walking time. Based on this, the pedestrian crossing decision is output.

[0080] For the membership function, the membership function is used to distinguish fuzzy sets and handle fuzzy relationships, and can fuzz the input variables. In the membership function, each specific value of the input variable has a membership degree between 0 and 1 corresponding to it. The membership function of the embodiment of the present application is mainly used for fuzzy processing of the pedestrian gap and the pedestrian crossing speed.

[0081] The embodiment of the present application adopts a Gaussian function as the membership function of the input variable, and performs Gaussian membership function fitting according to the pedestrian gap and pedestrian speed data obtained by field investigation, so as to calibrate the membership functions of the pedestrian gap and the pedestrian speed, and obtain the membership functions as follows:

[0082]

[0083]

[0084] f2=1-f1-f3

[0085] Wherein, f1 represents a short gap and a slow pedestrian speed, f2 represents a medium gap and a medium pedestrian speed, and f3 represents a long gap and a fast pedestrian speed; the parameter x c1 is the abscissa of the center of the short gap and slow speed curve peak, and the parameter x c3is the coordinate of the center of the short gap and slow speed curve peak; the parameter w1 is the standard deviation of the short gap and slow speed, and the parameter w3 is the standard deviation of the long gap and fast speed curve; a is the horizontal coordinate at which the membership degree of the short gap and slow speed starts to be not 1, b is the horizontal coordinate at which the membership degree of the medium gap and medium speed is 1, and c is the horizontal coordinate at which the membership degree of the long gap and fast speed starts to be 1.

[0086] The field survey data can be obtained in the following way:

[0087] Two cameras are placed on adjacent high-rise buildings and pedestrian overpasses to collect the trajectories of vehicles and pedestrians. The specific position of a pedestrian is defined as the middle of the pedestrian's two feet, and a vehicle is defined as a length of 4.5 meters. During the data extraction process, a measurer records the judgment of the driver on the pedestrian gap according to the dynamic traffic interaction. If the driver considers that the pedestrian gap is too small to pass, then the pedestrian gap will be determined as a short gap. If the driver determines that the pedestrian gap is safe enough to filter through, then this gap will be identified as a long gap. In addition, there are some gaps that some drivers choose to filter through, while some drivers choose to stop, and these gaps are considered to be medium gaps. At the same time, another measurer records the crossing time of each pedestrian and divides the crossing speed of the pedestrian into three levels: fast, medium and slow, thereby obtaining the data of the crossing speed of the pedestrian.

[0088] The output variable range of the fuzzy cellular automaton model is [1, 3], and the output response can be represented by a triangular membership function as follows:

[0089]

[0090] In some embodiments, before applying the fuzzy cellular automaton model, the model can be subjected to model verification, and the model verification adopts a new set of survey data on the same person crosswalk, and the verification data includes the mean value Mean and the coefficient of variation CoV. The mean is an index reflecting the trend in the data set, and the coefficient of variation (also known as the dispersion coefficient) is a normalized measure of the degree of dispersion of a probability distribution. The calibration basis of the model is that the error of the mean value Mean is below 3%, and the coefficient of variation CoV is below 0.1.

[0091] The step of establishing the localized vehicle emission factor library S120 can include but is not limited to including the following steps:

[0092] (1) Determine the measured vehicle, and obtain the measured operating parameters and measured emission results of the measured vehicle.

[0093] (2) Calculate the specific power of the measured vehicle according to the measured operating parameters.

[0094] According to the instantaneous speed, instantaneous acceleration and other information of the measured motor vehicle, the specific power (VSP) of the motor vehicle is calculated, and the calculation formula of the specific power is:

[0095] VSP = v x (1.1a + 0.132) + 0.000302v 3

[0096] Wherein, VSP is the specific power of the vehicle, v is the instantaneous speed of the vehicle, and a is the instantaneous acceleration of the vehicle.

[0097] (3) According to the specific power and the instantaneous speed of the vehicle, the running state of the vehicle is divided into several working condition intervals.

[0098] Referring to the MOVES model, the running state of the vehicle is divided into 23 working condition intervals according to the specific power and the instantaneous speed of the motor vehicle. Among them, the MOVES model is a new generation of comprehensive mobile source emission model, which is used as the basic model for calculating the vehicle model emission result in the present application, and the model structure can refer to Figure 3 , Figure 3 It is the model structure diagram of the MOVES model provided by the embodiment of the present application.

[0099] (4) According to the measured emission result, the average basic emission rate of each working condition interval is calculated.

[0100] For each working condition interval, the average basic emission rate under each working condition interval is calculated according to the measured vehicle emission test result of the measured vehicle, the quantitative relationship between the running condition and the emission rate is established, and the basic emission rate of the motor vehicle under the specific condition (according to the vehicle type, fuel type and emission standard) is obtained. The calculation formula of the average basic emission rate is as follows:

[0101]

[0102] Among them, is the average basic emission rate of the i th working condition interval, n i is the sample number of the i th working condition interval, ER ij is the j th emission rate of the i th working condition interval.

[0103] In some embodiments, if the measured data is lacking, the emission factor of different vehicle types and different emission standards provided in the literature is calculated by the emission factor multiple calculation method, and the basic emission rate of the motor vehicle obtained by the measured data is taken as the reference benchmark. The basic emission rate of the motor vehicle under different emission standards is calculated. In addition, the emission degradation rate of the motor vehicle adopts the default value of the MOVES model. The calculation formula is as follows:

[0104]

[0105]

[0106] ER ijk =Base_EF i ×α ij ×β ik

[0107] wherein, α is the multiple of emission factor, EF is the emission factor, Base_EF is the base emission factor, β is the deterioration rate, ER * is the model default emission rate, Base_ER * is the model default base emission rate, ER is the emission rate, Base_ER is the base emission rate, i, j, k represent each vehicle model, emission standard and vehicle age.

[0108] (5) According to the instantaneous speed of the measured vehicle and the average base emission rate, the emission factor of the operating condition interval is calculated.

[0109] According to the second-by-second driving speed (instantaneous speed) of the motor vehicle, the average base emission rate is converted into the emission factor (unit: g / km), so that the emission factor under each operating condition interval can be obtained, and the calculation formula of the emission factor is as follows:

[0110]

[0111] wherein, EF is the emission factor (unit: g / km), ER is the emission rate (unit: g / s), and v is the instantaneous speed of the vehicle (unit: km / h).

[0112] (6) All the emission factors are collected into a localized vehicle emission factor library.

[0113] The emission factors under each operating condition interval are collected to form a localized vehicle emission factor library.

[0114] S200, a fuzzy cellular automaton model is used to simulate different driving behavior scenarios, and driving behavior and corresponding second-by-second vehicle operating parameters of driving behavior are obtained.

[0115] In order to study the influence of different driving behaviors on motor vehicle exhaust emission, different driving behavior scenarios can be simulated.

[0116] Five different driving behavior scenarios are taken as examples, and the driver courtesy rates 0, 0.25, 0.5, 0.75 and 1 are configured for the five driving behavior scenarios respectively. Among them, 0 represents that the driver does not give way to pedestrians at all, but tries to find an opportunity to pass the pedestrian crossing as much as possible before the road section without signal control pedestrian crossing, and 1 represents that the driver gives way to pedestrians completely. For each scenario, the simulation time step is 1s, and the simulation is run for a total of 3660 time steps, of which the initialization time is 60 time steps, and each scenario is simulated for a total of three times. Run the simulation under different scenarios, and take the average of the three simulation runs as the output result of each scenario. If the simulation results of the three runs have no significant difference, it means that the simulation time of 3660 time steps is sufficient to obtain reliable simulation results, and at this time the second-by-second running status information of the vehicle can be output. If the simulation results of the three runs have significant differences, first check whether the simulation parameter settings are incorrect, and then output the simulation results after confirming that there is no error, so as to ensure the accuracy of the simulation results and provide a reliable data basis for subsequent data analysis.

[0117] It should be noted that the specific values of the simulation parameter configuration described above are only used as an example, and in some embodiments, the values can be modified according to actual needs.

[0118] Reference Figure 2 , Figure 2 is a workflow diagram of the fuzzy cellular automaton model provided by the embodiment of the present application, and the specific steps of each simulation include the following steps S210-S240:

[0119] S210, the simulation data of vehicles and pedestrians is randomly generated using normal distribution.

[0120] The simulation data of vehicles and pedestrians is randomly generated using normal distribution. Vehicles enter the system at an initial speed within the maximum speed. Each vehicle enters the system at an initial speed not greater than the maximum speed, and before a new vehicle enters the system, it is checked whether the first cell entered by the vehicle is empty. For a newly generated pedestrian, it is also checked whether there is an empty cell in the current waiting area. After a pedestrian passes through the pedestrian crossing, the pedestrian is removed. Similarly, when a vehicle passes through the pedestrian crossing to the end of the road, the vehicle is removed. The movement rules of vehicles and pedestrians both comply with the update rules of the NaSch (a cellular automaton model) model in S100. The iteration time step can be set, for example, the iteration time step is set to 1.

[0121] S220, based on the pedestrian dynamic gap acceptance model, the pedestrian crossing decision is determined according to the lane gap and the walking time of the pedestrian.

[0122] The pedestrian crossing decision can be determined by the pedestrian model simulation in the fuzzy cellular automaton model in S100.

[0123] S230, determining the fuzzy filtering decision of the driver in front of the pedestrian crossing based on the driver membership function according to the pedestrian gap and the pedestrian speed.

[0124] Based on the input traffic operation data, the model determines whether the current vehicle is in front of the pedestrian crossing. If the current vehicle is not in front of the pedestrian crossing, the current vehicle updates the speed and position of the vehicle according to the cellular automaton (CA) model. If the current vehicle is in front of the pedestrian crossing, the driver behavior will be divided: for the driver who obeys the traffic rules of respecting pedestrians, the speed and position of the driver are updated according to the rules of the cellular automaton (CA) model; for the driver who does not obey the traffic rules of respecting pedestrians, the filtering decision of the driver in front of the pedestrian crossing is inferred according to the fuzzy inference to determine the driving behavior.

[0125] S240, controlling the movement of the vehicle according to the above fuzzy filtering decision, and recording the second-by-second operation parameters of the vehicle.

[0126] The operation parameters of the vehicle include the instantaneous speed of the vehicle, the instantaneous acceleration of the vehicle, etc.

[0127] In some embodiments, the fuzzy cellular automaton model can be calibrated and checked before simulation to determine the appropriate membership function.

[0128] S300, combining the localized vehicle emission factor library, calculating the first emission amount of the vehicle according to the second-by-second operation parameters of the vehicle.

[0129] Specifically, step S300 includes steps S310-S330.

[0130] S310, calculating the second-by-second specific power value according to the instantaneous speed and the instantaneous acceleration of the actual operation of the vehicle.

[0131] The second-by-second specific power value of the vehicle can be calculated by referring to the second-by-second specific power value calculation formula in step S120, which will not be repeated here.

[0132] S320, matching the basic emission rate of the operating condition from the localized emission factor library according to the second-by-second specific power value.

[0133] S330, calculating the first emission amount of the vehicle from entering the road section to leaving the road section according to the basic emission rate and the operation time of the vehicle. The formula for calculating the first emission amount of a single vehicle from entering the road section to leaving the road section is:

[0134] E = ∑ i ER i × t i

[0135] Wherein, E is the first emission amount (unit: g), ER iis the average base emission rate (unit: g / s) of the i-th operating condition interval, t i is the driving time (unit: s) under the i-th operating condition interval, g represents gram, and s represents second.

[0136] S400, calculate the total emission of all vehicles on the no-signal control road section within a preset time period according to the first emissions of all vehicles.

[0137] The calculation formula of the total emission of all running vehicles on the road section within the preset time period is:

[0138] Q = ∑E m

[0139] Wherein, Q is the total emission of motor vehicles in the simulation period (unit: g), E m is the first emission (unit: g) of the m-th vehicle.

[0140] S500, according to the first emission and the total emission, predict the influence of driving behavior on emission.

[0141] Specifically, step S500 can include the following steps S510-S520.

[0142] S510, according to the first emission, calculate the second emission of each driving behavior scenario in combination with the relationship between driving conditions and each driving behavior scenario.

[0143] Statistical analysis of the proportion of each driving condition corresponding to each driver behavior scenario, and comparative analysis of the emission difference of each emission under different driving behavior scenarios. Among them, the driving conditions include acceleration, deceleration, constant speed, idle speed, and the meaning of each driving condition is:

[0144] Idle speed: speed and acceleration are zero.

[0145] Acceleration: acceleration is greater than or equal to acceleration threshold (0.1 m / s 2 ).

[0146] Constant speed: the absolute value of acceleration is less than the acceleration threshold, and the speed is not equal to zero.

[0147] Deceleration: acceleration is less than negative acceleration threshold.

[0148] S520, according to the second emission, determine the emission difference of each emission under different driving behavior scenarios, and obtain the influence of driving behavior on emission.

[0149] In some embodiments, for each driving behavior scenario, vehicle flow and pedestrian flow can be taken as variables in the simulation process, vehicle flow is controlled to change in the range of 360-1980 pcu / h (three-lane vehicle flow), pedestrian flow is controlled to change in the range of 180-504 ped / h, the input rate of the vehicle changes according to the change of the vehicle flow, the input rate of the pedestrian also changes according to the change of the pedestrian flow, then according to the simulation results, the conflict, delay and total emission of the vehicle passing through the pedestrian crossing under different traffic flow conditions are compared and analyzed, so as to comprehensively evaluate the influence of different driving behaviors on emissions under different traffic flow conditions in combination with traffic safety and efficiency and other indicators.

[0150] In another aspect, the embodiments of the present application also provide a system for predicting the influence of different driving behaviors of a vehicle on emissions. Figure 4 , Figure 4 FIG. 1 is a structural schematic diagram of a system for predicting the influence of different driving behaviors of a vehicle on emissions provided by the embodiments of the present application, the system comprising:

[0151] A first module for constructing a fuzzy cellular automaton model of a pedestrian crossing on a signal-free road section and establishing a localized vehicle emission factor library;

[0152] A second module for simulating the pedestrian motion condition and the vehicle motion condition by using the fuzzy cellular automaton model to obtain driving behaviors and corresponding vehicle second-by-second running parameters of the driving behaviors;

[0153] A third module for calculating a first emission amount of the vehicle according to the vehicle second-by-second running parameters in combination with the localized vehicle emission factor library;

[0154] A fourth module for calculating a total emission amount of all the vehicles on the signal-free road section within a preset time period according to the first emission amount of all the vehicles;

[0155] A fifth module for predicting the influence of driving behaviors on emissions according to the first emission amount and the total emission amount.

[0156] It should be noted that in some embodiments, the system can further comprise the following module:

[0157] A sixth module for calibrating and checking the fuzzy cellular automaton model.

[0158] In another aspect, the embodiments of the present application also provide an electronic device, as shown in Figure 5 , Figure 5 FIG. 1 is a structural schematic diagram of an electronic device provided by the embodiments of the present application, the electronic device comprising a processor and a memory; the memory is used for storing a program; the processor executes the program to realize the method as described above.

[0159] In another aspect, the embodiments of the present application also provide a computer storage medium, which stores a processor-executable program, and the processor-executable program is used for implementing the method as described above when executed by a processor.

[0160] The embodiments of the present application have the following beneficial effects:

[0161] 1) The fuzzy cellular automata model constructed for the emission calculation of the pedestrian crossing at the signal-free road section is beneficial to understand the fuzzy filtering decision of a single driver in front of the pedestrian crossing, truly reproduce the human-vehicle interaction in front of the signal-free pedestrian crossing, achieve a better prediction effect of predicting the influence of different driving behaviors of vehicles on emissions, and make the prediction more accurate.

[0162] 2) The simulation model output of the present application is used to calculate the second-by-second running condition information of the vehicle, and the specific power model based on MOVES is used to establish a localized emission factor library, so that the instantaneous emission of a certain type of vehicle can be calculated, which is beneficial to sensitively, dynamically and flexibly reflect the influence of the change of the vehicle running mode on the emission.

[0163] 3) The present application integrates safety and efficiency evaluation indexes, compares and analyzes the emission amount difference of each emission under different driving behavior scenarios, which is beneficial to comprehensively evaluate the influence of different driving behaviors on traffic and emissions.

[0164] In some alternative embodiments, the functions / operations mentioned in the block diagram can not occur in the order mentioned in the operation diagram. For example, depending on the functions / operations involved, two blocks shown in succession can actually be executed substantially simultaneously or the blocks can sometimes be executed in reverse order. In addition, the embodiments presented and described in the flowcharts of the present application are provided by way of example, with the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operations and logical flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and in which sub-operations described as part of larger operations are independently executed.

[0165] If the functions are implemented in the form of software function units and sold or used as independent products, they can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the parts of the technical solutions that essentially contribute to the prior art or the parts of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and various media that can store program codes.

[0166] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a list of executable instructions for implementing logic functions, which can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus or device, such as a computer-based system, a system including a processor or other system that can fetch the instructions from the instruction execution system, apparatus or device and execute the instructions, or in conjunction with these instructions execution systems, apparatus or devices. For the purpose of this specification, the "computer-readable medium" can be any device that can contain, store, communicate, propagate or transport programs for use by or in connection with an instruction execution system, apparatus or device, or in conjunction with these instruction execution systems, apparatus or devices.

[0167] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection having one or more wires (electrical devices), a portable computer diskette (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, because the program can be electronically obtained, for example, by optical scanning of the paper or other medium, followed by editing, interpreting or otherwise processing, if necessary, in other suitable ways, to be electronically obtained and then stored in the computer memory.

[0168] It should be understood that aspects of the application can be implemented in hardware, software, firmware or a combination thereof. In the above embodiments, various steps or methods can be implemented in software or firmware which is stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, and in another embodiment, any of the following technologies, known in the art, can be used: a combination of discrete logic circuits having logic gates for implementing logic functions upon an application of data signals, application specific integrated circuits having logic gates, field programmable gate arrays (FPGA), or other components, in combination or as the case can be.

[0169] In the description of the specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the application. In the specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in one or more embodiments or examples.

[0170] Although the embodiments of the present application have been shown and described, it would be appreciated by those skilled in the art that changes, modifications, alternatives and variations can be made to the embodiments without departing from the principles and spirit of the application, and the scope of the present application is defined by the claims and their equivalents.

[0171] The above is a specific description of the preferred embodiments of the present application, but the present application is not limited to the described embodiments, and those skilled in the art can make various equivalent modifications or replacements without departing from the spirit of the present application, and these equivalent modifications or replacements are all included in the scope defined by the claims of the present application.

Claims

1. A method for predicting the impact of different vehicle driving behaviors on emissions, characterized in that: include: Construct a fuzzy cellular automation model for pedestrian crossings on unsignalized roads and establish a localized vehicle emission factor library; Using the fuzzy cellular automaton model, different driving behavior scenarios are simulated to obtain driving behaviors and vehicle operating parameters corresponding to the driving behaviors on a second-by-second basis; In combination with the localized vehicle emission factor library, calculating the first emission amount of the vehicle based on the second-by-second operating parameters of the vehicle, including: calculating a second-by-second specific power value based on the instantaneous speed and instantaneous acceleration of the vehicle; matching a basic emission rate for the operating condition from the localized emission factor library based on the second-by-second specific power value; and calculating the first emission amount of the vehicle from entering the road section to leaving the road section based on the basic emission rate and the vehicle's operating time; Calculating the total emissions of all the vehicles on the unsignalized road section within a preset time period based on the first emissions of all the vehicles; Predicting the impact of driving behavior on emissions based on the first emissions and the total emissions includes: calculating, based on the first emissions, a second emissions for each driving behavior scenario in light of a relationship between the driving condition and the respective driving behavior scenarios; and determining, based on the second emissions, emission differences for each emission substance under different driving behavior scenarios to obtain the impact of driving behavior on emissions.

2. The method for predicting the impact of different vehicle driving behaviors on emissions according to claim 1, characterized in that: The fuzzy cellular automaton model of the pedestrian crossing on the unsignalized road section includes: A vehicle model, wherein the vehicle model comprises a vehicle yielding decision and a vehicle movement behavior; A pedestrian model, wherein the pedestrian model comprises pedestrian crossing decisions and pedestrian movement behaviors; Membership function used to determine the fuzzy filtering decision of a single driver who does not yield to pedestrians before a crosswalk.

3. The method for predicting the impact of different vehicle driving behaviors on emissions according to claim 1, characterized in that: The steps to establish a localized vehicle emission factor library include: Determining a vehicle to be tested, and obtaining the tested operating parameters and the tested emission results of the vehicle to be tested; Calculating the specific power of the vehicle under test according to the measured operating parameters; Dividing the vehicle operating state into a plurality of operating condition intervals according to the specific power and the instantaneous speed of the vehicle; Calculating an average basic emission rate for each operating condition interval based on the measured emission results; Calculating the emission factor for the operating range according to the instantaneous speed and the average basic emission rate of the tested vehicle; All of the emission factors are aggregated into a localized vehicle emission factor library.

4. The method for predicting the impact of different vehicle driving behaviors on emissions according to claim 1, characterized in that: The fuzzy cellular automaton model is used to simulate different driving behavior scenarios to obtain driving behaviors and vehicle operating parameters corresponding to the driving behaviors, including: Normal distribution is used to randomly generate simulated data of vehicles and pedestrians; Based on the pedestrian dynamic gap acceptance model, pedestrian crossing decisions are determined according to lane gaps and the pedestrian's walking time; Based on the membership function, the driver's fuzzy filtering decision before the crosswalk is determined according to the pedestrian gap and pedestrian speed; The movement of the vehicle is controlled according to the fuzzy filtering decision, and the operating parameters of the vehicle are recorded.

5. The method for predicting the impact of different vehicle driving behaviors on emissions according to claim 4, characterized in that: The expression of the membership function is: in, Indicates short gaps and slow pedestrian speeds, Indicates medium gap and pedestrian speed, Indicates long gaps and fast pedestrian speeds; parameter x c1 is the abscissa of the center of the peak of the short gap and slow speed curve, and the parameter x c3 are the coordinates of the center of the peak of the long gap and fast speed curve; the parameter w 1 is the standard deviation of short gap and slow speed, parameter w 3 is the standard deviation of the long gap and fast speed curves; a It is the horizontal coordinate where the membership of short gap and slow speed is not 1 at the beginning. b is the horizontal coordinate with the membership degree of 1 for medium gap and medium speed, c It is the horizontal coordinate where the membership of long gap and fast speed starts to be 1.

6. A system for implementing the method for predicting the impact of different vehicle driving behaviors on emissions according to any one of claims 1 to 5, characterized in that: include: The first module is used to construct a fuzzy cellular automation model for pedestrian crosswalks on unsignaled roads and establish a localized vehicle emission factor library; The second module is used to simulate the movement of pedestrians and vehicles using the fuzzy cellular automaton model to obtain driving behaviors and vehicle operating parameters corresponding to the driving behaviors on a second-by-second basis; A third module is configured to calculate a first emission of the vehicle based on the second-by-second operating parameters of the vehicle in combination with the localized vehicle emission factor library; A fourth module is configured to calculate the total emissions of all the vehicles on the non-signal controlled road section within a preset time period based on the first emissions of all the vehicles; The fifth module is used to predict the impact of driving behavior on emissions based on the first emissions and the total emissions.

7. An electronic device, characterized in that: including a processor and a memory; The memory is used to store programs; The processor executes the program to implement the method according to any one of claims 1 to 5.

8. A computer storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 5 when executed by the processor.

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