Method for evaluating driving ability of pilot vehicle driver in intelligent network connection truck formation in typical scene

Abstract

The invention relates to a method for evaluating the driving ability of a pilot vehicle driver in an intelligent network connection truck formation in a typical scene. The method comprises the steps of obtaining running state data of the truck formation in the typical scene; constructing a formation operation characteristic index system, and determining a control behavior threshold value of a pilot vehicle driver based on an optimal operation state in the operation state data; based on the control behavior threshold value, a desktop video experiment is carried out, and experiment original data is obtained; based on the experimental original data, constructing a relation model between the driving ability and the control behavior of the driver; and reversely solving the relation model based on the control behavior threshold, and obtaining a quantitative evaluation result of the driving ability of the pilot vehicle driver. The invention provides a systematized and quantifiable evaluation method for evaluating the driving ability of the pilot vehicle driver of the intelligent network-connected truck formation.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent network-connected platooning, and in particular to a method for evaluating the driving ability of a driver of a leading vehicle in an intelligent network-connected truck platoon under a typical scenario. Background Art

[0002] By combining two or more autonomous trucks, the intelligent networked truck formation can reduce the headway when driving, greatly improve the traffic capacity, help alleviate traffic congestion and improve logistics transportation efficiency. However, under the current technical architecture of the autonomous driving system, autonomous vehicles may still face the possibility of being unable to handle automatically in certain specific situations. When the scenario faced by the vehicle exceeds the design domain of the autonomous driving system, the driver is required to intervene manually or take over. Therefore, the cooperation of truck formations and pilot car drivers in transportation will become a new operating mode. This mode requires the use of the professional skills and experience of the pilot car driver to make up for the shortcomings of the autonomous driving formation system in certain situations to a certain extent, so as to ensure that the truck formation can still drive safely and stably under complex road conditions, bad weather or other unpredictable situations. It can be seen that the driving ability of the pilot car driver has an important impact on the operating characteristics of the intelligent networked truck formation and is one of the key factors to ensure the safe and smooth operation of the formation.

[0003] Regarding the driving ability of drivers, current research focuses on the perception, attention, reaction speed, maneuvering skills, fatigue characteristics and typical driving behavior characteristics of traditional vehicle drivers, as well as the collaborative interaction characteristics between the driving ability of drivers and the autonomous driving system in the networked single vehicle mode. Since the intelligent networked platooning technology has just emerged, there is a relative lack of research on the driving ability of the driver of the intelligent networked platooning pilot car, and most of the research focuses on the truck platooning technology itself. Unlike traditional truck drivers and autonomous truck safety officers, the driver of the truck platooning pilot car not only needs to bear the responsibility of monitoring the real-time operating status of the vehicle, such as when encountering dangerous scenes, special events, bad weather, etc. that are beyond the design domain of the autonomous vehicle, quickly take over and control the vehicle, and should always monitor the following status and requests of other trucks in the queue to maintain the best platooning operation mode. However, what standard of driving ability of the pilot car driver should meet to meet the best truck platooning operation status is an urgent problem to be solved. Summary of the invention

[0004] The purpose of the present invention is to propose a method for evaluating the driving ability of a driver of a leader vehicle in an intelligent connected truck platoon under a typical scenario, so as to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for evaluating the driving ability of a pilot vehicle driver in a smart connected truck platoon under a typical scenario includes:

[0007] Obtain the operating status data of truck platoons in typical scenarios;

[0008] Constructing a formation operation characteristic index system, and determining a control behavior threshold of the pilot vehicle driver based on the best operation state in the operation state data;

[0009] Based on the manipulation behavior threshold, a desktop video experiment is conducted to obtain raw experimental data;

[0010] Based on the raw data of the experiment, a relationship model between the driver's driving ability and control behavior is constructed;

[0011] The relationship model is reversely solved based on the control behavior threshold to obtain a quantitative evaluation result of the driving ability of the pilot vehicle driver.

[0012] Optionally, obtaining the running status data of the truck convoy in a typical scenario includes:

[0013] Determine the pilot car driver's control behavior and the characterization indicators of control behavior in typical scenarios;

[0014] Based on the control behavior and characterization indicators, the truck platoon simulation is carried out to obtain the operation status data of the truck platoon.

[0015] Optionally, the operation status data of the truck formation includes: basic information of the truck formation, location information, operation data and energy consumption and emission data;

[0016] The basic information of the truck formation includes: simulation time and vehicle number;

[0017] The position information includes: horizontal position coordinates and vertical position coordinates;

[0018] The operating data includes: longitudinal speed, lateral speed, longitudinal acceleration, lateral acceleration, steering wheel angle, collision time, collision time countdown, vehicle-to-vehicle distance, and headway;

[0019] The energy consumption and emission data include: CO2 emissions, NO x Emissions, CO emissions, HC emissions, fuel consumption.

[0020] Optionally, the formation operation characteristic index system includes: stability dimension, safety dimension and ecological dimension;

[0021] The stability dimensions include: longitudinal speed, longitudinal acceleration, and vehicle-to-vehicle distance;

[0022] The longitudinal velocity is:

[0023]

[0024] Where V represents the longitudinal velocity, V i represents the longitudinal speed of the i-th vehicle in the truck platoon, and n represents the number of vehicles in the platoon;

[0025] The longitudinal acceleration is:

[0026]

[0027] Where A represents the longitudinal acceleration, A i represents the longitudinal acceleration of the i-th vehicle in the truck platoon;

[0028] The workshop distance is:

[0029] g i =x i+1 -x i -l

[0030] Among them, g i represents the distance between the i-th vehicle and the vehicle in front, x i represents the position of the i-th vehicle; l represents the length of the vehicle in front of the i-th vehicle;

[0031] The safety dimensions include: collision time countdown, headway, and lane change area;

[0032] The inverse of the collision time is:

[0033] ITTC=(v f -v r ) / (Sl)

[0034] Among them, v f Indicates the speed of the preceding vehicle, v r It represents the speed of the rear vehicle, S represents the headway between the front and rear vehicles, and ITTC represents the inverse time to collision.

[0035] The headway time is:

[0036]

[0037] Among them, THW represents the time headway;

[0038] The lane-changing area is: the space area required for the platoon vehicles to perform lane-changing operations on the road;

[0039] The ecological dimensions include: fuel consumption saving rate, CO emission saving rate, CO2 emission saving rate, NO x Emissions savings rate.

[0040] Optionally, the desktop video experiment includes: pre-experiment subjective testing, pre-experiment driving training, pre-experiment and formal experiment;

[0041] The subjective test before the experiment was: a driving ability test based on a subjective questionnaire;

[0042] The formal experiment is: a manipulation behavior test based on a desktop video experiment.

[0043] Optionally, the driver's driving ability includes: theoretical knowledge of truck platooning, practical skills of truck platooning and driver's psychological state.

[0044] Optionally, the relationship model between the driver's driving ability and control behavior is:

[0045] y1=a o +a1x1+a2x2+a3x3+ε1

[0046] y2=b o +b1x1+b2x2+b3x3+ε2

[0047] Among them, y1 represents the prediction model between driving ability and takeover behavior, y2 represents the prediction model between driving ability and lane changing behavior, x1 represents the theoretical knowledge score, x2 represents the practical skill score, x3 represents the psychological state score, a1, a2, a3, b1, b2, b3 represent the coefficients in the constraints, a0, b0 are the intercept terms in the linear regression model, which represent the expected values ​​of the dependent variables (y1, y2) when the values ​​of all independent variables (x1, x2, x3) are zero, ε1 ,ε2 is the residual, which represents the random fluctuations that the model cannot explain or the random errors in the data.

[0048] Optionally, reversely solving the relationship model based on the manipulation behavior threshold includes:

[0049] Taking the driver's control behavior level as the optimization target and the control behavior threshold as the constraint condition, a solution model for the minimum driving ability requirement is constructed;

[0050] Based on the solution model, the relationship model is solved.

[0051] Optionally, the solution model is:

[0052] Objective function: Min Z = c1x1 + c2x2 + c3x3

[0053] Constraints:

[0054] a0+a1x1+a2x2+a3x3+ε1≥y1

[0055] b0+b1x1+b2x2+b3x3+ε2≥y2

[0056] Capacity constraints:

[0057] x1+x2+x3≤300

[0058] Non-negativity constraints:

[0059] x1,x2,x3≥0

[0060] in, Z Indicates the control time. S.t Represents a constraint.

[0061] The beneficial effects of the present invention are:

[0062] The present invention provides a systematic and quantifiable evaluation method for evaluating the driving ability of the driver of the pilot vehicle of an intelligent networked truck platoon.

[0063] The data collection method of the present invention is accurate and effective. It uses micro-traffic simulation technology to restore the truck platoon test scenario and obtain the full process data of the platoon operation status. The platoon operation characteristic index system takes into account the three levels of stability, safety and ecology. From a macro perspective, subjective questionnaires and desktop experiments are used to obtain driving ability and control behavior data respectively.

[0064] The driving ability evaluation method of the pilot vehicle driver of an intelligent connected truck platoon under a typical scenario of the present invention is generally applicable to other scenarios.

[0065] The present invention can effectively reduce the safety risks and experimental cost in the testing process through traffic simulation technology and desktop experiments.

[0066] The driving ability evaluation method of the truck platoon pilot vehicle driver of the present invention can provide a theoretical basis for the selection, training and assessment of truck platoon pilot vehicle drivers, and facilitate the implementation of intelligent connected truck platoon technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0068] Figure 1 A schematic diagram of an operating characteristic index system for a smart connected truck platoon according to an embodiment of the present invention;

[0069] Figure 2 The present invention is a flowchart of a method for evaluating the driving ability of a pilot vehicle in a smart connected truck platoon in a typical scenario of an embodiment of the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0072] Based on the clarification of the driving ability requirements for the driver of the truck platoon leader vehicle, this embodiment collects data through micro-traffic simulation technology and desktop experiments, determines the control behavior level threshold based on the formation operation characteristics, and quantitatively evaluates the driving ability performance of the leader vehicle driver in typical scenarios, providing a theoretical basis for the selection, training and assessment of truck platoon leader vehicle drivers.

[0073] The present embodiment proposes a method for evaluating the driving ability of a pilot vehicle driver in a typical scenario of an intelligent connected truck platoon, including:

[0074] Obtain the operating status data of truck platoons in typical scenarios;

[0075] Construct a formation operation characteristic index system and determine the control behavior threshold of the pilot vehicle driver based on the best operation state in the operation state data;

[0076] Based on the manipulation behavior threshold, a desktop video experiment was conducted to obtain the original experimental data;

[0077] Based on the original experimental data, a relationship model between the driver's driving ability and control behavior is constructed;

[0078] The relationship model is solved inversely based on the control behavior threshold to obtain the quantitative evaluation results of the driving ability of the pilot car driver.

[0079] Furthermore, obtaining the operation status data of the truck platoon in a typical scenario includes:

[0080] Determine the pilot car driver's control behavior and the characterization indicators of control behavior in typical scenarios;

[0081] Based on the control behavior and characterization indicators, the truck platoon simulation is carried out to obtain the operation status data of the truck platoon.

[0082] Specifically, in this embodiment, determining the control behavior of the pilot vehicle driver in a typical scenario and the characterization index of the control behavior includes:

[0083] Select typical scenarios of intelligent connected truck platooning, determine the control behavior of the pilot vehicle driver in typical scenarios and its characterization indicators and parameters; specifically include:

[0084] Small debris scattered on the highway was selected as a typical scenario, and takeover behavior and lane changing behavior were selected as typical control behaviors. Among them, the takeover reaction time was used as a characterization indicator of the takeover behavior, which was defined as the duration from the vehicle sending a takeover request or the driver discovering an obstacle to the driver taking over the control of the vehicle, with a horizontal range of 1-5s; the lane changing duration was used as a characterization indicator of the lane changing behavior of the pilot vehicle driver, which was defined as the time interval from the driver starting to control the vehicle to the vehicle completing the lane changing operation, with a horizontal range of 3-13s.

[0085] Based on Plexe-SUMO, a simulation platform is built to simulate truck platoons and obtain the operation status data of truck platoons; specifically, it includes:

[0086] Taking the Beijing section of the Beijing-Xiongan Expressway as a prototype, the SUMO simulation software was used to build a 10km long one-way four-lane expressway basic section as a simulation road scene based on actual road geometry and other information. Then, the PLEXE third-party formation creation tool was used to build a typical model of a 3-truck formation, restore the test scene road, traffic and environmental conditions, and determine the formation vehicle operation parameters with reference to previous research results. The formation behavior was controlled by programming different control level parameter inputs, and the formation operation status data of the entire driving process was simultaneously obtained.

[0087] Specifically, in this embodiment, the operation status data of the truck formation includes: basic information of the truck formation, location information, operation data and energy consumption and emission data;

[0088] Basic information of truck platoons includes: simulation time and vehicle number;

[0089] The location information includes: horizontal location coordinates and vertical location coordinates;

[0090] Operation data include: longitudinal speed, lateral speed, longitudinal acceleration, lateral acceleration, steering wheel angle, collision time, collision time countdown, vehicle-to-vehicle distance, and headway;

[0091] Energy consumption and emission data include: CO2 emissions, NO x Emissions, CO emissions, HC emissions, fuel consumption.

[0092] Construct a formation operation characteristic index system and determine the control behavior threshold of the pilot vehicle driver based on the best operation state in the operation state data;

[0093] Furthermore, the constructed truck platoon operation characteristic index system can be divided into three dimensions: stability, safety and ecology.

[0094] Among them, the calculation formula of secondary indicators and quantitative data in the stability dimension is as follows:

[0095] (1) Longitudinal speed: The speed at which vehicles in a truck platoon move in the longitudinal direction, reflecting the relative movement and mutual influence of vehicles in the platoon in the longitudinal direction. The calculation formula is as follows:

[0096]

[0097] Where V i is the longitudinal velocity of the i-th vehicle in the truck platoon; 1<i≤n, n is the number of vehicles in the platoon.

[0098] (2) Longitudinal acceleration: The rate of change of the speed of vehicles in the formation in the longitudinal direction, which reflects the degree of speed change between vehicles in the formation. The calculation formula is as follows:

[0099]

[0100] In the formula, A i is the longitudinal acceleration of the i-th vehicle in the truck platoon; 1<i≤n, n is the number of vehicles in the platoon.

[0101] (3) Inter-vehicle distance: The distance between adjacent vehicles in a platoon, often used to describe the relative position of vehicles in a platoon. The calculation formula is as follows:

[0102] g i =x i+1 -x i -l

[0103] In the formula, g i is the distance between the i-th vehicle and the vehicle in front; x i is the position of the i-th vehicle; l is the body length of the vehicle in front of the i-th vehicle.

[0104] Among them, the calculation formula for the secondary indicators and quantitative data in the security dimension is as follows:

[0105] (1) Reciprocal time to collision: The reciprocal of the time to collision (TTC) between adjacent vehicles in a platoon, often used to assess the risk of collision between vehicles. The calculation formula is as follows:

[0106] ITTC=(v f -v r ) / (Sl)

[0107] In the formula, v f is the speed of the preceding vehicle; v r is the speed of the rear vehicle; S is the distance between the front and rear vehicles.

[0108] (2) Headway: The time difference between the head positions of the adjacent vehicles in the formation. The calculation formula is as follows:

[0109]

[0110] Where S is the distance between the front and rear vehicles; v r is the speed of the following vehicle.

[0111] (3) Lane-changing area: The space required for platoon vehicles to change lanes on the road.

[0112] Among them, the secondary indicators in the ecological dimension include fuel consumption (FC) saving rate, CO emission saving rate, CO2 emission saving rate, NO x Emission saving rate. It is defined as the saving efficiency of platooning vehicles of the same size compared to non-platooning under the same mileage, and the unit is usually expressed in %.

[0113] Specifically, in this embodiment, firstly, combined with the sustainable development of platooning technology, a truck platooning operation characteristic index system is constructed from three aspects: stability, safety, and ecology through literature reading, such as Figure 1 As shown in Table 1 and Table 2. Secondly, the specific operating characteristics of different control levels at each indicator level are analyzed to preliminarily clarify the threshold range of control behavior under the optimal operation state of the formation. Then, the initial results are further analyzed and verified from a statistical perspective, and the results are shown in Table 1 and Table 2. Finally, it is determined that the threshold range of the pilot car driver's takeover behavior in a typical scenario is 4-5s, and the threshold range of lane change behavior is 3-4s.

[0114] Table 1 Verification results

[0115]

[0116] Table 2 Verification results 2

[0117]

[0118] Furthermore, based on the manipulation behavior threshold, a desktop video experiment was conducted to obtain the original experimental data;

[0119] In this embodiment, 30 or more subjects are selected to carry out a desktop video experiment and obtain raw experimental data;

[0120] The basic process of desktop experiments includes subjective testing before the experiment, driving training before the experiment, pre-experiment and formal experiment.

[0121] Among them, the pre-experimental subjective test refers to the driving ability test based on a subjective questionnaire;

[0122] Among them, the formal experiment refers to the manipulation behavior test based on the desktop video experiment.

[0123] The pre-experiment process is the same as the formal experiment. The purpose is to familiarize yourself with the overall experimental process and basic operations. After confirmation, you can enter the formal experiment.

[0124] Specifically, this embodiment recruits 42 test subjects with a male-female ratio of 5:1. All test subjects hold a Class A or Class B driver's license and have actual truck driving experience. The experiment is arranged to be tested in the same closed laboratory, and there is no external interference during the test. The experiment consists of four parts: pre-experimental subjective testing, pre-experimental driving training, pre-experimental and formal experiments. The original experimental data of each subject is obtained through the data recording module. Table 3 is an example of some of the original data of the subjects.

[0125] Table 3. Example of raw data

[0126]

[0127] The data examples in Table 3 are the driving ability data obtained by the driver through the subjective questionnaire test and the control behavior data obtained through the desktop video experiment. The driving ability unit is points, and the larger the score, the higher the driver's driving ability. The control level unit is seconds, and the shorter the time, the stronger the driver's control ability.

[0128] Furthermore, based on the original experimental data, a relationship model between the driver's driving ability and control behavior is constructed;

[0129] The driver's driving ability mainly includes three aspects: theoretical knowledge of truck platooning, practical skills of truck platooning and driver's psychological state.

[0130] Among them, truck platooning theoretical knowledge includes but is not limited to road traffic laws and regulations, autonomous driving technology knowledge, truck platooning management knowledge, emergency response knowledge, safe driving knowledge, etc.;

[0131] Among them, truck platooning practical skills include but are not limited to basic driving operations, automatic driving system control, truck platooning system control, communication system control, manual takeover, etc.;

[0132] Among them, the psychological state of driving life includes but is not limited to strong physical fitness, flexible reaction ability, high attention and concentration, coordination and teamwork ability, etc.

[0133] Specifically, first, the correlation analysis between variables is performed, as shown in Table 4. Then, the prediction model between driving ability and takeover behavior and lane change behavior is selected and established, and the model results are verified, as shown in Tables 5 and 6. The results show that the established prediction model has a good fitting effect, and there is no collinearity problem between variables. Finally, the accuracy of the model is analyzed, and the average relative errors of the two models are 6.34% and 6.89% respectively by calculating the relative errors, which have good prediction accuracy.

[0134] Table 4 Correlation analysis

[0135]

[0136] Table 5 Model fitting effect analysis

[0137]

[0138] Table 6 Regression coefficients and significance tests

[0139]

[0140] Based on the above test results, the multivariate linear regression model that can express the relationship between driving ability and control behavior is determined as follows:

[0141] y1=11.579-0.039x1-0.051x2-0.03x3

[0142] y2=14.631-0.035x1-0.068x2-0.026x3

[0143] Where y1 is the prediction model between driving ability and takeover behavior; y2 is the prediction model between driving ability and lane changing behavior.

[0144] Furthermore, the relationship model is reversely solved based on the control behavior threshold to obtain the quantitative evaluation results of the driving ability of the pilot car driver;

[0145] Specifically, in this embodiment, the driver's control behavior level is taken as the optimization target, and the control behavior threshold range is taken as the constraint condition to construct a solution model for the minimum driving ability requirement, as shown below.

[0146]

[0147] In the formula, x1 is the theoretical knowledge score, x2 is the practical skills score, and x3 is the student's psychological state score.

[0148] The solution results show that in a typical scenario of spilled objects on a highway, as a qualified truck platoon leader driver, the theoretical knowledge score should not be less than 59 points, the practical skills score should not be less than 80 points, and the psychological state score should not be less than 40 points. Figure 2 shown.

[0149] This embodiment proposes a method for evaluating the driving ability of the pilot car driver of a smart connected truck formation in a typical scenario. It is guided by the results of the formation operation status, clarifies the threshold of the driver's control behavior level, and reversely analyzes and evaluates the driving ability of the pilot car driver based on the correlation between driving ability and control behavior. It shows that the present invention can more scientifically and reasonably evaluate the minimum driving ability requirements that a qualified pilot car driver of a smart connected truck formation should have, which is helpful for the selection, training and assessment of the pilot car driver of a truck formation. It conforms to the actual situation and has a certain degree of promotion.

[0150] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for evaluating the driving ability of a pilot vehicle driver in a smart connected truck platoon under a typical scenario, characterized in that: include: Obtain the operating status data of truck platoons in typical scenarios; Constructing a formation operation characteristic index system, and determining a control behavior threshold of the pilot vehicle driver based on the best operation state in the operation state data; Based on the manipulation behavior threshold, a desktop video experiment is conducted to obtain raw experimental data; Based on the raw data of the experiment, a relationship model between the driver's driving ability and control behavior is constructed; The relationship model is reversely solved based on the control behavior threshold to obtain a quantitative evaluation result of the driving ability of the pilot vehicle driver.

2. The method for evaluating the driving ability of the pilot vehicle of the intelligent connected truck platoon in a typical scenario according to claim 1 is characterized in that: Obtaining the operating status data of truck platoons in typical scenarios includes: Determine the pilot car driver's control behavior and the characterization indicators of control behavior in typical scenarios; Based on the control behavior and characterization indicators, the truck platoon simulation is carried out to obtain the operation status data of the truck platoon.

3. The method for evaluating the driving ability of the pilot vehicle of the intelligent connected truck platoon in a typical scenario according to claim 2 is characterized in that: The operation status data of the truck formation includes: basic information of the truck formation, location information, operation data and energy consumption and emission data; The basic information of the truck formation includes: simulation time and vehicle number; The position information includes: horizontal position coordinates and vertical position coordinates; The operating data includes: longitudinal speed, lateral speed, longitudinal acceleration, lateral acceleration, steering wheel angle, collision time, collision time countdown, vehicle-to-vehicle distance, and headway; The energy consumption and emission data include: CO2 emissions, NO x Emissions, CO emissions, HC emissions, fuel consumption.

4. The method for evaluating the driving ability of the pilot vehicle of a smart connected truck platoon in a typical scenario according to claim 1 is characterized in that: The formation operation characteristic index system includes: stability dimension, safety dimension and ecological dimension; The stability dimensions include: longitudinal speed, longitudinal acceleration, and vehicle-to-vehicle distance; The longitudinal velocity is: Where V represents the longitudinal velocity, V i represents the longitudinal speed of the i-th vehicle in the truck platoon, and n represents the number of vehicles in the platoon; The longitudinal acceleration is: Where A represents the longitudinal acceleration, A i represents the longitudinal acceleration of the i-th vehicle in the truck platoon; The workshop distance is: g i =x i+1 -x i -l Among them, g i represents the distance between the i-th vehicle and the vehicle in front, x i represents the position of the i-th vehicle; l represents the length of the vehicle in front of the i-th vehicle; The safety dimensions include: collision time countdown, headway, and lane change area; The inverse of the collision time is: ITTC=(v f -v r ) / (Sl) Among them, v f Indicates the speed of the preceding vehicle, v r It represents the speed of the rear vehicle, S represents the headway between the front and rear vehicles, and ITTC represents the inverse time to collision. The headway time is: THW=S v r Among them, THW represents the time headway; The lane-changing area is: the space area required for the platoon vehicles to perform lane-changing operations on the road; The ecological dimensions include: fuel consumption saving rate, CO emission saving rate, CO2 emission saving rate, NO x Emissions savings rate.

5. The method for evaluating the driving ability of the pilot vehicle of the intelligent connected truck platoon in a typical scenario according to claim 1 is characterized in that: The desktop video experiment includes: pre-experimental subjective testing, pre-experimental driving training, pre-experimental and formal experiments; The subjective test before the experiment was: a driving ability test based on a subjective questionnaire; The formal experiment is: a manipulation behavior test based on a desktop video experiment.

6. The method for evaluating the driving ability of the pilot vehicle of the intelligent connected truck platoon in a typical scenario according to claim 1 is characterized in that: The driver's driving ability includes: theoretical knowledge of truck platooning, practical skills of truck platooning and the driver's psychological state.

7. The method for evaluating the driving ability of the pilot vehicle of a smart connected truck platoon in a typical scenario according to claim 1 is characterized in that: The relationship model between the driver's driving ability and control behavior is: y1=a o +a1x1+a2x2+a3x3+ε1 y2=b o +b1x1+b2x2+b3x3+ε2 Among them, y1 represents the prediction model between driving ability and takeover behavior, y2 represents the prediction model between driving ability and lane changing behavior, x1 represents the theoretical knowledge score, x2 represents the practical skill score, x3 represents the psychological state score, a1, a2, a3, b1, b2, b3 represent the coefficients in the constraints, a0, b0 represent the intercept terms in the linear regression model, and ε1, ε2 represent the residuals.

8. The method for evaluating the driving ability of the pilot vehicle of a smart connected truck platoon in a typical scenario according to claim 1 is characterized in that: Reversely solving the relationship model based on the manipulation behavior threshold includes: Taking the driver's control behavior level as the optimization target and the control behavior threshold as the constraint condition, a solution model for the minimum driving ability requirement is constructed; Based on the solution model, the relationship model is solved.

9. The method for evaluating the driving ability of the pilot vehicle driver of a smart connected truck platoon under typical scenarios according to claim 8 is characterized in that: The solution model is: Objective function: Min Z = c1x1 + c2x2 + c3x3 Constraints: a0+a1x1+a2x2+a3x3+ε1≥y1 b0+b1x1+b2x2+b3x3+ε2≥y2 Capacity constraints: x1+x2+x3≤300 Non-negativity constraints: x1,x2,x3≥0 in, Z Indicates the control time. S.t Represents a constraint.

Images