CN114323698B - Real vehicle experiment platform testing method for man-machine co-driving intelligent vehicle - Google Patents
Real vehicle experiment platform testing method for man-machine co-driving intelligent vehicle Download PDFInfo
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Abstract
The invention relates to a test method for an intelligent automobile real vehicle experiment platform for man-machine co-driving, which mainly comprises the following steps: step one: on the basis of an E-HS3 real vehicle platform, a controllable motor and a moment/angle sensor are additionally arranged in a steering system, so that interfaces of man-machine co-driving are met, and the platform has three modes of human driver driving, intelligent driving system driving and human driver and intelligent driving system sharing driving; step two: on the basis of the first step, a millimeter wave radar, a camera, a GPS positioning system, an industrial personal computer and a bottom layer control system are deployed for a man-machine co-driving system to form a closed loop system containing external information; step three: aiming at the characteristics of man-machine co-driving real vehicle experiments, a test frame which accords with man-machine co-driving is designed. According to the invention, a high-efficiency and reliable man-machine co-driving experiment platform is designed aiming at the experimental and testing problems of the man-machine co-driving intelligent automobile, and a corresponding testing control frame is designed, so that the platform can effectively verify the characteristics of man-machine co-driving in a real automobile environment.
Description
Technical Field
The invention relates to a test method for a test platform of a man-machine co-driving intelligent automobile, in particular to a driving test platform and a test method for meeting the requirements of a man-machine co-driving intelligent automobile in a real automobile environment.
Background
Automatic driving vehicles have been rapidly developed in the past years, but the completely unmanned driving of the level L4 and L5 still has many safety problems and legal policy problems, so that Advanced Driving Assistance Systems (ADAS) based on shared control have been increasingly studied, and unlike the previous driving assistance, intelligent controllers in man-machine co-driving can continuously assist drivers in the control field to perform safe driving, thereby improving driving safety and reducing driving burden of drivers. In the research process of the man-machine co-driving research technology, the testing and evaluating technology is widely researched in the industry, and particularly, the establishment of an experimental platform for man-machine co-driving and the establishment of evaluation standards are realized.
For the establishment of an experiment platform for man-machine co-driving, a hardware-in-the-loop experiment platform is widely developed in the industry, jiangsu university Jiang Hao and the like develop a set of man-machine co-driving hardware-in-the-loop experiment platform comprising a PC (personal computer), a man-machine co-driving steering ECU (electronic control unit), a driving simulator, a front torque/rotation angle sensor, a rear torque/rotation angle sensor, a CAN (controller area network) card and a data acquisition device, and the platform has two modes of man-driving and machine driving, so that development cost CAN be effectively reduced, but the platform cannot consider the vehicle motion characteristics under the actual road environment (Chinese patent: CN, CN107727417A, a hardware-in-the-loop simulation test platform for a man-machine co-driving steering system).
The Jilin university Zhu Bing discloses a driving test platform for intelligent automobile man-machine co-driving, which mainly introduces the mechanical construction principle of the test platform, and still lacks the test of a landing scene (Chinese patent: CN, CN109493681A, a driving test platform for intelligent automobile man-machine co-driving). It can be seen that further research is still needed on the experimental platform based on man-machine co-driving under the condition of considering the actual road environment and the actual vehicle.
For the establishment of test and evaluation standards of man-machine co-driving experiments, different evaluations are currently carried out from different objects in the industry, and a fault generation and control switching detection module is constructed by Jilin university Shi Shuming and the like, so that a set of man-machine co-driving reliability evaluation method is formed, but the indexes such as driver burden, man-machine co-driving cooperative performance and the like still lack specific explanation (Chinese patent: CN, CN107871418A, an experimental platform for evaluating man-machine co-driving reliability). Therefore, the test and evaluation method of the man-machine co-driving experiment still needs to be further perfected, and the lane keeping performance, the driver operation load and the man-machine co-driving cooperative performance should be more paid attention to evaluation.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a real vehicle experiment platform testing method for a man-machine co-driving intelligent automobile, wherein a man-machine co-driving interface is required to be introduced into the platform, a sensor containing external information and an industrial personal computer for information processing are deployed, and a corresponding experiment testing framework is designed according to the man-machine co-driving characteristics.
In order to achieve the purpose, the invention provides a real vehicle experiment platform test method for a man-machine co-driving intelligent vehicle, which adopts the following technical scheme: step one: on the basis of an E-HS3 real vehicle platform, an EPS power-assisted motor of an original vehicle is required to be shielded, so that the influence of an original vehicle power-assisted system on a man-machine co-driving system is counteracted, a controllable motor and a moment/angle sensor are additionally arranged in a steering system, an interface meeting the man-machine co-driving is constructed, and the platform has three modes of human driver driving, intelligent driving system driving and human driver and intelligent driving system sharing driving; step two: on the basis of the first step, a millimeter wave radar, a camera, a GPS positioning system, an industrial personal computer and a bottom layer control system are deployed for a man-machine co-driving system, so that a closed loop test system containing external information is formed; step three: aiming at the characteristics of man-machine co-driving real vehicle experiments, a test frame which accords with man-machine co-driving is designed. And evaluating the experimental result by a subjective and objective experimental evaluation scheme.
Further, (1) designing a steering system meeting a man-machine co-driving interface on the basis of an E-HS3 real vehicle platform, which comprises the following steps:
(1) firstly, because the moment and angle signals of the original test vehicle E-HS3 steering system cannot be obtained, the original moment and angle signals of the vehicle must be shielded, so that the influence of the signals on the analysis of the man-machine co-driving system after the modification is finished, namely the power assisting T of the original vehicle EPS, is avoided eps Is approximately equal to 0 and is,
T eps ≈0 (1)
(2) then a torque/corner reading sensor and a torque/angle motor are additionally arranged on the steering column, the sensor can read the torque and the angle of the steering of the vehicle, the motor can execute control signals, under the premise that the control signals of the torque/angle are output by the industrial personal computer (namely, the output of the controller) are known, the control input of a driver is additionally arranged, and the resultant torque/angle acting on the additionally arranged motor can be finally obtained, so that the scheme can reach the standard of touch type man-machine co-driving, and when the output of the controller is shielded, the mode is the individual driving mode; when the driver does not operate the steering wheel and the controller continuously outputs the control quantity, the controller driving mode is adopted; when the driver continuously operates the steering wheel and the controller continuously outputs the control quantity, the man-machine co-driving mode is adopted.
The human driver drives alone:
T sensor ＝T d (2)
and (3) driving by an intelligent driving system:
T sensor ＝T c (3)
human driver and intelligent driving system share driving:
T sensor ＝T d +T c (4)
wherein ,Tsensor Is the torque read by a sensor arranged on the steering column, T d Is the magnitude of the moment exerted on the steering wheel by the driver, T c Is the magnitude of the torque applied to the steering column by the intelligent driving system.
Further, (2) establishing a closed-loop man-machine co-driving test system containing external information, which mainly comprises the following steps:
the industrial personal computer is connected with the sensing system through 2 paths of CAN (one path is responsible for sending the yaw rate and the speed of the vehicle to the camera, and the other path is responsible for acquiring the information processed by the camera and the millimeter wave radar). The camera is connected with the millimeter wave radar through a 1-path CAN. The industrial personal computer is connected with a high-precision positioning system through 1 path of USB (used for acquiring information of the position, the direction angle and the like of the self-vehicle), and the GPS module is connected with the 4G module through one path of RS232 (used for solving the network problem of the positioning system). The bottom layer controller is connected with the industrial personal computer through a 1-way CAN, and is required to send commands to the controller and receive information of steering wheel angles and torque from the controller. Because the industrial personal computer only has 2 paths of CAN, the CAN for transmitting the yaw rate and the vehicle speed of the vehicle to the camera is connected with the CAN of the bottom controller in parallel, and no conflict exists between the CAN ID is required. The vehicle-mounted hardware is directly powered by the vehicle-mounted battery to provide 12V direct current.
Further, (1) deploying a perception system: the system is a set of fusion sensing device comprising millimeter wave radar and a camera, and is used for inputting the speed V of a vehicle ego And yaw rate gamma ego On the premise of the information, a position (x obs ,y obs ) Direction angle psi obs Velocity V obs Size information size of the display device obs (l, w, h). Wherein the position information of the obstacle output by the sensor is described by a track:
Traj obs ＝{(x obs ,y obs ):f(x obs ,y obs )＝0} (5)
if the lane line is the form of a binary linear function:
Traj obs ＝{(x obs ,y obs ):Ax obs +By obs +C＝0} (6)
wherein A, B, C are coefficients of the corresponding function.
Further, (2) deploying a high precision positioning system: the system is a set of high-precision positioning system based on differential (RTK) GPS and Inertial Measurement Unit (IMU) and is used for acquiring the position (x) ego ,y ego ) Direction angle psi ego Angular velocity gamma ego And the like. The part of information can be directly analyzed and then input into an industrial personal computer for online processing. It should be noted that since the differential technology requires network support, an additional set of 4G modules needs to be deployed to meet the network requirements of the system. The relative position d of the vehicle and the obstacle can be obtained by knowing the position information, the direction angle, the speed and the like of the vehicle and the obstacle rel Relative direction angle ψ rel Relative yaw rate gamma rel Etc.
ψ rel ＝ψ ego -ψ obs (8)
γ rel ＝γ ego -γ obs (9)
Further, (3) deploying an upper layer processing unit: the system takes a vehicle-standard industrial personal computer as a carrier and is used for carrying out information processing and real-time calculation of control commands. The control command of steering wheel moment/angle and acceleration CAN be output for the bottom layer executor to process, and the calculation of the control command is based on the control signal, i.e. moment T, calculated in real time c ，
δ c ＝C 1 d rel +C 2 ψ rel +C 3 γ rel (10)
T c ＝Kδ c (11)
wherein ,C1 Is a coefficient related to the relative position of the bicycle and the obstacle, C 2 Is a coefficient related to the relative angle between the vehicle and the obstacle, C 3 Is a coefficient relating to the yaw rate of the vehicle relative to the obstacle. Output control moment T c And controlling the rotation angle delta c There is a conversion coefficient K between them.
Further, (4) deploy an underlying control system: a steering floor controller for receiving steering wheel angle or torque commands, and a longitudinal control system for receiving acceleration commands. Meanwhile, the bottom layer control system CAN also provide chassis CAN bus state information such as vehicle speed, steering wheel angle, moment and the like for the upper layer processing unit.
Further, (3) aiming at the characteristics of the man-machine co-driving real vehicle experiment, a test control method framework which accords with the man-machine co-driving is designed, and the experimental result is evaluated by a subjective and objective experimental evaluation scheme, and mainly comprises the following steps:
the test frame is mainly produced according to the position information and the external environment information of the high-precision positioning systemGenerating a reference track, obtaining deviation according to the position information and the attitude information of the reference track, and finally obtaining a moment regulator based on fuzzy PID and active disturbance rejection, thereby achieving the purpose of controlling the transverse movement of the vehicle; comprising the following steps: (1) obtaining the angle required by steering by a fuzzy PID method, (2) obtaining the tracking angle delta by an active disturbance rejection method c Moment T of (2) c 。
Further, (1) obtaining the angle required for steering by a fuzzy PID method:
in order to make the controller quickly and stably, a corresponding fuzzy rule is designed so as to update the parameter k in the PID algorithm in real time p ,k I ,k d Respectively obtaining parameters updated in real timeAnd get->The control angle of the final output is delta c ，
wherein ,δd The target rotation angle based on the reference track is used as a feedforward response to act on a subsequent active disturbance rejection controller, and the main purpose of the target rotation angle based on the reference track is to obtain a rapid and accurate rotation angle response.
(2) Obtaining tracking angle delta by active disturbance rejection method c Moment T of (2) c ：
Because a coefficient K exists between the control force rejection and the control rotation angle, the invention calculates the control moment T by designing an active disturbance rejection algorithm c To control the angle delta as much as possible c ，
wherein , x i i=1, 2 is the system state quantity, r, h is the adjustable parameter, T is the sampling step size, z i I=1, 2,3 is the expansion state quantity, β i I=1, 2,3 is an adjustable parameter, α, δ, b is an adjustable parameter, e i I=1, 2 is the deviation of the input state from the expanded state quantity, and u is the regulating torque acting on the steering column.
A coefficient K exists between the control force rejection and the control rotation angle, and the control moment T is calculated by designing an active disturbance rejection algorithm c To track and control the angle delta c See equation (16).
Further, for evaluation of experimental results, subjective evaluation of a man-machine co-driving system is performed by respectively considering four indexes of driving safety, driving accuracy, driving comfort and overall driving experience in the man-machine co-driving process. The driving safety comprises whether the driving safety can be improved, whether traffic accidents can be reduced and whether the error behaviors of a driver can be compensated; driving accuracy includes accuracy and smoothness during driving that the driver subjectively understands; driving comfort includes subjective assessment of physical and psychological load of the driver; the overall evaluation comprises the trust degree of the driver on the man-machine co-driving system, wherein the evaluation items respectively comprise gender (1), driving age (2), age (3), driving safety (4-5), driving accuracy (6-7), driving comfort (8-10) and overall evaluation (11-13). The method comprises the steps of carrying out a first treatment on the surface of the Scoring included 1-5 points (1-disagree, 2-disagree, 3-neutral, 4-slightly agreeable, 5-very agreeable);
(4) The objective evaluation of the man-machine co-driving system respectively considers the lane keeping performance, the driver operation load and the man-machine co-driving cooperative performance.
The lane keeping performance mainly considers the lane departure degree, the track tracking precision and the average passing time; the driver operation accords with the requirement of mainly considering the change of the moment of the driver, the change of the transverse acceleration and the change rate of the transverse acceleration; the man-machine cooperative control performance mainly considers the steering correction force and the steering correction frequency of a driver in the man-machine co-driving process.
(1) Lane keeping performance index:
degree of lane departure: including the relative positional deviation d rel Relative angle deviation ψ rel ；
Track tracking accuracy: including deviations of the actual trajectory from the desired trajectory.
Traj error ＝{(x ego -x obs ,y ego -y obs ):A error Δx+B error Δy+C error ＝0} (17)
wherein ,Aerror ，B error ，C error Is a correlation coefficient.
Average transit time: under the condition of ensuring the lane departure degree and the track tracking error precision, the vehicle can pass through quickly
Where l is the test road distance, v (d rel ,ψ rel ,Traj pre ) Is the longitudinal speed of the vehicle affected by the degree of lane departure and the trajectory tracking error, and t is the vehicle transit time. d, d pre Is a distance safety threshold, ψ pre Is the direction angle safety threshold, traj pre Is a trajectory safety threshold.
(2) Driver operation load index:
driver torque: respectively obtain the driving of the driver when driving aloneDriver torque T in driver torque and human-machine co-driving mode d,d ,T d,cop 。
Lateral acceleration and rate of change: respectively acquiring the moment of the driver and the lateral acceleration and the change rate a under the man-machine co-driving mode when the driver singly drives y ,Δa y 。
(3) And the man-machine co-driving cooperative performance index:
steering correction force: deviation T of controller moment and steering correcting moment cor ＝T c -T al 。
wherein ,Tal ＝-F yf (t m +t p ) Is steering aligning moment, F yf Is the lateral force of the tyre, t m Is the distance t between the ground and the tread of the tire, which is the backward inclined extension line of the main pin p Is the tire support distance F yf Can be approximately estimated to obtain t m ,t p Usual parameters may be taken.
Driver steering correction rate: the number of times the driver corrects the steering wheel in a certain time. Compared with the existing man-machine co-driving experiment platform, the invention has the following advantages:
1. the steering system modified based on the real vehicle E-HS3 can meet the requirements of man-machine co-driving, forms a touch type man-machine co-driving mode under a real vehicle platform, and has three different switchable driving modes: the driver drives, intelligent driving system drives, driver and intelligent driving system share drive, and compared with other hardware in-loop platforms, the platform can effectively meet the actual vehicle experiment requirement of the man-machine co-driving system. 2. The invention constructs the closed-loop man-machine co-driving real vehicle test system containing external information, and can more accurately verify the movement characteristics of man-machine co-driving in a real environment by on-line processing of external road and obstacle information, vehicle position and posture and other information.
3. According to the invention, aiming at the characteristic of man-machine co-driving, a test control frame conforming to man-machine co-driving is designed, and the experimental result is evaluated by a subjective and objective experimental evaluation scheme, so that the man-machine co-driving real vehicle experimental platform can be effectively executed and evaluated.
Drawings
FIG. 1 is a schematic illustration of a steering system that satisfies a human-machine co-drive interface.
Fig. 2 is a hardware overall architecture of the man-machine co-driving experiment platform.
Fig. 3 is a test control structure of the man-machine co-driving experiment platform.
Fig. 4 is a subjective evaluation analysis of the man-machine co-driving experiment.
Fig. 5 is a longitudinal velocity tracking feature.
Fig. 6 is a trace tracking variation for a driver driving alone and co-driving with a human.
Fig. 7 is a graph of driver torque variation for a driver driving alone and co-driving with a human.
Fig. 8 is a graph of vehicle lateral deviation variation for a driver driving alone and co-driving with man-machine.
Detailed Description
The following description of the embodiments of the present invention will be made apparent and fully in view of the accompanying drawings, in which some, but not all embodiments of the invention are shown. The examples listed below are only a further understanding and implementation of the technical solution of the present invention and do not constitute a further limitation of the claims of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The following further describes the details of the present invention and its embodiments.
(1) Steering system refitted to meet man-machine co-driving interface
Considering the richness of the driving modes of the man-machine co-driving real vehicle experiment platform, there are three modes of human driver driving, intelligent driving system driving and human driver and intelligent driving system sharing driving, the man-machine co-driving platform needs to meet the following conditions:
(1) the control signal of the intelligent system can be completely shielded when the human driver is driving.
(2) When the intelligent system is driven independently, the control signals of people can be completely shielded.
(3) When a human driver and an intelligent driving system share driving, the human and the intelligent systems can jointly act on the steering system and can mutually perceive.
In order to meet the above conditions, the invention improves the steering system of the red flag E-HS3 vehicle, the structure of the steering system is shown in figure 1, wherein the power-assisted motor (shielding) of the original vehicle is arranged in a dotted line frame, the motor and the torque/angle sensor are additionally arranged in a solid line frame, the sensor can read the torque and the angle of the steering of the vehicle, and the motor can execute control signals. Under the premise of knowing a control signal of the torque/angle output by the industrial personal computer (namely the output of the controller), the control input of a driver is additionally added, and finally, the added torque/angle sensor can obtain the resultant torque/angle acting on the steering column. Because the EPS open source signal of the original vehicle cannot be obtained, the invention can only shield the EPS open source signal of the original vehicle, the final result is that the steering power of the vehicle is reduced, the motor outputs a smaller moment of inertia to act on the steering column, the result can be ignored approximately, thereby avoiding the influence of the output of the motor of the original vehicle on the man-machine co-driving experiment, and the power assisted T of the motor of the original vehicle is used for the power assisted T of the motor of the original vehicle eps Is small enough so that it can be ignored, i.e. T eps ≈0。
Table 1 vehicle lateral test data after retrofitting a steering system
When the human driver drives, the moment obtained by the sensor is the moment applied to the steering wheel by the human driver, as shown in formula (2).
When the intelligent system is driven alone, the torque obtained by the sensor is the torque applied to the steering column by the intelligent system, as shown in formula (3).
When the human driver and the intelligent driving system share driving, the moment obtained by the sensor is the sum of the moment of the human driver and the moment of the intelligent driving system, as shown in a formula (4).
Therefore, the structure can be shown to provide three driving modes for human-machine co-driving real vehicle experiments: human driver driving, intelligent driving system driving, human driver sharing driving with intelligent driving system.
In order to further verify the effectiveness of the structure, the modified steering system is subjected to a transverse steering test, and left and right turning operations of different angles are respectively carried out on the steering wheel, so that the obtained results are shown in a table 1, and the results of the table 1 show that the zero drift of the steering system of the modified vehicle is almost within 0.4deg, the steering angle steady-state error is ensured to be within 0.5deg, the maximum overshoot of the steering wheel is within 6.9deg, and the steering system meets the steering performance.
(2) Establishing a closed-loop man-machine co-driving test system containing external information
The main content of the part is to deploy a sensing system, a high-precision positioning system, an upper processing unit and a bottom control system for a vehicle, wherein the hardware architecture is shown in fig. 2, the hardware information is shown in table 2, and the main content comprises the following steps:
(1) deploying a perception system: the system is a set of fusion sensing device comprising millimeter wave radar and a camera, and is used for inputting the speed V of a vehicle ego And yaw rate gamma ego On the premise of the information, a position (x obs ,y obs ) Direction angle psi obs Velocity v obs Size information size of the display device obs (l, w, h). Wherein the position information of the obstacle output by the sensor is described by a track, as in formula (5), and if the track is a lane line, the track is in the form of a binary linear function, as in formula (6).
(2) Deploying a high-precision positioning system: the system is a set of high-precision positioning system based on differential (RTK) GPS and Inertial Measurement Unit (IMU) and is used for acquiring the position (x) ego ,y ego ) Direction angle psi ego Angular velocity gamma ego And the like. The part of information canAnd directly analyzing and then inputting the analyzed data into an industrial personal computer for online processing. It should be noted that since the differential technology requires network support, an additional set of 4G modules needs to be deployed to meet the network requirements of the system. The relative position d of the vehicle and the obstacle can be obtained by knowing the position information, the direction angle, the speed and the like of the vehicle and the obstacle rel Relative direction angle ψ rel Relative yaw rate gamma rel And the like, as in formulas (7) - (9).
(3) Deploying an upper layer processing unit: the system takes a vehicle-standard industrial personal computer as a carrier and is used for carrying out information processing and real-time calculation of control commands. The control command is mainly used for receiving vehicle-mounted CAN bus information, information such as the position and the gesture of a high-precision positioning system, and road and obstacle information of a fusion sensing system, outputting control commands of steering wheel moment/rotation angle and acceleration for a bottom layer executor to process, calculating the control commands mainly based on information processing and calculated control signals in real time, wherein the control signals comprise rotation angle delta c And moment T c As shown in formulas (11) and (12).
(4) Deploying an underlying control system: a steering floor controller for receiving steering wheel angle or torque commands, and a longitudinal control system for receiving acceleration commands. Meanwhile, the bottom layer control system CAN also provide chassis CAN bus state information such as vehicle speed, steering wheel angle, moment and the like for the upper layer processing unit.
In the aspect of hardware line connection, the industrial personal computer is connected with the sensing system through a 2-way CAN (one way is responsible for sending the yaw rate and the speed of the vehicle to the camera, and the other way is responsible for acquiring the information processed by the camera and the millimeter wave radar). The camera is connected with the millimeter wave radar through a 1-path CAN. The industrial personal computer is connected with a high-precision positioning system through 1 path of USB (used for acquiring information of the position, the direction angle and the like of the self-vehicle), and the GPS module is connected with the 4G module through one path of RS232 (used for solving the network problem of the positioning system). The bottom layer controller is connected with the industrial personal computer through a 1-way CAN, and is required to send commands to the controller and receive information of steering wheel angles and torque from the controller. Because the industrial personal computer only has 2 paths of CAN, the CAN for transmitting the yaw rate and the vehicle speed of the vehicle to the camera is connected with the CAN of the bottom controller in parallel, and no conflict exists between the CAN ID is required. The vehicle-mounted hardware is directly powered by the vehicle-mounted battery to provide 12V direct current.
TABLE 2 Main hardware information
(3) Test control method framework for designing man-machine co-driving system
The test structure of the invention is shown in fig. 3, wherein the longitudinal control is controlled by a PD algorithm, the accelerator pedal of the vehicle is continuously regulated, the longitudinal speed of the vehicle can be kept at a stable constant, the transverse control mainly generates a reference track according to the position information and the external environment information of a high-precision positioning system, the transverse deviation amount is obtained according to the position information and the posture information of the vehicle, and finally the rotation angle or the moment of the steering motor is regulated by a rotation angle mode based on fuzzy PID or a moment mode based on fuzzy PID and active disturbance rejection, thereby achieving the purpose of controlling the transverse movement of the vehicle.
And (3) longitudinal control:
(1) controlling the longitudinal speed of the vehicle in a PD method:
V error ＝V ego -V d (19)
wherein ,Vego Is the actual longitudinal speed of the vehicle, V d Is the target longitudinal speed of the vehicle, V error Is the longitudinal speed deviation of the vehicle, u V Is the adjustment amount of the accelerator pedal.
And (3) transverse control:
(2) the steering required angle is obtained by a fuzzy PID method, and the results are shown in formulas (12) - (15).
(3) Obtaining tracking angle delta by active disturbance rejection method c Moment T of (2) c The result is shown in formula (16).
For subjective evaluation of the man-machine co-driving system, specific evaluation items are shown in table 3, and the evaluation items respectively comprise gender (1), driving age (2), age (3), driving safety (4-5), driving accuracy (6-7), driving comfort (8-10) and overall evaluation (11-13). The method comprises the steps of carrying out a first treatment on the surface of the The scores included 1-5 points (1-disagree, 2-disagree, 3-neutral, 4-slightly agreeable, 5-very agreeable).
Table 3 subjective evaluation items
Finally, subjective evaluation results of different drivers are obtained, and the results can be averagedMean square error->Root mean square error->And processing, so as to further analyze subjective evaluation of different drivers on the man-machine co-driving system.
(4) For objective evaluation of the co-driving system, as shown in table 4, average value, mean square error and root mean square error can be considered in analyzing the data.
(1) Lane keeping performance index:
degree of lane departure: including the relative positional deviation d rel Relative angle deviation ψ rel The method comprises the steps of carrying out a first treatment on the surface of the Track tracking accuracy: including deviations of the actual trajectory from the desired trajectory as in equation (17); average transit time: under the condition of ensuring the lane departure degree and the track tracking error precision, the vehicle can pass through quickly, as shown in the formula (18).
(2) Driver operation load index:
driver torque: respectively acquiring the driver moment when the driver singly drives and the driver moment T in the man-machine co-driving mode d,d ,T d,cop . Lateral acceleration and rate of change: respectively acquiring the moment of the driver and the lateral acceleration and the change rate a under the man-machine co-driving mode when the driver singly drives y ,Δa y 。
(3) And the man-machine co-driving cooperative performance index:
steering correction force: deviation T of controller moment and steering correcting moment cor ＝T c -T al. wherein ,Tal ＝-F yf (t m +t p ) Is steering aligning moment, F yf Is the lateral force of the tyre, t m Is the distance t between the ground and the tread of the tire, which is the backward inclined extension line of the main pin p Is the tire support distance F yf Can be approximately estimated to obtain t m ,t p Usual parameters may be taken. Driver steering correction rate: the number of times the driver corrects the steering wheel in a certain time.
Table 4 objective evaluation items
On the basis of three parts, the first step ensures a hardware interface of the man-machine co-driving, the second step ensures a closed-loop test containing external information, and the third step ensures a man-machine co-driving test method and an evaluation scheme.
(1) The experimental road selects unmanned rotary island in-out road conditions, and approximately simulates double-lane-change working conditions.
(2) The reference track is obtained after planning according to positioning information (GPS), gesture heading Information (IMU) and environment information (millimeter wave radar+camera).
(3) In order to verify the effectiveness of the experimental platform, PD self-adaptive cruise control is adopted in longitudinal control, the speed of the vehicle is controlled to be 15km/h, fuzzy PID+active disturbance rejection control algorithm is adopted in transverse control, and a driver can control a steering wheel in real time according to environmental information so as to control the vehicle.
(4) And selecting drivers of different age groups to perform real vehicle man-machine co-driving test.
The statistics of the results of the subjective evaluation items of the drivers of different ages are shown in table 5, and the data analysis after the processing is shown in fig. 4.
The average value is analyzed, so that in the aspect of safety, the driver can maintain a relatively conservative attitude for the co-driving system to improve the driving safety, but the co-driving system can make up for the error behavior of the driver when the driver has the conditions of error operation, fatigue driving and the like; in terms of accuracy, drivers are authorized to accept the co-driving system, so that the vehicle can be kept in the middle of a lane more easily and can run more stably; in terms of comfort, the driver considers that the co-driving system can reduce the driving physical burden of the driver and can adapt to the co-driving system mutually, but the less accepted co-driving system can reduce the driving psychological burden of the driver, which is probably due to the fact that the coordination between the human and the machine is inconsistent, a running-in process is needed between the driver and the intelligent controller, so that the rationality of the human-machine co-driving system is improved; finally, it can be seen that the driver as a whole can trust the co-driving system and consider it to be possible to equip the co-driving system.
The method has the advantages that the method can be obtained by analyzing and analyzing the variance and the average value, and different drivers have larger divergence in the problem that the driving safety can be improved by the co-driving system, and the problem is probably due to the fact that few drivers potentially feel to fight against the co-driving system in mind, so that the controller can fight against the drivers, and the safety is influenced; however, from the overall evaluation, different drivers have use value on the co-driving system, and the fact that the co-driving system is provided with the minimum divergence is considered, so that on the basis of the above experiment, drivers with different sexes, driving ages and ages generally have positive optimistic attitudes on the co-driving system.
Table 4 statistics of subjective evaluation items of co-driving of man-machine
The objective evaluation of the experimental results is shown in fig. 5-8, and fig. 5 shows that the man-machine co-driving real vehicle experimental platform not only can realize transverse control, but also can study the longitudinal control problem, and further can carry out a series of transverse and longitudinal coupling test experiments. Fig. 6 shows that the man-machine co-driving real vehicle experimental platform can be used for analyzing the change characteristics of track tracking when a driver drives alone and drives together with a man-machine, and the track tracking precision under the man-machine co-driving is higher than that of the driver alone; FIG. 7 shows that the platform can be used to analyze the moment operating load of the driver when the driver drives alone and co-drives with a human-machine, thereby analyzing the influence on the driver, and the process that the driver may have a countermeasure with the intelligent system in the process of co-driving with the human-machine can be seen; fig. 8 shows that the platform can be used for analyzing the lateral deviation of the independent driving of the driver and the co-driving of the human-machine, namely, the track tracking deviation of different driving modes can be analyzed, and therefore, the larger deviation can be suddenly generated when the driver singly drives, and the change of the lateral deviation in the co-driving process of the human-machine is relatively stable.
The invention only lists some experiments and analyses with feasibility, in practice, the platform can meet the experiments of track tracking test of a driver in three modes of independent driving, independent driving of an intelligent system and co-driving of a man and a machine, load test of the driver, anti-interference test of the man and the machine under co-driving, characteristic analysis, model and algorithm verification and the like of changes of different variables (transverse deviation, transverse acceleration, yaw rate and the like), and in addition, the invention can also study transverse and longitudinal coupling conditions of vehicle dynamics in different modes.
Although embodiments of the invention have been disclosed above, they are not limited to the use listed in the specification and embodiments. It can be applied to various fields suitable for the present invention. Additional modifications will readily occur to those skilled in the art. Therefore, the invention is not to be limited to the specific details and illustrations shown and described herein, without departing from the general concepts defined in the claims and their equivalents.
Claims (7)
1. A real vehicle experiment platform test method for a man-machine co-driving intelligent vehicle comprises the following design steps:
step one: on the basis of an E-HS3 real vehicle platform, an EPS power-assisted motor of an original vehicle is shielded, a controllable motor and a moment/angle sensor are additionally arranged in a steering system, so that an interface meeting the requirements of man-machine co-driving is constructed, and the platform has three modes of human driver driving, intelligent driving system driving and shared driving of a human driver and the intelligent driving system;
step two: on the basis of the first step, a millimeter wave radar, a camera, a GPS positioning system, an industrial personal computer and a bottom layer control system are deployed for the co-driving system of the man-machine, so that a closed-loop co-driving test system of the man-machine containing external information is formed;
step three: aiming at the characteristics of man-machine co-driving real vehicle experiments, a test frame conforming to man-machine co-driving is designed; wherein:
in the second step, the closed-loop man-machine co-driving test system specifically comprises: (1) deploying a sensing system, (2) deploying a high-precision positioning system, (3) deploying an upper-layer processing unit, and (4) deploying a bottom-layer control system;
the deployment awareness system: the system is a set of fusion sensing device comprising millimeter wave radar and a camera, and is used for inputting the speed V of a vehicle ego And yaw rate gamma ego On the premise of the information, for acquiring a position (x obs ,y obs ) Direction angle psi obs Velocity V obs Size information size of the display device obs (l, w, h) wherein the position information of the obstacle output by the sensor is described by a trace:
Traj obs ＝{(x obs ,y obs ):f(x obs ,y obs )＝0} (5)
if the lane line is the form of a binary linear function:
Traj obs ＝{(x obs ,y obs ):Ax obs +By obs +C＝0} (6)
wherein A, B and C are the coefficients of the corresponding functions;
the deployment high-precision positioning system comprises: the system is a set of high-precision positioning system based on differential (RTK) GPS and Inertial Measurement Unit (IMU) and is used for acquiring the position (x) ego ,y ego ) Direction angle psi ego Angular velocity gamma ego Waiting for positioning information; the part of information can be directly analyzed and then input into an industrial personal computer for online processing; because the differential technology needs network support, a set of 4G modules needs to be deployed additionally to meet the network requirements of the system; the relative position distance d between the vehicle and the obstacle is obtained by knowing the position information, the direction angle and the angular velocity information of the vehicle and the obstacle rel Relative direction angle ψ rel Relative yaw rate gamma rel Information;
ψ rel ＝ψ ego -ψ obs (8)
γ rel ＝γ ego -γ obs (9)；
the deployment upper layer processing unit: the system takes a vehicle-standard industrial personal computer as a carrier and is used for carrying out information processing and real-time calculation of control commands; the information processing comprises information input of a sensing system, high-precision positioning system input, vehicle-mounted CAN information input and control signal input; the calculation of the control command is based mainly on control signals calculated in real time by information processing, i.e. the torque T c ，
δ c ＝C 1 d rel +C 2 ψ rel +C 3 γ rel (10)
T c ＝Kδ c (11)
wherein ,C1 is a coefficient related to the relative position of the bicycle and the obstacle, C 2 Is a coefficient related to the relative angle between the vehicle and the obstacle, C 3 The coefficient of the yaw rate of the vehicle relative to the obstacle is used for outputting a control torque T c And controlling the rotation angle delta c There is a conversion coefficient K between them.
2. The test method of the real vehicle experiment platform for the man-machine co-driving intelligent vehicle according to claim 1, wherein in the first step, on the basis of the E-HS3 real vehicle platform, an EPS (electric power steering) assisting motor of an original vehicle is firstly required to be shielded, so that the influence of an original vehicle assisting system on the man-machine co-driving system is counteracted, namely, the assisting force of the original vehicle EPS is approximately equal to 0, then a controllable motor and a moment/angle sensor are added in a steering system, so that an interface meeting the man-machine co-driving requirement is constructed,
T eps ≈0 (1)
finally, three modes of human driver driving, intelligent driving system driving and human driver and intelligent driving system sharing driving are formed:
(1) the human driver drives alone:
T sensor ＝T d (2)
(2) and (3) driving by an intelligent driving system:
T sensor ＝T c (3)
(3) human driver and intelligent driving system share driving:
T sensor ＝T d +T c (4)
wherein ,Teps Is EPS assistance of the original vehicle, T sensor Is the torque read by a sensor arranged on the steering column, T d Is the magnitude of the moment exerted on the steering wheel by the driver, T c Is the magnitude of the torque applied to the steering column by the intelligent driving system.
3. The method for testing the real vehicle experimental platform of the man-machine co-driving intelligent vehicle according to claim 1, wherein the establishing a closed-loop man-machine co-driving testing system containing external information in the second step comprises the following steps:
the industrial personal computer is connected with the sensing system through 2 paths of CAN, one path is responsible for sending the yaw rate and the vehicle speed of the vehicle to the camera, the other path is responsible for acquiring the information processed by the camera and the millimeter wave radar, the camera is connected with the millimeter wave radar through 1 path of CAN, the industrial personal computer is connected with the high-precision positioning system through 1 path of USB and is used for acquiring the information of the position, the direction angle and the like of the vehicle, and the GPS module is connected with the 4G module through one path of RS232 and is used for solving the network problem of the positioning system; the bottom layer controller is connected with the industrial personal computer through a 1-way CAN, and is required to not only send a command to the controller, but also receive information of steering wheel rotation angle and torque from the controller; because the industrial personal computer only has 2 paths of CAN, the CAN for transmitting the yaw rate and the vehicle speed of the vehicle to the camera is connected in parallel with the CAN of the bottom controller, and no conflict exists between CAN IDs; the vehicle-mounted hardware is directly powered by a vehicle-mounted battery to provide 12V direct current;
the closed-loop man-machine co-driving test system specifically comprises: (1) the method comprises the steps of (1) deploying a sensing system, (2) deploying a high-precision positioning system, (3) deploying an upper-layer processing unit, and (4) deploying a bottom-layer control system.
4. The method for testing a real vehicle experimental platform for a man-machine co-driving intelligent vehicle according to claim 3, wherein the deployment bottom layer control system: a steering floor controller for receiving steering wheel angle or torque commands, and a longitudinal control system for receiving acceleration commands; meanwhile, the bottom layer control system CAN also provide chassis CAN bus state information such as vehicle speed, steering wheel angle, moment and the like for the upper layer processing unit.
5. The method for testing the real vehicle experimental platform for the man-machine co-driving intelligent vehicle according to claim 1, wherein in the third step, aiming at the characteristics of the man-machine co-driving real vehicle experiment, a test control framework which accords with the man-machine co-driving is designed, and the experimental result is evaluated by a subjective and objective experimental evaluation scheme, and the method comprises the following steps:
the test frame mainly generates a reference track according to the position information and the external environment information of the high-precision positioning system, obtains deviation according to the position information and the posture information of the test frame, and finally obtains a moment regulator based on fuzzy PID and active disturbance rejection, thereby achieving the purpose of controlling the transverse movement of the vehicle;
comprising the following steps: (1) obtaining the angle required by steering by a fuzzy PID method, (2) obtaining the tracking angle delta by an active disturbance rejection method c Moment T of (2) c 。
6. The method for testing the real vehicle experimental platform of the man-machine co-driving intelligent vehicle according to claim 5, wherein (1) the required steering angle is obtained by a fuzzy PID method:
in order to make the controller quickly and stably, a corresponding fuzzy rule is designed so as to update the parameter k in the PID algorithm in real time p ,k I ,k d Respectively obtaining parameters updated in real timeAnd get->The control angle of the final output is delta c ，
wherein ,δd The target rotation angle based on the reference track is used as a feedforward response to act on a subsequent active disturbance rejection controller, and the main purpose of the target rotation angle is to obtain a rapid and accurate rotation angle response;
the tracking angle delta is obtained by the active disturbance rejection method c Moment T of (2) c ：
wherein , x i i=1, 2 is the system state quantity, r, h is the adjustable parameter, T is the sampling step size, z i I=1, 2,3 is the expansion state quantity, β i I=1, 2,3 is an adjustable parameter, α, δ, b is an adjustable parameter, e i I=1, 2 is the deviation of the input state from the expanded state quantity, u is the regulating moment acting on the steering column;
a coefficient K exists between the control force rejection and the control rotation angle, and the control moment T is calculated by designing an active disturbance rejection algorithm c To track and control the angle delta c See equation (16).
7. The method for testing the real vehicle experimental platform for the man-machine co-driving intelligent vehicle according to claim 6, wherein the evaluation of the experimental result is performed by respectively considering four indexes of driving safety, driving accuracy, driving comfort and overall driving experience in the man-machine co-driving process for subjective evaluation of the experimental system for the man-machine co-driving intelligent vehicle; the driving safety comprises whether the driving safety can be improved, whether traffic accidents can be reduced and whether the error behaviors of a driver can be compensated; driving accuracy includes accuracy and smoothness during driving that the driver subjectively understands; driving comfort includes subjective assessment of physical and psychological load of the driver; the overall evaluation comprises the trust degree of a driver on a man-machine co-driving system; the evaluation items respectively comprise gender (1), driving age (2), age (3), driving safety (4-5), driving accuracy (6-7), driving comfort (8-10) and overall evaluation (11-13); scoring included 1-5 points (1-disagree, 2-disagree, 3-neutral, 4-slightly agreeable, 5-very agreeable);
the objective evaluation mainly evaluates the lane keeping performance, the operation load of the driver and the co-driving cooperative performance of the man-machine,
(1) lane keeping performance index:
degree of lane departure: including the relative positional deviation d rel Relative angle deviation ψ rel ；
Track tracking accuracy: the deviation between the actual track and the expected track is included;
Traj error ＝{(x ego -x obs ,y ego -y obs ):A error △x+B error △y+C error ＝0} (17)
wherein ,Aerror ，B error ，C error Is a correlation coefficient;
average transit time: under the condition of ensuring the lane departure degree and the track tracking error precision, the vehicle can pass through quickly,
where l is the test road distance, v (d rel ,ψ rel ,Traj pre ) Is the longitudinal speed of the vehicle affected by the degree of lane departure and the tracking error of the track, t is the vehicle transit time, d pre Is a distance safety threshold, ψ pre Is the direction angle safety threshold, traj pre Is a trajectory safety threshold;
(2) driver operation load index:
driver torque: respectively acquiring the driver moment when the driver singly drives and the driver moment T in the man-machine co-driving mode d,d ,T d,cop ；
Lateral acceleration and rate of change: respectively acquiring the moment of the driver and the lateral acceleration and the change rate a under the man-machine co-driving mode when the driver singly drives y ,△a y ；
(3) And the man-machine co-driving cooperative performance index:
steering correction force: deviation T of controller moment and steering correcting moment cor ＝T c -T al, wherein ,Tal ＝-F yf (t m +t p ) Is steering aligning moment, F yf Is the lateral force of the tyre, t m Is the distance t between the ground and the tread of the tire, which is the backward inclined extension line of the main pin p Is the tire support distance F yf Can be approximately estimated to obtain t m ,t p Usual parameters may be taken;
driver steering correction rate: the number of times the driver corrects the steering wheel in a certain time.
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