CN116578103B - Method for evaluating running risk of man-vehicle-road closed-loop system under limit working condition - Google Patents
Method for evaluating running risk of man-vehicle-road closed-loop system under limit working condition Download PDFInfo
- Publication number
- CN116578103B CN116578103B CN202310856805.5A CN202310856805A CN116578103B CN 116578103 B CN116578103 B CN 116578103B CN 202310856805 A CN202310856805 A CN 202310856805A CN 116578103 B CN116578103 B CN 116578103B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- actual
- target
- expected
- working condition
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000006073 displacement reaction Methods 0.000 claims description 106
- 238000012360 testing method Methods 0.000 claims description 20
- 230000007246 mechanism Effects 0.000 claims description 10
- 230000001133 acceleration Effects 0.000 claims description 8
- 238000005312 nonlinear dynamic Methods 0.000 claims description 7
- 230000009471 action Effects 0.000 claims description 6
- 230000008878 coupling Effects 0.000 claims description 5
- 238000010168 coupling process Methods 0.000 claims description 5
- 238000005859 coupling reaction Methods 0.000 claims description 5
- 238000005259 measurement Methods 0.000 claims 1
- 238000011156 evaluation Methods 0.000 description 15
- 230000006399 behavior Effects 0.000 description 5
- 230000003993 interaction Effects 0.000 description 5
- 230000008447 perception Effects 0.000 description 5
- 238000013461 design Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000003068 static effect Effects 0.000 description 4
- 230000006870 function Effects 0.000 description 3
- 230000002452 interceptive effect Effects 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 206010039203 Road traffic accident Diseases 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000003930 cognitive ability Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002232 neuromuscular Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 230000035484 reaction time Effects 0.000 description 1
- 238000012827 research and development Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000009295 sperm incapacitation Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a method for evaluating the running risk of a man-vehicle-road closed loop system under a limit working condition, which comprises the following steps: acquiring characteristic parameters of a target driver under the current limiting working condition; according to the characteristic parameters of the target driver, the running parameters of the target vehicle and the running parameters of the reference vehicle, the actual running track of the target vehicle and the expected running track of the target vehicle based on the man-vehicle-road closed-loop system under the limit working condition are respectively obtained; and evaluating the man-vehicle-road closed-loop system under the limit working condition according to the actual running track and the expected running track. The method can evaluate the accuracy of the man-vehicle-road closed-loop system under the limit working condition, can provide theoretical reference for evaluating the running risk of the system, and can provide basis for designing an automatic driving decision-making method under the complex environment.
Description
Technical Field
The invention relates to the technical field of intelligent automobile evaluation, in particular to a method for evaluating the running risk of a man-vehicle-road closed-loop system under a limit working condition.
Background
Traffic accidents causing major casualties mainly occur in extreme driving conditions, and under the extreme conditions, drivers have different behaviors, strong vehicle dynamics coupling and complex and changeable driving road environments, so that the running risk identification of a man-vehicle-road closed-loop system under the extreme conditions is very challenging. The system operation risk refers to the possibility of system instability or system operation deviating from an expected target and loss thereof caused by the complexity and randomness of an external environment and the limitation of the cognitive ability and the adaptability of a driver to the environment in the operation process of a microscopic road traffic system consisting of people, vehicles, roads and the like. Therefore, the research of the human-vehicle-road closed-loop system operation risk evaluation method is helpful for accelerating the research and development process of the closed-loop system operation risk early warning device, assisting traffic control departments and drivers in risk avoidance, and has important significance for blocking operation risk propagation and improving the operation safety of a microscopic traffic system.
The risk factors of the operation of the human-vehicle-road closed-loop system under the limit working conditions mainly comprise the control of a driver, such as sudden incapacitation, misoperation and the like of the driver; vehicle faults such as tire burst, collision, etc.; road environment changes such as long downhill slopes, steep curves, etc. The above elements act or cooperate to make the closed loop system easily unstable, which is mainly represented by vehicle instability and collision risk, so that the operation risk identification of the human-vehicle-road closed loop system, namely the operation risk identification of the road microscopic traffic system, is mainly the identification of the collision and instability risk of the vehicle, and how to evaluate the risk identification effect becomes a technical problem to be broken through in the test of the automatic driving and driving auxiliary system, and is also an important premise for realizing the assembly of the sleeve product before the driving auxiliary system. Through the design of a limit scene, the environment parameters of a driver, a vehicle and a driving road are adapted, the matching degree of the braking time of the vehicle under the control of a skilled driver and the braking time under the action of a closed-loop system is compared, and finally, the generalized and reproducible risk evaluation method for the operation of the closed-loop system is formed.
In the related technology, the risk of collision between multiple focused traffic participants is identified and evaluated in the human-vehicle-road closed-loop system operation risk research, and the accuracy of the system operation under the limit working condition is not evaluated. Therefore, it is difficult to provide a basis for the design of an automatic driving decision method in a complex environment.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, a first object of the present invention is to provide a method for evaluating risk of operation of a closed-loop system of a person-vehicle-road under a limited condition, which can evaluate accuracy of the closed-loop system of the person-vehicle-road under the limited condition, can provide theoretical reference for evaluating risk of operation of the system, and can provide basis for designing an automatic driving decision method under a complex environment.
In order to achieve the above objective, an embodiment of a first aspect of the present invention provides a method for evaluating risk of operation of a man-vehicle-road closed loop system under a limit condition, including: acquiring characteristic parameters of a target driver under the current limiting working condition; according to the characteristic parameters of the target driver, the running parameters of the target vehicle and the running parameters of the reference vehicle, the actual running track of the target vehicle and the expected running track of the target vehicle based on the man-vehicle-road closed-loop system under the limit working condition are respectively obtained; and evaluating the man-vehicle-road closed-loop system under the limit working condition according to the actual running track and the expected running track.
According to the method for evaluating the running risk of the human-vehicle-road closed-loop system under the limiting working condition, the characteristic parameters of the target driver under the current limiting working condition are firstly obtained, then the actual running track of the target vehicle and the expected running track of the target vehicle based on the human-vehicle-road closed-loop system under the limiting working condition are respectively obtained according to the characteristic parameters of the target driver, the running parameters of the target vehicle and the running parameters of the reference vehicle, and the human-vehicle-road closed-loop system under the limiting working condition is evaluated according to the actual running track and the expected running track. Therefore, the method can evaluate the accuracy of the man-vehicle-road closed-loop system under the limit working condition, can provide theoretical reference for system operation risk evaluation, and can provide basis for automatic driving decision method design under the complex environment.
In addition, the method for evaluating the running risk of the man-vehicle-road closed loop system under the limit working condition according to the embodiment of the invention can also have the following additional technical characteristics:
according to one embodiment of the present invention, when the current limit condition is a wet road condition, acquiring the characteristic parameter of the target driver under the current limit condition includes: acquiring the turning angle rate of a steering wheel controlled by a target driver; and determining characteristic parameters of the target driver according to the rotation angle rate.
According to one embodiment of the invention, the driving parameters include: at least one of lateral displacement, longitudinal displacement, vehicle speed, lateral acceleration, longitudinal acceleration, and yaw angle, the running parameters of the target vehicle further include: accelerator pedal opening, brake pedal opening, and steering wheel angle.
According to one embodiment of the invention, the evaluation of the man-vehicle-road closed-loop system under the limit working condition according to the actual running track and the expected running track comprises the following steps: determining an actual transverse displacement error and an actual longitudinal displacement error according to the actual running track of each target driver; determining an expected lateral displacement error and an expected longitudinal displacement error according to the expected running track of each target driver; and evaluating the man-vehicle-road closed-loop system under the limit working condition according to the actual transverse displacement error, the actual longitudinal displacement error, the expected transverse displacement error and the expected longitudinal displacement error.
According to one embodiment of the invention, the evaluation of the man-vehicle-road closed loop system under the limit working condition according to the actual transverse displacement error, the actual longitudinal displacement error, the expected transverse displacement error and the expected longitudinal displacement error comprises the following steps: acquiring a transverse error rate between an actual transverse displacement error and an expected transverse displacement error, and acquiring a longitudinal error rate between an actual longitudinal displacement error and an expected longitudinal displacement error; and evaluating the man-vehicle-road closed loop system under the limit working condition according to the transverse error rate and the longitudinal error rate.
According to one embodiment of the invention, the actual lateral displacement error and the actual longitudinal displacement error are determined by the following formulas:
wherein ,representing the actual lateral displacement error,/->Representing the actual longitudinal displacement error,/->Representing the actual lateral displacement of the i-th target driver,/->Indicating the desired lateral displacement of the ith target driver,/->Representing the total number of target drivers, +.>Represents the actual longitudinal displacement of the i-th target driver,/->Indicating the desired longitudinal displacement of the i-th target driver.
According to another embodiment of the present invention, the evaluation of the man-car-road closed loop system under the limit working condition according to the actual running track and the expected running track includes: acquiring the actual headway of the target vehicle according to the actual running track of each target driver, and acquiring the expected headway of the target vehicle according to the expected running track of each target driver; and evaluating the man-vehicle-road closed-loop system under the limit working condition according to the actual headway and the expected headway.
According to one embodiment of the invention, the evaluation of the man-vehicle-road closed-loop system under the limit working condition according to the actual headway and the expected headway comprises the following steps: acquiring an actual headway root mean square according to all actual headway, and acquiring an expected headway root mean square according to all expected headway; and evaluating the man-vehicle-road closed-loop system under the limit working condition according to the approaching degree of the actual headway root mean square and the expected headway root mean square.
According to one embodiment of the invention, the actual headway root mean square is determined by the following formula:
wherein ,representing the root mean square and the ∈r of the actual headway>Representing the headway of the i-th target driver,representing the headway of N target driversAverage, N, indicates the total number of target drivers.
According to one embodiment of the invention, the human-vehicle-road closed-loop system under the limit working condition is established according to the combined action of a preset dynamic-static road model based on the driving environment of virtual mechanics, a vehicle high-dimensional nonlinear dynamics model based on Newton mechanics, an integrated perception-decision-control driver model and an interactive coupling mechanism between the model.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a flow chart of a method for evaluating risk of operation of a man-vehicle-road closed loop system under extreme conditions according to an embodiment of the invention;
FIG. 2 is a schematic diagram of a man-vehicle-road closed loop system under extreme conditions according to one embodiment of the invention;
FIG. 3 is a schematic illustration of a limit condition being a following condition in accordance with one embodiment of the present invention;
FIG. 4 is a flow chart of evaluating a person-vehicle-road closed loop system under extreme conditions according to an actual running track and a desired running track in a following vehicle condition according to one embodiment of the present invention;
FIG. 5 is a schematic illustration of a cut-in condition at a limit condition according to one embodiment of the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The method for evaluating the running risk of the man-vehicle-road closed-loop system under the limit working condition provided by the embodiment of the invention is described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a method for evaluating risk of operation of a man-vehicle-road closed loop system under a limit condition according to an embodiment of the present invention.
According to one embodiment of the invention, the human-vehicle-road closed-loop system under the limit working condition is established according to the combined action of a preset dynamic-static road model based on the driving environment of virtual mechanics, a vehicle high-dimensional nonlinear dynamics model based on Newton mechanics, an integrated perception-decision-control driver model and an interactive coupling mechanism between the model.
Specifically, as shown in fig. 2, the dynamic-static road model of the driving environment based on virtual mechanics can be obtained by the following method: modeling a virtual force formed by dynamic and static information in a running environment, grasping an interaction mechanism of a driver and the running environment, and establishing the running environment dynamic and static road model based on the virtual mechanics based on the interaction mechanism of the driver and the running environment. Further, the dynamic and static road model of the driving environment based on virtual mechanics comprises modeling of traffic elements, wherein the traffic elements at least comprise road boundaries, traffic signals and traffic participants.
The vehicle high-dimensional nonlinear dynamics model based on Newton mechanics can be obtained by the following steps: and modeling tires and vehicle dynamics based on a randomness theory, grasping an interaction mechanism of a vehicle and a road environment, and establishing the vehicle high-dimensional nonlinear dynamics model based on Newton mechanics based on the interaction mechanism of the vehicle and the road environment. In particular, the vehicle high-dimensional nonlinear dynamics model based on newton mechanics includes a tire model and a vehicle model.
The driver behavior model integrating perception-decision-control can be obtained by the following modes: modeling a perception range of the vision of a driver based on a psychological field, describing a decision model based on a minimum action amount, describing a control model based on neuromuscular dynamics, grasping mechanisms and rules followed by the perception, the decision and the control of the driver, and establishing a driver behavior model integrating the perception, the decision and the control based on the mechanisms and the rules of the perception, the decision and the control.
According to the combined action of a preset dynamic and static road model of the driving environment based on virtual mechanics, a vehicle high-dimensional nonlinear dynamics model based on Newton mechanics, a driver behavior model integrating perception, decision and control and an interactive coupling mechanism between the model, a man-vehicle-road closed-loop system under the limit working condition can be established. The running track of the vehicle can be obtained through simulation by a man-vehicle-road closed-loop system under the limit working condition.
After the man-vehicle-road closed-loop system is established under the limit working condition, the operation risk of the man-vehicle-road closed-loop system under the limit working condition, namely the accuracy of the output running track of the vehicle, is required to be evaluated, and the man-vehicle-road closed-loop system under the limit working condition can be put into use under the condition that the accuracy reaches the expected.
As shown in fig. 1, the method for evaluating the running risk of the man-vehicle-road closed loop system under the limit working condition in the embodiment of the invention can comprise the following steps:
s101, obtaining characteristic parameters of a target driver under the current limiting working condition.
According to one embodiment of the present invention, when the current limit condition is a wet road condition, acquiring the characteristic parameter of the target driver under the current limit condition includes: acquiring the turning angle rate of a steering wheel controlled by a target driver; and determining characteristic parameters of the target driver according to the rotation angle rate.
Specifically, when the current limit working condition is a wet road surface condition, firstly building a wet road surface scene in a test road, configuring corresponding road surface adhesion coefficients and road textures, and recruiting N target drivers to drive vehicles for evaluation. Different target drivers have different driving styles, such as a conservative driving style, an aggressive driving style and the like, and the drivers with the aggressive driving style usually have driving behaviors such as frequent lane changing, rapid acceleration and deceleration and the like, and the drivers with the conservative driving style cannot. The sensor on the target vehicle can acquire the steering wheel angle, and the steering wheel angle speed of the steering wheel can be obtained after the steering wheel angle is derived. The characteristic parameters of the target driver can be determined through the steering angle rate of the steering wheel of the target driver, so that the driving style of the target driver can be determined, for example, the steering angle rate of the steering wheel of the target driver in the aggressive driving style is larger, and the steering angle rate of the steering wheel of the driver in the conservative driving style is smaller.
S102, acquiring the actual running track of the target vehicle and the expected running track of the target vehicle based on the man-vehicle-road closed-loop system under the limit working condition according to the characteristic parameters of the target driver, the running parameters of the target vehicle and the running parameters of the reference vehicle.
According to one embodiment of the invention, the driving parameters include: at least one of lateral displacement, longitudinal displacement, vehicle speed, lateral acceleration, longitudinal acceleration, and yaw angle, the running parameters of the target vehicle further include: accelerator pedal opening, brake pedal opening, and steering wheel angle.
Specifically, in the evaluation process, the reference vehicle is automatically controlled to run on the test road according to the test requirement, and the target driver drives the vehicle according to the test requirement. The on-vehicle computing unit of the target vehicle may acquire the running parameter of the target vehicle and the actual running track of the target vehicle, and the on-vehicle computing unit of the reference vehicle may acquire the running parameter of the reference vehicle. And inputting the characteristic parameters of the target driver into a human-vehicle-road closed-loop system under the limiting working condition, and combining the running parameters and road information of the target vehicle, wherein the expected running track of the target vehicle is output by the human-vehicle-road closed-loop system under the limiting working condition.
And S103, evaluating the man-vehicle-road closed-loop system under the limit working condition according to the actual running track and the expected running track.
Specifically, after the actual running track and the expected running track of the target vehicle are obtained in the step S102, the actual running track and the expected running track can be compared by selecting appropriate methods according to different limit working conditions, so that the accuracy of the man-vehicle-road closed-loop system under the limit working conditions is obtained, and the evaluation is completed.
The method for evaluating the human-vehicle-road closed-loop system under the limit working condition according to the actual running track and the expected running track under different working conditions is described below by combining with the specific embodiments.
First, the following operation is described with the limit operation. Specifically, as shown in fig. 3, the dynamic model test of the closed loop system of the man-vehicle road is carried out according to the following steps: 1) Only the test vehicle is reserved on the test road, so that the safety of the road test is ensured; 2) The target driver drives the vehicle 1 (target vehicle) to follow the vehicle 2 (reference vehicle) according to the driving habit of the target driver, and drives the vehicle to and fro on the test road; 3) Automatically controlling the driving of the vehicle 2 in the test, wherein the vehicle speed v2=50+20sin (2×pi×t/T2) of the vehicle 2; 4) The vehicle 2 runs back and forth on the test road and is symmetrical in back and forth; 5) Round trip data is collected for each target driver.
According to one embodiment of the present invention, as shown in fig. 4, the evaluation of the man-vehicle-road closed loop system under the limit working condition according to the actual running track and the expected running track includes the following steps:
s201, determining an actual transverse displacement error and an actual longitudinal displacement error according to the actual running track of each target driver.
According to one embodiment of the invention, the actual lateral displacement error and the actual longitudinal displacement error are determined by the following formulas:
wherein ,representing the actual lateral displacement error,/->Representing the actual longitudinal displacement error,/->Representing the actual lateral displacement of the i-th target driver,/->Indicating the desired lateral displacement of the ith target driver,/->Representing the total number of target drivers, +.>Represents the actual longitudinal displacement of the i-th target driver,/->Indicating the desired longitudinal displacement of the i-th target driver.
Specifically, according to the actual travel track of each target driver, the actual lateral displacement and the actual longitudinal displacement of each target driver, i.e. the actual lateral displacement of the ith target driver, can be obtainedAnd the i-th target driver's actual longitudinal displacement +.>In order to reduce errors of the target drivers in the driving process, an average value of actual lateral displacement and actual longitudinal displacement can be obtained after each target driver drives for a plurality of times in the testing process. While in the case of a fixed road condition, the desired trajectory of each target driver output by the man-vehicle-road closed-loop system under the limit conditions is known, so that the desired lateral displacement and the desired longitudinal displacement of each target driver are also known, i.e. the i-th target driver's desired lateral displacement->And desired longitudinal displacement +.>Are known. Actual lateral displacement of the ith target driverActual longitudinal displacement of the ith target driver +.>Desired lateral displacement of the ith target driver +.>And i desired longitudinal displacement of the target driver +.>Substituting the above formula to obtain the actual lateral displacement error +.>And the actual longitudinal displacement error>。
S202, determining an expected lateral displacement error and an expected longitudinal displacement error according to the expected running track of each target driver.
According to one embodiment of the invention, the desired lateral displacement error and the desired longitudinal displacement error are determined by the following formulas:
wherein ,indicating the expected lateral displacement error +.>Indicating the expected longitudinal displacement error +.>Represents the simulated lateral displacement of the ith target driver,/->Representing the simulated longitudinal displacement of the i-th target driver.
Specifically, during the test, the reference vehicle is obtainedThe vehicle 2) takes the measured parameters into a minimum-acting-amount driver model, the planned speed acts on the return test of the target vehicle, each target driver drives the target vehicle to return, so that a simulated running track corresponding to each target driver is obtained, and the simulated transverse displacement and the simulated longitudinal displacement of each target driver are obtained according to the simulated running track corresponding to each target driver. Simulated lateral displacement of the ith target driverSimulated longitudinal displacement of the ith target driver +.>Desired lateral displacement of the ith target driver +.>And i desired longitudinal displacement of the target driver +.>Substituting the above formula to obtain the expected lateral displacement errorAnd the desired longitudinal displacement error +.>。
S203, evaluating the man-vehicle-road closed loop system under the limit working condition according to the actual transverse displacement error, the actual longitudinal displacement error, the expected transverse displacement error and the expected longitudinal displacement error.
According to one embodiment of the invention, the evaluation of the man-vehicle-road closed loop system under the limit working condition according to the actual transverse displacement error, the actual longitudinal displacement error, the expected transverse displacement error and the expected longitudinal displacement error comprises the following steps: acquiring a transverse error rate between an actual transverse displacement error and an expected transverse displacement error, and acquiring a longitudinal error rate between an actual longitudinal displacement error and an expected longitudinal displacement error; and evaluating the man-vehicle-road closed loop system under the limit working condition according to the transverse error rate and the longitudinal error rate.
According to one embodiment of the invention, the lateral error rate between the actual lateral displacement error and the desired lateral displacement error is determined by the following formula:
the longitudinal error rate between the actual longitudinal displacement error and the expected longitudinal displacement error is determined by the following formula:
wherein ,represents the transverse error rate,/->Representing the longitudinal error rate.
Specifically, the actual lateral displacement error obtained in steps S201 and S202 is determinedError in actual longitudinal displacement->Desired lateral displacement error->And the desired longitudinal displacement error +.>By substituting the above formulas, the transverse error rate +.>And longitudinal error rate->. Based on the lateral error rate and the longitudinal error rateThe size can evaluate the man-vehicle-road closed-loop system under the limit working condition. The smaller the transverse error rate and the longitudinal error rate, the higher the accuracy of the human-vehicle-road closed-loop system under the limit working condition is, and the larger the transverse error rate and the longitudinal error rate are, the lower the accuracy of the human-vehicle-road closed-loop system under the limit working condition is.
The following description will be made with the limit operation mode as the cut-in operation mode. Specifically, the cut-in working condition is shown in fig. 5, and the main flow of the human-vehicle-road closed loop system verification comparison test under the limit working condition is as follows:
1) Only the test vehicle is reserved on the test road, so that the safety of the road test is ensured; 2) The target driver drives the vehicle 1 (target vehicle) to follow the vehicle 2 (reference vehicle) according to the driving habit thereof, and automatically controls the running of the vehicle 2; 3) The longitudinal position of the vehicle 3 is maintained between the vehicle 1 and the vehicle 2 or slightly behind the vehicle 1; 4) When the vehicle 3 driver considers the vehicle 1 and the vehicle 2 to be properly spaced, the following steering yaw motion is performed: making a cutting trend, but not really changing lanes, and driving back to the original lane to keep the center line of the lane to go straight after a few seconds; cutting in: the control car 3 cuts into between 1 and 2, keeps straight after cutting, cuts into the right lane again after keeping for 5-7 seconds. 5) And collecting information such as yaw angle, position, time, speed, acceleration and the like of the three vehicles.
According to another embodiment of the present invention, the evaluation of the man-car-road closed loop system under the limit working condition according to the actual running track and the expected running track includes: acquiring the actual headway of the target vehicle according to the actual running track of each target driver, and acquiring the expected headway of the target vehicle according to the expected running track of each target driver; and evaluating the man-vehicle-road closed-loop system under the limit working condition according to the actual headway and the expected headway.
Specifically, the headway refers to a time interval in which two consecutive vehicle headway ends pass through a certain section in a train of vehicles traveling on the same lane. And, the headway represents the maximum reaction time that the rear driver has when the vehicle is braked currently, so it does not fluctuate with the change in speed. After each target driver finishes driving the target vehicle, the actual headway of the target vehicle can be obtained according to the actual running track of each target driver. And (3) bringing the characteristic parameters of each target driver into a person-vehicle-road closed-loop system under the limiting working condition to obtain the expected running track of each target driver, and then acquiring the expected headway of the target vehicle according to the expected running track of each target driver.
Further, according to an embodiment of the present invention, the evaluation of the person-vehicle-road closed-loop system under the limit working condition according to the actual headway and the expected headway comprises: acquiring an actual headway root mean square according to all actual headway, and acquiring an expected headway root mean square according to all expected headway; and evaluating the man-vehicle-road closed-loop system under the limit working condition according to the approaching degree of the actual headway root mean square and the expected headway root mean square.
According to one embodiment of the invention, the actual headway root mean square is determined by the following formula:
wherein ,representing the root mean square and the ∈r of the actual headway>Representing the headway of the i-th target driver,the average value of the headway of N target drivers is represented, and N represents the total number of target drivers.
Specifically, after the headway of each target driver is obtained, the average of headway of N target drivers can be obtainedThen the (i) th target driver's headway +.>And average value of the headway of N target drivers +.>Substituting the formula to obtain the root mean square +.>。
According to the same principle, after the expected headway of each target driver is obtained, the average value of the expected headway of N target drivers can be obtained, and then the average value of the expected headway of the ith target driver and the expected headway of N target drivers is substituted into the following formula, so that the inter-headway root mean square can be obtained:
wherein ,representing the root mean square of the expected headway->Indicating the desired headway of the ith target driver,/->The average of the expected headway of N target drivers is represented, N representing the total number of target drivers.
Root mean square of actual time interval of vehicle headAnd the expected headway root mean square +.>Substitution into the following formula, the approach degree +.>：
And evaluating the man-vehicle-road closed-loop system under the limit working condition according to the magnitude of the proximity value. The smaller the proximity, the higher the accuracy of the man-car-road closed-loop system under the limiting working condition, the larger the proximity, and the lower the accuracy of the man-car-road closed-loop system under the limiting working condition.
In summary, according to the method for evaluating the running risk of the human-vehicle-road closed-loop system under the limit working condition in the embodiment of the invention, firstly, the characteristic parameters of the target driver under the current limit working condition are obtained, then, the actual running track of the target vehicle and the expected running track of the target vehicle based on the human-vehicle-road closed-loop system under the limit working condition are respectively obtained according to the characteristic parameters of the target driver, the running parameters of the target vehicle and the running parameters of the reference vehicle, and the human-vehicle-road closed-loop system under the limit working condition is evaluated according to the actual running track and the expected running track. Therefore, the method can evaluate the accuracy of the man-vehicle-road closed-loop system under the limit working condition, can provide theoretical reference for system operation risk evaluation, and can provide basis for automatic driving decision method design under the complex environment.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered as a ordered listing of executable instructions for implementing logical functions, and may 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, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), 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 may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly, through intermediaries, or both, may be in communication with each other or in interaction with each other, unless expressly defined otherwise. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (6)
1. A method for evaluating the running risk of a man-vehicle-road closed-loop system under a limit working condition is characterized in that,
acquiring characteristic parameters of a target driver under the current limiting working condition;
acquiring an actual running track of the target vehicle and an expected running track of the target vehicle based on a person-vehicle-road closed-loop system under the limit working condition according to the characteristic parameters of the target driver, the running parameters of the target vehicle and the running parameters of the reference vehicle;
evaluating the man-vehicle-road closed-loop system under the limit working condition according to the actual running track and the expected running track, wherein evaluating the man-vehicle-road closed-loop system under the limit working condition according to the actual running track and the expected running track comprises the following steps:
determining an actual transverse displacement error and an actual longitudinal displacement error according to the actual running track and the expected running track of each target driver;
determining an expected transverse displacement error and an expected longitudinal displacement error according to the expected running track and the simulated running track of each target driver, wherein the obtained measurement parameters of the reference vehicle are brought into a minimum-acting-amount driver model, the planned speed is acted in a target vehicle return test, and each target driver drives the target vehicle to return so as to obtain the simulated running track corresponding to each target driver;
acquiring a transverse error rate between the actual transverse displacement error and the expected transverse displacement error, and acquiring a longitudinal error rate between the actual longitudinal displacement error and the expected longitudinal displacement error;
evaluating a man-vehicle-road closed-loop system under the limit working condition according to the transverse error rate and the longitudinal error rate; or alternatively
Evaluating the man-vehicle-road closed-loop system under the limit working condition according to the actual running track and the expected running track, comprising the following steps:
acquiring an actual headway of the target vehicle according to the actual running track of each target driver, and acquiring an expected headway of the target vehicle according to the expected running track of each target driver;
acquiring an actual headway root mean square according to all the actual headway, and acquiring an expected headway root mean square according to all the expected headway;
and evaluating the man-vehicle-road closed-loop system under the limit working condition according to the approaching degree of the actual headway root mean square and the expected headway root mean square.
2. The method of claim 1, wherein obtaining the characteristic parameter of the target driver under the current limit condition when the current limit condition is a wet road condition comprises:
acquiring the rotation angle rate of the steering wheel controlled by the target driver;
and determining characteristic parameters of the target driver according to the rotation angle rate.
3. The method of claim 1, wherein the travel parameters include: at least one of lateral displacement, longitudinal displacement, vehicle speed, lateral acceleration, longitudinal acceleration, and yaw angle, the running parameters of the target vehicle further include: accelerator pedal opening, brake pedal opening, and steering wheel angle.
4. The method of claim 1, wherein the actual lateral displacement error and the actual longitudinal displacement error are determined by the following formulas:
wherein ,representing said actual lateral displacement error,/>Representing said actual longitudinal displacement error,/and>representing the actual lateral displacement of the i-th said target driver,/and>indicating the desired lateral displacement of the i-th said target driver,representing the total number of said target drivers, < >>Representing the actual longitudinal displacement of the i-th said target driver,/or->Representing the expected longitudinal displacement of the i-th said target driver.
5. The method of claim 1, wherein the actual headway root mean square is determined by the formula:
wherein ,representing the root mean square of the actual headway +.>Represents the headway of the ith target driver,/->The average value of the headway of N target drivers is represented, and N represents the total number of target drivers.
6. The method according to claim 1, wherein the man-vehicle-road closed-loop system under the limit working condition is established according to the combined action of a preset dynamic-static road model based on virtual mechanics in the driving environment, a vehicle high-dimensional nonlinear dynamics model based on newton mechanics, and an integrated perception-decision-control driver behavior model and a mutual coupling mechanism between the model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310856805.5A CN116578103B (en) | 2023-07-13 | 2023-07-13 | Method for evaluating running risk of man-vehicle-road closed-loop system under limit working condition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310856805.5A CN116578103B (en) | 2023-07-13 | 2023-07-13 | Method for evaluating running risk of man-vehicle-road closed-loop system under limit working condition |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116578103A CN116578103A (en) | 2023-08-11 |
| CN116578103B true CN116578103B (en) | 2023-10-03 |
Family
ID=87541736
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310856805.5A Active CN116578103B (en) | 2023-07-13 | 2023-07-13 | Method for evaluating running risk of man-vehicle-road closed-loop system under limit working condition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116578103B (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108573357A (en) * | 2018-05-08 | 2018-09-25 | 清华大学 | Driving risk real time evaluating method based on equivalent force and its device |
| CN111731268A (en) * | 2020-06-01 | 2020-10-02 | 南京航空航天大学 | Vehicle rollover prevention control method considering different driver characteristics |
| CN113946943A (en) * | 2021-09-26 | 2022-01-18 | 清华大学 | Human-vehicle-road micro traffic system modeling and risk identification method and device |
| CN114117829A (en) * | 2022-01-24 | 2022-03-01 | 清华大学 | Dynamic modeling method and system for man-vehicle-road closed loop system under limit working condition |
| CN114323698A (en) * | 2022-02-11 | 2022-04-12 | 吉林大学 | Real vehicle experiment platform testing method for man-machine driving-together intelligent vehicle |
| CN115344939A (en) * | 2022-06-23 | 2022-11-15 | 重庆理工大学 | Automatic driving virtual test system based on human, vehicle and environment modeling |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11608059B2 (en) * | 2019-08-26 | 2023-03-21 | GM Global Technology Operations LLC | Method and apparatus for method for real time lateral control and steering actuation assessment |
-
2023
- 2023-07-13 CN CN202310856805.5A patent/CN116578103B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108573357A (en) * | 2018-05-08 | 2018-09-25 | 清华大学 | Driving risk real time evaluating method based on equivalent force and its device |
| CN111731268A (en) * | 2020-06-01 | 2020-10-02 | 南京航空航天大学 | Vehicle rollover prevention control method considering different driver characteristics |
| CN113946943A (en) * | 2021-09-26 | 2022-01-18 | 清华大学 | Human-vehicle-road micro traffic system modeling and risk identification method and device |
| CN114117829A (en) * | 2022-01-24 | 2022-03-01 | 清华大学 | Dynamic modeling method and system for man-vehicle-road closed loop system under limit working condition |
| CN114323698A (en) * | 2022-02-11 | 2022-04-12 | 吉林大学 | Real vehicle experiment platform testing method for man-machine driving-together intelligent vehicle |
| CN115344939A (en) * | 2022-06-23 | 2022-11-15 | 重庆理工大学 | Automatic driving virtual test system based on human, vehicle and environment modeling |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116578103A (en) | 2023-08-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109808685B (en) | Automobile early warning automatic collision avoidance control method based on danger assessment | |
| DE102017117698B4 (en) | Driver assistance device for a vehicle | |
| CN105966396B (en) | A kind of vehicle intelligent collision control method based on driver's collision avoidance behavior | |
| CN103158705B (en) | Method and system for controlling a host vehicle | |
| US8258934B2 (en) | Vehicle and method of advising a driver therein | |
| US6873911B2 (en) | Method and system for vehicle operator assistance improvement | |
| CN107415945A (en) | For assessing the automotive drive system and its application method of track lane-change | |
| CN105501220A (en) | Vehicle collision warning method and device and vehicle | |
| CN113253739B (en) | Driving behavior decision method for expressway | |
| CN104768821A (en) | Drive assist device and method, collision prediction device and method, and alerting device and method | |
| CN108394411A (en) | Vehicle follows start control device | |
| CN104334431A (en) | Driving characteristics estimation device and driver assistance system | |
| CN103661399A (en) | Method for determining an evasion trajectory for a motor vehicle, and safety device or safety system | |
| CN102139696A (en) | Grid unlock | |
| DE112010005774T5 (en) | Systems and methods for scheduling driver interface tasks based on driver workload | |
| CN113946943B (en) | Human-vehicle-road micro traffic system modeling and risk identification method and device | |
| DE102018131646A1 (en) | DRIVER WARNING SYSTEMS AND METHOD | |
| CN104417558A (en) | Deceleration setting system, deceleration setting method, and deceleration setting program | |
| CN104583038A (en) | Vehicle control apparatus | |
| CN110239558A (en) | A kind of driving style hierarchical fuzzy identifying system based on migration index | |
| CN111595597B (en) | Method for testing AEB VRU performance in complex environment | |
| DE102018107341A1 (en) | LANE CHANGE GUIDE | |
| Lee et al. | Development of a driving behavior-based collision warning system using a neural network | |
| Lu et al. | From vehicle stability control to intelligent personal minder: Real-time vehicle handling limit warning and driver style characterization | |
| CN116552552A (en) | Intelligent driving behavior decision method and system for vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |