CN114492037A - Method for testing danger perception capability of driver - Google Patents
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Abstract
The invention is suitable for the technical field of vehicle and road safety, and provides a method for testing the danger perception capability of a driver, which comprises the following steps: collecting a road driving risk scene; classifying risk scenes and building a scene library; determining a driving response baseline; collecting dangerous response data of a driver through a hardware-in-the-loop driving simulation test system; and evaluating the danger perception capability of the driver. The method for assessing the driving safety of the driver tests the danger perception capability of the driver on the cognitive, operation and reaction level in a simpler and more convenient way, is a method for assessing the driving safety of the driver which can be widely popularized, and has the further benefit of improving the danger perception capability and safety awareness of the driver so as to reduce traffic accidents.
Description
Technical Field
The invention relates to the technical field of vehicle and road safety, in particular to a method for testing the danger perception capability of a driver.
Background
The danger perception means that the state development of objective entities under a scene is predicted, potential dangers possibly existing are analyzed, the method is a behavior basis for preventing or reducing danger loss, and dangerous accidents caused by human factors are almost directly related to the failure of the danger perception. The Risk perception is related to subjective situational awareness (situational awareness), Risk perception (Risk permission), Risk decision (Hazard decision), objective scene complexity, potential Risk identification degree, external signal interference and other factors. People are the main related objects of road traffic, the proportion of traffic accidents caused by human factors exceeds 90%, and most of the accidents caused by the failure of dangerous perception of drivers occupy the accidents. Driver danger perception is therefore a subject of long-term research in the field of traffic safety. Since road danger awareness plays an extremely important role in traffic accidents and is a main behavior of a driver to prevent and cope with dangerous accidents, the ability of the driver to perform danger awareness is considered by researchers at home and abroad as the driving ability most related to the safety of the driver.
At present, scholars at home and abroad make many efforts on how to quantify the driver danger perception capability to evaluate the driving safety of the driver. The test method adopted by the existing achievements needs to be realized by a more complex technical means, and is difficult to popularize. Therefore, it is desirable to provide a method for testing the driver's danger perception capability, which aims to solve the above problems.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method for testing the danger perception capability of a driver so as to solve the problems in the background technology.
The invention is realized in such a way that a method for testing the danger perception capability of a driver comprises the following steps:
collecting a road driving risk scene;
classifying risk scenes and building a scene library;
determining a driving response baseline;
collecting dangerous response data of a driver through a hardware-in-the-loop driving simulation test system;
and evaluating the danger perception capability of the driver.
As a further scheme of the invention: the risk scene comprises 5 types of straight roads, T-shaped intersections, special intersections, crossroads and turning roads, and the scene library is a risk scene video library which is built based on a computer technology and hardware in-loop driving simulation system and is required for the risk perception capability test.
As a further scheme of the invention: the step of determining the driving reaction baseline specifically needs to determine a risk avoiding mode, an optimal reaction time period, a suboptimal reaction time period, an optimal turning angle range of a steering wheel, a suboptimal turning angle range of the steering wheel, an optimal vehicle braking deceleration range and a suboptimal range which need to be adopted based on the type of the risk scene.
As a further scheme of the invention: the step of collecting dangerous reaction data of the driver through the hardware-in-the-loop driving simulation test system specifically comprises the following steps of:
when a driver is placed in the hardware-in-the-loop driving simulation test system, a risk scene display screen plays a driving risk scene, wherein the driving risk scene comprises normal driving and dangerous event precursor occurrence;
when a dangerous event is about to occur, the display screen stops playing, after the video playing is finished, risk scene evaluation is conducted on the driver, and scene evaluation data, driver reaction data and driver operation data of the driver are recorded.
As a further scheme of the invention: the driver danger perception capability testing device is used for collecting driver danger response data and evaluating the driver danger perception capability and comprises a risk scene presenting module, a driver operation collecting module, a driver subjective evaluation collecting module and an experiment control host.
As a further scheme of the invention: the driver danger perception capability comprises a driver scene risk perception capability P, a driver reaction capability S and a driver danger avoiding operation capability C.
As the invention proceedsThe one-step scheme comprises the following steps: the driver scene risk perception capability P takes the reaction score of the driver for the risk scene as a reference, and the subjective evaluation score X of the driver for the risk sceneiThe calculation rule of (1) is as follows:
rule 1.1, if the driver correctly determines whether substantial risk exists in the scene, correctly discriminates the scene potential risk source and accurately determines the urgency of the scene risk, then Xi＝1；
Rule 1.2, if the driver correctly judges whether substantial risk exists in the scene, the potential risk source of the scene is correctly discriminated, but the scene risk urgency degree is incorrectly judged, Xi＝0.8；
Rule 1.3, if the driver correctly determines whether there is substantial risk in the scene, but does not correctly determine the potential risk source of the scene, then Xi＝0.6；
Rule 1.4, if there is a substantial risk in the scene of driver error determination, then Xi＝0。
As a further scheme of the invention: the driver reaction ability S is a reaction behavior benchmark of a driver for a risk scene, and a driver reaction ability score YiThe calculation rule of (1) is as follows:
rule 2.1, if the driver is facing a risk scenario in the optimal reaction time interval [ ts1，ts2]By taking danger-avoiding operation, Yi＝1；
Rule 2.2, if the driver is in the suboptimal reaction time interval (t) when facing a risk scenarios2，ts3]Taking the risk avoiding operation in the system, wherein the time point of taking the risk avoiding operation is tiThen Y isiThe calculation method of (2) is as follows:
rule 2.3, if the driver is facing the risk scene, the time point of taking the risk avoidance operation is tiAnd t isiThe following conditions are satisfied: 0 < ti-ts3≤(ts3-ts1) 30% or 0 < ts1-ti≤(ts3-ts1) 30% of, then
Rule 2.4, if the driver is facing the risk scene, the time point t of the risk avoiding operation is adoptediSatisfies the condition ti-ts3＞(ts3-ts1) 30% or ts1-ti＞(ts3-ts1) 30% of, then Yi＝0。
As a further scheme of the invention: the risk avoiding operation capability C of the driver takes the specific risk avoiding operation made by the driver aiming at the risk scene as a reference, and the risk avoiding operation score Z of the driveriThe calculation rule is as follows:
rule 3.1, if the driver is facing the risk scene that the steering wheel needs to be rotated or the brake pedal is stepped on, the angle of the rotating steering wheel is in the optimal interval [ q ]u1，qu2]Or the vehicle braking deceleration is in the optimum interval qf2，qf3]Then Z isi＝1；
Rule 3.2, if the driver is facing the risk scene that the steering wheel needs to be rotated or the brake pedal is stepped on, the angle of the rotating steering wheel is in a suboptimal interval (q)u2，qu3]Or the vehicle braking deceleration is in a suboptimal interval qf1，qf2)U(qf3，qf4]Then, then
Rule 3.3, if the driver faces the risk scene that the steering wheel needs to be rotated or the brake pedal is stepped on, the angle of the rotating steering wheel is in the minimum limit value interval 0 < qui-qu3≤(qu3-qu1) 30% or the vehicle braking deceleration is in the minimum interval 0 < qf1-qfi≤(qf3-qf1) 30% or 0 < qfi-qf4≤(qf4-qf2) 30% of, then
Rule 3.4, if the driver's steering wheel angle or vehicle brake deceleration exceeds the interval value specified by rules 3.1, 3.2, and 3.3, then Z is writteni＝0；
In the above rule, quiOutputting a value, q, for the driver's steering wheel anglefiA value is output for the driver to operate the vehicle brake deceleration.
As a further scheme of the invention: the method for evaluating the driver danger perception capability adopts a fuzzy comprehensive evaluation method, and specifically comprises the following steps:
determining an evaluation set of the driver danger perception capability index;
determining weight values of risk perception capability P, driver reaction capability S and driver risk avoidance operation capability C for each type of risk scene according to an entropy weight method;
determining the weight value of 5 types of risk scenes according to an analytic hierarchy process, respectively calculating judgment matrixes of the 5 types of risk scenes and the P, S and C capacities of drivers, then solving a fuzzy relation matrix corresponding to the 5 types of risk scenes according to a membership function, multiplying and normalizing the solved fuzzy relation matrix and the weight of the corresponding index to obtain a fuzzy comprehensive evaluation matrix;
and (4) obtaining the danger perception capability score of the driver according to the corresponding score of each evaluation set, and determining the evaluation grade of the danger perception capability.
Compared with the prior art, the invention has the beneficial effects that:
the method for assessing the driving safety of the driver tests the danger perception capability of the driver on the cognitive, operation and reaction level in a simpler and more convenient way, is a method for assessing the driving safety of the driver which can be widely popularized, and has the further benefit of improving the danger perception capability and safety awareness of the driver so as to reduce traffic accidents.
Drawings
FIG. 1 is a flow chart of a method for testing the driver's danger awareness ability.
Fig. 2 is a schematic diagram of a driver danger perception capability testing device.
FIG. 3 is a schematic diagram of a risk scenario.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Specific implementations of the present invention are described in detail below with reference to specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides a method for testing a driver's danger perception capability, where the method includes the following steps:
s100, collecting a road driving risk scene;
s200, risk scene classification and scene library construction are carried out;
s300, determining a driving reaction baseline;
s400, collecting dangerous response data of a driver through a hardware-in-the-loop driving simulation test system;
and S500, evaluating the danger perception capability of the driver.
In the embodiment of the invention, in the process of acquiring the road driving risk scene, a driver with skilled (driving mileage is more than 20 kilometers or driving age over ten years or professional driver) driving experience is required to drive the scene acquisition vehicle. In the scene acquisition vehicle, a vehicle driving scene acquisition system is required to be configured, scene information in visual angles of a driver, such as a front windshield, two side windows, an interior rearview mirror and two side rearview mirrors, is recorded through a scene recorder, and scene sound is recorded at the same time.
As shown in fig. 2, the scene recorder carried by the driving scene collecting vehicle includes a front windshield scene recorder (c), a right side window scene recorder (c), a left side window scene recorder (c), an inside rearview mirror scene recorder (c), a right side rearview mirror scene recorder (c), and a left side rearview mirror scene recorder (c). According to the collected scenes, the risk scenes are divided into 5 types of straight roads, T-shaped intersections, special intersections, crossroads and turning roads, and after the time length and the display process of the videos are standardized, the videos are guided into an experiment control host.
The straight road type risk scene is a traffic scene with danger on a straight road: the method comprises the following steps that dangerous traffic scenes such as sudden deceleration of a front vehicle, passing close to the front vehicle, continuous lane change, starting of a roadside vehicle, merging of other vehicles into a main road and the like appear on a straight road; the T-junction risk scene is a traffic scene with danger in the T-junction: the traffic scene with danger such as vehicles stopping at the T-shaped intersection, hidden vehicles driving out at the T-shaped intersection and the like is included; the special intersection risk scene is a traffic scene with danger at a special intersection: the method mainly refers to that a Y-shaped intersection or a rare intersection is calculated as a special intersection, and dangerous traffic scenes such as that vehicles continuously change lanes to drive to the Y-shaped intersection, vehicles on the right side change lanes to drive to the Y-shaped intersection and the like appear when passing through the special intersection; the crossroad risk scene is a traffic scene with danger at the crossroad: the traffic scene comprises traffic scenes that pedestrians cross a road when passing through the crossroad, a vision blind area exists at the crossroad, vehicles turn left when passing through the crossroad and the like, and the traffic scene is dangerous. The turn road risk scene is a traffic scene with danger on a turn road: the method comprises traffic scenes with dangers, such as oncoming vehicles on the sharp-turning road, visual field blind areas on the sharp-turning road, other vehicles entering the sharp-turning road during turning, and the like.
The scene library is a risk scene video library which is set up based on a computer technology and a hardware-in-the-loop driving simulation system and is required for the risk perception capability test; the step of determining the driving reaction baseline specifically needs to determine a risk avoiding mode, an optimal reaction time period, a suboptimal reaction time period, an optimal turning angle interval of a steering wheel, a suboptimal turning angle interval of the steering wheel, an optimal interval of vehicle braking deceleration and a suboptimal interval which need to be adopted based on the type of the risk scene, and provides a basis for subsequent driver risk perception capability tests.
The step of collecting dangerous reaction data of the driver through the hardware-in-the-loop driving simulation test system specifically comprises the following steps of: when a driver is placed in the hardware-in-the-loop driving simulation test system, a risk scene display screen plays a driving risk scene, wherein the driving risk scene comprises normal driving and dangerous event precursor occurrence; when a dangerous event is about to occur, the display screen stops playing, a driver is required to take proper measures through a driving simulator when watching so as to avoid accidents, after the video playing is finished, the driver is required to perform risk scene evaluation, scene evaluation data of the driver and driver reaction data and driver operation data during execution are recorded, and the score condition of the driver in the scene is calculated according to the three data.
The risk scene evaluation mainly refers to subjective evaluation of a driver on a risk scene, and comprises the contents of judging whether the driver has substantial risk on the driving scene, identifying a scene potential risk source, judging the urgency degree of risk in the driving scene and the like;
the substantial risk is defined as a dangerous accident that must occur if the driver takes no reactive measures. Insubstantial risk is defined as no substantial risk in the scene, no reaction measures may be taken by the driver, and an accident will not generally occur, but a source of risk still exists in the scene, which may cause a risk in the future. The substantial risk is defined as the inevitable occurrence of an accident if the driver takes no reactive measures. The elements in this risk need to satisfy the following conditions:
condition one, from the final result, the accident causes certain economic loss or casualties;
in terms of indirect inducement, the accident is caused by improper operation or failure of the driver;
the condition three is that the vehicle performance is good and no operation obstacle exists in terms of objective conditions;
and fourthly, in terms of the nature of the risk, the risk source is an objective factor in an experimental scene and a non-driver subjective factor.
Insubstantial risk is defined as the absence of substantial risk in a scene, the driver may not take reactive action, and the elements in the risk need to satisfy the following conditions:
condition one, from the end result, accidents generally do not occur;
the condition two is that the driver is allowed not to act from the human intervention;
the condition three is that the vehicle performance is good and no operation obstacle exists in terms of objective conditions;
and a fourth condition is that, in terms of risk nature, a risk source exists in the scene, and the subsequent risk is possibly caused by improper operation of the driver.
The risk urgency is defined as U, and the optimal starting time t for processing the risk is calculated according to vehicle kinematics and dynamics knowledgeuAnd a final cut-off time t that just does not lead to an accidentfDefinition of U ═ tu-tf. Then the threshold λ is defined as 1.6 s. If U is smaller than lambda, the system is in a risk urgent state, and the larger the difference value between U and lambda is, the higher the urgency degree is. The risk urgency degree U is divided into five grades, and the higher the grade number is, the higher the urgency degree is.
In the first stage of the process,
the urgency degree is 0, so that the risk of accidents is avoided or is extremely low;
in the second stage of the process,
pressing forceThe degree is 1, namely an accident is about to occur, but enough time is available for dealing with the accident;
in the third stage, the first stage and the second stage are combined,
the urgency is 2, an accident is about to occur, and a driver must immediately react to prevent the accident;
in the fourth stage, the first stage is a first stage,
the urgency degree is 3, so that an accident is about to occur, and a driver must immediately react to reduce the injury caused by the accident;
and in the fifth stage, U is less than 0, and the urgency is 4, namely the accident occurs.
The risk scene shown in fig. 3 is introduced, the scene is located in an urban area, the road type is a bidirectional double lane, the center line of the lane is a dotted line, the situation that motor vehicles and non-motor vehicles are mixed exists, parked vehicles occupy the lane on the lane close to a sidewalk, and the speed limit is 40 km/h. At that time, the driving speed of the scene capturing vehicle is about 30km/h, a non-motor vehicle with a speed of about 10km/h is merged into the lane where the scene capturing vehicle is located at about 20m in front of the scene capturing vehicle at about 0.1m/s, and a vehicle parked at the right side of the lane is started at 30 m and is merged into the lane where the scene capturing vehicle is located at about 1 m/s. The reaction baseline of the driver facing the scene is determined through basic principles of vehicle kinematics and dynamics, wherein the reaction baseline comprises the determination of an optimal interval, a suboptimal interval and a minimum limit interval of reaction time, the determination of an optimal interval, a suboptimal interval and a minimum limit interval of steering wheel steering angle, and the determination of an optimal interval, a suboptimal interval and a minimum limit interval of vehicle deceleration, which are shown in the following table.
| Reaction time(s) | Steering wheel angle (°) | Vehicle deceleration (m/s)2) | |
| Optimum interval | [0，1.5] | / | [2.7，3] |
| Sub-optimal interval | (1.5，2] | / | [2.4，2.7)、(3，4] |
| Minimum limit interval | (2，2.6] | / | [2.22，2.4)、(4，4.39] |
In the scene analysis, it is known that the speed of the vehicle is too high, and if the braking action is not taken in time and other traffic participants do not realize the risk, the vehicle inevitably collides with the non-motor vehicle and the parked vehicle merged into the lane, so that a substantial risk exists, the risk source 1 is the non-motor vehicle, and the risk source 2 is the parked vehicle merged into the lane.
In the embodiment of the invention, the driver danger reaction data are acquired and the driver danger perception capability is evaluated through the driver danger perception capability testing device, and the driver danger perception capability testing device comprises a risk scene presenting module, a driver operation acquiring module, a driver subjective evaluation acquiring module and an experiment control host. The risk scene presenting module is used for playing a driving scene, is connected with the experiment control host, and controls the display screen to present front windshield glass, two side rearview mirrors and an interior rearview mirror image to a driver by the experiment control host. The driver operation acquisition module is used for acquiring the operation information of the driver, converting the information of the driver such as the steering wheel rotation, the vehicle deceleration, the gear shifting mechanism operation and the like into digital signals to be input into the experiment control host, and converting the signals into data marked by a time shaft by the experiment control host to be stored. And the driver subjective evaluation acquisition module is used for presenting the investigation problem, recording the driver subjective evaluation and transmitting the driver subjective evaluation to the host.
The driver danger perception capability comprises three dimensions of a driver scene risk perception capability P, a driver reaction capability S and a driver danger avoiding operation capability C.
The driver scene risk perception capability P takes the reaction score of the driver for the risk scene as a reference, and the subjective evaluation score X of the driver for the risk sceneiThe calculation rule of (1) is as follows:
rule 1.1, if the driver correctly determines whether substantial risk exists in the scene, correctly discriminates the scene potential risk source and accurately determines the urgency of the scene risk, then Xi＝1；
Rule 1.2, if the driver correctly judges whether substantial risk exists in the scene, the potential risk source of the scene is correctly discriminated, but the scene risk urgency degree is incorrectly judged, Xi＝0.8；
Rule 1.3, if the driver correctly determines whether substantial risk exists in the scene, but does not correctly determine the potential risk source of the scene, then Xi＝0.6；
Rule 1.4, if there is a substantial risk in the scene of driver error determination, then Xi＝0。
The driver reaction ability S is a reaction behavior benchmark of a driver for a risk scene, and a driver reaction ability score YiThe calculation rule of (1) is as follows:
rule 2.1, if the driver is facing a risk scenario in the optimal reaction time interval [ ts1，ts2]By taking danger-avoiding operation, Yi＝1；
Rule 2.2, if the driver is in the suboptimal reaction time interval (t) when facing a risk scenarios2，ts3]Taking the risk avoiding operation in the system, wherein the time point of taking the risk avoiding operation is tiThen Y isiThe calculation method of (2) is as follows:
rule 2.3, if the driver is facing the risk scene, the time point of taking the risk avoidance operation is tiAnd t is and tiThe following conditions are satisfied: 0 < ti-ts3≤(ts3-ts1) 30% or 0 < ts1-ti≤(ts3-ts1) 30% of, then
Rule 2.4, if the driver is facing the risk scene, the time point t of the risk avoiding operation is adoptediSatisfies the condition ti-ts3＞(ts3-ts1) 30% or ts1-ti＞(ts3-ts1) 30% of, then Yi＝0。
The risk avoiding operation capability C of the driver takes the specific risk avoiding operation of the driver aiming at the risk scene as a reference, and the risk avoiding operation score Z of the driveriThe calculation rule is as follows:
rule 3.1, if the driver is facing the risk scene that the steering wheel needs to be rotated or the brake pedal is stepped on, the angle of the rotating steering wheel is in the optimal interval [ q ]u1，qu2]Or the vehicle braking deceleration is in the optimum interval qf2，qf3]Then Z isi＝1；
Rule 3.2, if the driver is facing a risk scenario that requires turning the steering wheel or stepping on the brake pedal, turnThe steering wheel angle is in a suboptimal interval (q)u2，qu3]Or the vehicle braking deceleration is in a suboptimal interval qf1，qf2)U(qf3，qf4]Then, then
Rule 3.3, if the driver faces the risk scene that the steering wheel needs to be rotated or the brake pedal is stepped on, the angle of the rotating steering wheel is in the minimum limit interval of 0 < qui-qu3≤(qu3-qu1) 30% or the vehicle braking deceleration is in the minimum interval 0 < qf1-qfi≤(qf3-qf1) 30% or 0 < qfi-qf4≤(qf4-qf2) 30% of, then
Rule 3.4, if the driver's steering wheel angle or vehicle brake deceleration exceeds the interval value specified by rules 3.1, 3.2, and 3.3, then Z is writteni＝0；
In the above rule, quiOutputting a value, q, for the driver's steering wheel anglefiA value is output for the driver to operate the vehicle brake deceleration.
The method for evaluating the driver danger perception capability adopts a fuzzy comprehensive evaluation method, and specifically comprises the following steps: determining an evaluation set of the driver danger perception capability index; determining weight values of risk perception capability P, driver reaction capability S and driver risk avoidance operation capability C for each type of risk scene according to an entropy weight method; determining the weight values of 5 types of risk scenes according to an analytic hierarchy process, respectively calculating judgment matrixes of the 5 types of risk scenes and the abilities of P, S and C of a driver, then solving a fuzzy relation matrix corresponding to the 5 types of risk scenes according to a membership function, multiplying and normalizing the solved fuzzy relation matrix and the weight values of corresponding indexes to obtain a fuzzy comprehensive evaluation matrix; and (4) obtaining the danger perception capability score of the driver according to the corresponding score of each evaluation set, and determining the evaluation grade of the danger perception capability.
The evaluation set of the driver danger perception capability index refers to the domain of the driver danger perception capability index and is divided into 5 grades in total, namely I ═ I { (I)1 I2 I3 I4 I5In which I1Grade 1, which represents very good; i is2Is grade 2, which means better; i is3Is 3 grades, which represents general; i is4Rank 4, indicating poor; i is5Grade 5, indicating very poor.
The entropy weight method is used for determining the risk perception capability P, the response capability S and the risk avoiding operation capability C of the driver aiming at each type of risk scene, and comprises the following steps:
for 3 evaluation indexes P, S, C, each index has n values Xi＝{xi1，xi2...xin}，Yi＝{yi1，yi2...yin}，Zi＝{zi1，zi2...zinStandardizing the index to obtain K1，K2...Kn，KijThe calculation method of (2) is as follows:
calculating the information entropy E of each index through the definition of the information entropyj：
Obtaining a weight vector v of 3 evaluation indexes according to the following formulaj：
The analytic hierarchy process needs to establish a hierarchical structure scheme, a decision target is the danger perception capability of a driver, decision factors consist of five types of risk scene straight roads, T-shaped intersections, special intersections, crossroads and turning roads, and the structural scheme is constructed as shown in the following table:
index hierarchy table
The core of the judgment matrix is to determine the relative importance degree among all factors of the index layer, and the criterion for constructing the judgment matrix is shown as the following table:
judgment matrix aijMethod of scaling
| Scale | Means of |
| 1 | Showing the same importance of the two factors compared |
| 3 | Indicating that one factor is slightly more important than the other factor when compared to the other |
| 5 | Indicating that one factor is significantly more important than the other factor when compared to the other factor |
| 7 | Indicating that one factor is more important than the other factor |
| 9 | Indicating that one factor is extremely important compared to the other factor |
| 2，4，6，8 | Median value of the above two adjacent judgments |
| Reciprocal of the | Judgment a of factor i compared with jijA judgment a comparing the factor j with the factor iji＝1/aij |
According to the criterion, various scene judgment matrixes are constructed, column vector normalization is carried out on the judgment matrixes, then row calculation and normalization are carried out, and weight values V of various scenes are obtainedi。
The establishment of the fuzzy relation matrix needs to calculate the membership of each index of the scene risk perception capability P, the response capability S and the risk avoiding operation capability C of the driver, and because the distribution of the scene risk perception capability of the driver belongs to the centered distribution, normal distribution is adopted as a membership distribution function, wherein a and b are constants.
The membership degree calculation mode is as follows:
wherein the content of the first and second substances,
the fuzzy relation matrix is calculated as follows:
wherein WiAnd (3) representing the risk perception capability P, the reaction capability S and the risk avoidance operation capability C evaluation index fuzzy matrix of the driver in the ith scene.
The fuzzy comprehensive evaluation matrix is defined as R, and the calculation method is as follows:
to RiWhen normalization is performed, R is expressed as follows:
the evaluation level of the driver danger perception capability is obtained by multiplying the weight value vector of each scene by a fuzzy comprehensive evaluation matrix R:
I＝VT*R
and obtaining the evaluation grade of the driver danger perception ability from the I according to the maximum membership principle.
The present invention has been described in detail with reference to the preferred embodiments thereof, and it should be understood that the invention is not limited thereto, but is intended to cover modifications, equivalents, and improvements within the spirit and scope of the present invention.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a portion of the steps in various embodiments may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a non-volatile computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the program is executed. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. A method for testing the danger perception capability of a driver is characterized by comprising the following steps:
collecting a road driving risk scene;
classifying risk scenes and building a scene library;
determining a driving response baseline;
collecting dangerous response data of a driver through a hardware-in-the-loop driving simulation test system;
and evaluating the danger perception capability of the driver.
2. The method for testing the driver risk perception capability according to claim 1, wherein the risk scenes comprise 5 types of straight roads, T-shaped intersections, special intersections, crossroads and turning roads, and the scene library is a risk scene video library which is built based on a computer technology and hardware-in-the-loop driving simulation system and is required for the risk perception capability test.
3. The method for testing the driver's danger perception capability according to claim 2, wherein the step of determining the driving reaction baseline specifically requires determining a risk avoidance manner, an optimal reaction time period, a sub-optimal reaction time period, an optimal turning angle amount interval of a steering wheel, a sub-optimal turning angle amount interval of the steering wheel, an optimal vehicle braking deceleration interval, and a sub-optimal interval to be adopted based on the type of the risk scene.
4. The method for testing the driver danger awareness ability according to claim 3, wherein the step of collecting the driver danger reaction data through a hardware-in-the-loop driving simulation test system specifically comprises:
when a driver is placed in the hardware-in-the-loop driving simulation test system, a risk scene display screen plays a driving risk scene, wherein the driving risk scene comprises normal driving and dangerous event precursor occurrence;
when a dangerous event is about to occur, the display screen stops playing, after the video playing is finished, risk scene evaluation is conducted on the driver, and scene evaluation data, driver reaction data and driver operation data of the driver are recorded.
5. The method for testing the driver danger awareness ability according to claim 4, wherein the driver danger response data are collected and the driver danger awareness ability is evaluated through a driver danger awareness ability testing device, and the driver danger awareness ability testing device comprises a risk scene presenting module, a driver operation collecting module, a driver subjective evaluation collecting module and an experiment control host.
6. The method for testing the driver danger perception capability of claim 5, wherein the driver danger perception capability includes a driver scene risk perception capability P, a driver reaction capability S and a driver danger avoidance operation capability C.
7. The method for testing the driver's risk perception capability of claim 6, wherein the driver's scene risk perception capability P is based on a reaction score of the driver to the risk scene, and a subjective evaluation score X of the driver to the risk sceneiThe calculation rule of (1) is as follows:
if the driver correctly judges whether substantial risks exist in the scene, correctly discriminates the potential risk sources of the scene and accurately judges the urgency degree of the scene risks, Xi＝1；
If the driver correctly judges whether substantial risks exist in the scene, the potential risk sources of the scene are correctly discriminated, but the scene risk urgency degree is incorrectly judged, Xi＝0.8；
If the driver correctly judges whether the substantive risk exists in the scene but does not correctly discriminate the potential risk source of the scene, Xi＝0.6；
If the driver mistakenly judges whether substantial risk exists in the scene, Xi＝0。
8. The method for testing driver 'S risk perception capability of claim 7, wherein the driver' S reaction capability S is a driver 'S reaction behavior benchmark against a risk scenario, and the driver' S reaction capability score YiThe calculation rule of (c) is as follows:
if the driver is in the optimal reaction time interval t when facing the risk scenes1，ts2]By taking danger-avoiding operation, Yi＝1；
If the driver is in the suboptimal reaction time interval (t) when facing the risk scenes2，ts3]Taking the risk avoiding operation in the system, wherein the time point of taking the risk avoiding operation is tiThen Y isiThe calculation method of (2) is as follows:
if the driver faces the risk scene, the time point of taking the risk avoiding operation is tiAnd t isiThe following conditions are satisfied: 0 < ti-ts3≤(ts3-ts1) 30% or 0 < ts1-ti≤(ts3-ts1) 30% of, then
If the driver faces the risk scene, adopting the time point t of risk avoiding operationiSatisfies the condition ti-ts3＞(ts3-ts1) 30% or ts1-ti＞(ts3-ts1) 30% of, then Yi＝0。
9. The method for testing the driver risk perception capability according to claim 8, wherein the driver risk avoiding operation capability C is based on a specific risk avoiding operation performed by a driver for a risk scene, and the driver risk avoiding operation score Z isiThe calculation rule is as follows:
if the driver faces the risk scene that the steering wheel needs to be rotated or the brake pedal is stepped on, the angle of the rotating steering wheel is in the optimal interval [ q ]u1，qu2]Or the vehicle braking deceleration is in the optimum interval qf2，qf3]Then Z isi＝1；
If a driver faces a risk scene that the steering wheel needs to be rotated or the brake pedal needs to be stepped on, the angle of the rotating steering wheel is in a suboptimal interval (q)u2，qu3]Or the vehicle braking deceleration is in a suboptimal interval qf1，qf2)∪(qf3，qf4]Then, then
If the driver faces the risk scene that the steering wheel needs to be rotated or the brake pedal is stepped on, the angle of the rotating steering wheel is in the minimum limit value interval of 0 < qui-qu3≤(qu3-qu1) 30% or the vehicle braking deceleration is in the minimum interval 0 < qf1-qfi≤(qf3-qf1) 30% or 0 < qfi-qf4≤(qf4-qf2) 30% of, then
If the steering wheel angle operated by the driver or the vehicle braking deceleration exceeds the interval value specified by the above rule, then Z is recordedi＝0；
In the above rule, quiOutputting a value, q, for the driver's steering wheel anglefiA value is output for the driver to operate the vehicle brake deceleration.
10. The method for testing the driver danger perception capability according to claim 9, wherein the evaluation of the driver danger perception capability adopts a fuzzy comprehensive evaluation method, and specifically comprises:
determining an evaluation set of the driver danger perception capability index;
determining weight values of risk perception capability P, driver reaction capability S and driver risk avoidance operation capability C for each type of risk scene according to an entropy weight method;
determining the weight values of 5 types of risk scenes according to an analytic hierarchy process, respectively calculating judgment matrixes of the 5 types of risk scenes and the abilities of P, S and C of a driver, then solving a fuzzy relation matrix corresponding to the 5 types of risk scenes according to a membership function, multiplying and normalizing the solved fuzzy relation matrix and the weight values of corresponding indexes to obtain a fuzzy comprehensive evaluation matrix;
and (4) obtaining the danger perception capability score of the driver according to the corresponding score of each evaluation set, and determining the evaluation grade of the danger perception capability.
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