CN113325261B - Temperature and humidity adaptability test method and system for industrial control hardware of automatic driving vehicle - Google Patents
Temperature and humidity adaptability test method and system for industrial control hardware of automatic driving vehicle Download PDFInfo
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Abstract
The invention discloses a temperature and humidity adaptability test method for industrial control hardware of an automatic driving vehicle, which comprises the following steps: acquiring a road model to be driven by an automatic driving vehicle; generating an input signal of a vehicle-mounted computing platform of the autonomous vehicle according to the road model; acquiring a working condition model of the vehicle-mounted computing platform; setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model; importing the input signal into a vehicle-mounted computing platform, wherein the vehicle-mounted computing platform simulates the operation situation of a real driving process; and acquiring an output signal of the vehicle-mounted computing platform. The invention can accurately simulate the environment condition of the vehicle-mounted computing platform during actual working, and can effectively detect the running condition of the vehicle-mounted computing platform under different working environment conditions.
Description
Technical Field
The present invention relates to the field of autopilot. More specifically, the invention relates to a temperature and humidity adaptability test method and system for industrial control hardware of an automatic driving vehicle.
Background
With the development of automobile intellectualization and networking, the automatic driving technology gradually simplifies automobile driving, but the research and development of automatic driving need to be deeper and deeper. In the face of the vast demand population in the south, the sea and the north, the environmental adaptability of the automatic driving of the automobile faces a great test. When the automatic driving system is in actual operation, data are collected through equipment such as a camera, a laser radar and a high-precision positioning instrument which are arranged outside a vehicle, and then a black box arranged inside the vehicle is used for carrying out mass operation to obtain a result, so that the vehicle is controlled to move. Data processing and storage in autonomous vehicles has become a mandatory requirement in europe and america. NHTSA and DMV in california in the united states require that the autopilot system properly store and maintain accident data for investigation. German federal academy of commission allows automotive autopilot systems to replace human driving under certain conditions in the future by law. The law clearly states that a device similar to a black box is installed in an automobile provided with an automatic driving system, and the specific conditions of different stages of system operation, intervention requirements, manual driving and the like are recorded so as to clearly determine the responsibility of a traffic accident. The vehicle-mounted computing platform is a black box, mainly comprises functions of monitoring, computing, storing and replaying, and mainly comprises hardware: the system comprises a vehicle-mounted data acquisition board card, a processor, a memory, gateway equipment and the like. In the operation process of the automatic driving system, a vehicle-mounted computing platform generates a large amount of heat while performing massive computation, storage and other work, so that the requirement on the adaptability of the hardware environment under extremely hot extreme environments is increased.
Therefore, an environmental suitability test method for the hardware of the autopilot computing platform is urgently needed to evaluate and guarantee the universality of system operation.
Disclosure of Invention
It is an object of the present invention to address at least the above problems and to provide at least the advantages described hereinafter.
The invention also aims to provide a temperature and humidity adaptability test method and system for the industrial control hardware of the automatic driving vehicle, which can accurately simulate the environment condition of the vehicle-mounted computing platform during actual working and can effectively detect the running condition of the vehicle-mounted computing platform under different working environment conditions.
To achieve these objects and other advantages and in accordance with the purpose of the invention, as embodied and broadly described herein, there is provided a temperature and humidity adaptability test method of industrial control hardware of an autonomous vehicle, comprising:
acquiring a road model to be driven by an automatic driving vehicle;
generating an input signal of a vehicle-mounted computing platform of the autonomous vehicle according to the road model;
acquiring a working condition model of the vehicle-mounted computing platform;
setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model;
importing the input signal into a vehicle-mounted computing platform, wherein the vehicle-mounted computing platform simulates the operation situation of a real driving process;
and acquiring an output signal of the vehicle-mounted computing platform.
Preferably, the method of generating an input signal for an on-board computing platform of an autonomous vehicle from a road model comprises:
the method comprises the steps that the proportion of the length of each first road section in a road model to be driven by an automatic driving vehicle is obtained, the road model to be driven by the automatic driving vehicle is divided into at least one first road section according to road condition grades, each first road section corresponds to one road condition grade, the road condition grades are obtained by dividing the road conditions according to preset conditions, and the number of the road condition grades is multiple;
acquiring real road signals acquired by the automatic driving vehicle at a second road section of each road condition grade;
and intercepting and splicing real road signals acquired by the second road sections with different road condition grades according to the proportion of the length of the first road section with the corresponding road condition grade to the length of the road model to be driven by the automatic driving vehicle to obtain input signals of the vehicle-mounted computing platform.
Preferably, when the real road signals collected by the second road sections with different road condition grades are spliced, the real road signals are spliced according to the appearance sequence of the first road section with the corresponding road condition grade on the road to be driven by the automatic driving vehicle.
Preferably, the real road signal includes: laser radar signals, camera signals, radar input signals and high-precision position finder signals.
Preferably, the method of dividing the road condition into a plurality of road condition classes according to the preset condition includes:
and dividing the road condition into a plurality of road condition grades according to two indexes of traffic flow density and control difficulty of the driving route in the driving process, wherein each road condition grade corresponds to a preset traffic flow density value range and a preset control difficulty value range of the driving route.
Preferably, the calculation formula of the traffic flow density a during driving is:
wherein L is the length of the road segment, m is the number of the traffic participants, L i The number of the i-th traffic participants.
Preferably, the calculation formula of the control difficulty B of the driving route is:
wherein R is the length of the road segment, n is the number of road type categories, b is the number of intersection type categories, R j Road length, S, for class j road type a The number of intersections of the type of the a-th intersection.
Preferably, the operating condition model includes: the vehicle-mounted computing platform comprises two indexes, namely a vehicle-mounted computing platform installation position and a running environment factor of an automatic driving vehicle carrying the vehicle-mounted computing platform;
the method for setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model comprises the following steps:
acquiring the working condition grade of the vehicle-mounted computing platform according to the working condition model, wherein the working condition of the vehicle-mounted computing platform is divided into multiple working condition grades according to the mounting position of the vehicle-mounted computing platform and two indexes of the running environment factor of the automatic driving vehicle carrying the vehicle-mounted computing platform, and each working condition grade corresponds to the preset specific mounting position of the vehicle-mounted computing platform and the running environment factor value range of the automatic driving vehicle carrying the vehicle-mounted computing platform;
and setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition grades of the vehicle-mounted computing platform, wherein the initial temperature and humidity of the environment where different vehicle-mounted computing platforms are located are preset correspondingly in different working condition grades.
The invention also provides a temperature and humidity adaptability test system of the industrial control hardware of the automatic driving vehicle, which comprises the following components:
a road model acquisition module for acquiring a road model on which an autonomous vehicle is to travel;
an input signal generation module for generating an input signal of an on-board computing platform of an autonomous vehicle from a road model;
the working condition acquisition module is used for acquiring a working condition model of the vehicle-mounted computing platform;
the environment setting module is used for setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model;
the signal import module is used for importing the input signal into a vehicle-mounted computing platform, and the vehicle-mounted computing platform simulates the operation situation of the real driving process;
and the output signal acquisition module is used for acquiring the output signal of the vehicle-mounted computing platform.
The invention also provides a temperature and humidity adaptability testing device of the industrial control hardware of the automatic driving vehicle, which comprises the following components:
the test box is used for loading the vehicle-mounted computing platform;
an electronic device, the electronic device comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any of claims 1-8, the electronic device having an interface to interface with an in-vehicle computing platform; and
the temperature and humidity sensor is arranged in the test box, and the execution device is used for adjusting the temperature and humidity in the test box and is connected with the electronic equipment.
The invention at least comprises the following beneficial effects: the method provided by the invention can be used for testing the temperature and humidity environment adaptability of the vehicle-mounted computing platform in the working state. Meanwhile, the vehicle-mounted automatic driving vehicle test system can selectively combine and test different mounting positions of the vehicle-mounted computing platform, different driving environments of the automatic driving vehicle and different driving road conditions so as to adapt to the automatic driving vehicles with different purposes or different models, and has wide application range. In addition, the device provided by the invention can truly simulate the effect of superposition of heat energy generated by the vehicle-mounted computing platform during operation and the ambient temperature, and more accurately restore the operation state of the vehicle-mounted computing platform in the actual working state.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention.
Drawings
Fig. 1 is a flow chart of a temperature and humidity adaptability test method according to the present invention;
fig. 2 is a schematic diagram of splicing real road signals in the temperature and humidity adaptability test method of the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
It is to be noted that the experimental methods described in the following embodiments are all conventional methods unless otherwise specified, and the reagents and materials, if not otherwise specified, are commercially available; in the description of the present invention, the terms "lateral", "longitudinal", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention.
As shown in fig. 1, the invention provides a temperature and humidity adaptability test method for industrial control hardware of an automatic driving vehicle, which comprises the following steps:
s1, acquiring a road model to be driven by an automatic driving vehicle;
the road model may be several preset typical road models, such as: the model of the urban main road of the first-line city in China, or the model of the country road, and the like. In order to simulate more road conditions, a user-defined road model can be set;
s2, generating an input signal of a vehicle-mounted computing platform of the automatic driving vehicle according to the road model;
the method for generating an input signal for an on-board computing platform of an autonomous vehicle from a road model comprises:
s21, acquiring the proportion of the length of each first road section in a road model to be driven by the automatic driving vehicle, wherein the road model to be driven by the automatic driving vehicle is divided into at least one first road section according to road condition grades, each first road section corresponds to one road condition grade, the road condition grades are obtained by dividing the road conditions according to preset conditions, and the number of the road condition grades is multiple;
specifically, if the selected road model is one of several preset typical road models, the typical road model road condition data can be collected and stored in advance, so that the proportion of the length of each first road section in the road model to be driven by the automatic driving vehicle can be obtained from the typical road model road condition data collected and stored in advance, and if the selected road model is the user-defined road model, the user can automatically input the proportion of the length of the first road section of each road condition grade to the total length of the road model, and more ideally, the user can also automatically input the front-back sequence of the first road section of each road condition grade appearing in the road model.
There are various methods for dividing the road condition into multiple road condition classes according to preset conditions, and the method adopted in this embodiment is as follows:
and dividing the road condition into a plurality of road condition grades according to two indexes of traffic flow density and control difficulty of the driving route in the driving process, wherein each road condition grade corresponds to a preset traffic flow density value range and a preset control difficulty value range of the driving route.
As shown in table 1, the traffic flow density during travel is represented by a, the control difficulty of the travel route is represented by B, and the road condition rank is represented by K.
TABLE 1
| A1 | A2 | A3 | A4 | |
| B1 | K1 | K2 | K3 | K4 |
| B2 | K2 | K3 | K4 | K5 |
| B3 | K3 | K4 | K5 | K6 |
| B4 | K4 | K5 | K6 | K7 |
The traffic flow density in the driving process can be divided into four levels A1-A4 according to different value ranges of A, the control difficulty of the driving route can be divided into four levels B1-B4 according to different value ranges of B, and the road condition can be divided into 7 road condition levels of K1-K7 by combining the traffic flow density of different levels and the control difficulty of different levels.
Here, the calculation formula of the traffic flow density a during travel may be various, and the calculation formula employed in the present embodiment is as follows:
wherein L is the length of the road segment, m is the number of the traffic participants, L i The number of the i-th traffic participants includes: pedestrian, nonAutomotive vehicles, motor vehicles, and the like.
As can be seen from the above formula of a, the value a actually represents the average value of the types and the numbers of the transportation participants after dimensionless processing, so that the values a in different value ranges are set to different levels, that is, the road conditions are actually distinguished according to the types and the numbers of the transportation participants, for example: assuming that x3> x2> x1>0, and setting A1=0, it indicates that the road is open during driving and there are substantially no traffic participants, setting A2 ∈ (0, x 1), it indicates that the traffic participants on the road are sparse during driving and there are few types and numbers of traffic participants, setting A3 ∈ (x 1, x 2), it indicates that the traffic participants on the road are daily levels and there are some types and numbers of traffic participants, and setting A4 ∈ (x 2, x 3), it indicates that the road traffic during driving and there are a large number of types and numbers of traffic participants and road congestion.
Specifically, the values of x1, x2, and x3 may be set by the user according to the specific situation.
Here, the calculation formula of the control difficulty B of the driving route may be various, and the calculation formula adopted in the present embodiment is as follows:
wherein R is the length of the road segment, n is the number of road type categories, b is the number of intersection type categories, R j Road length, S, for class j road type a The number of intersections of the type a, specifically, the road type includes: straight roads, curved roads (single or continuous), u-turn roads, tidal lanes, non-paved lanes, etc. Specifically, the intersection types include: crossroads, T-shaped intersections, Y-shaped intersections, special-shaped intersections and the like.
It can be seen from the above formula of B that the B value actually represents the mean of the road type and number and the intersection type and number after dimensionless processing, so that different levels are set for the B values in different value ranges, that is, the road conditions are actually distinguished according to the road type and number and the intersection type and number, for example: the method is characterized in that y4> y3> y2> y1>0, B1 belongs to [ y1, y2], the road is simple, the road basically only comprises common road types such as straight roads and curved roads, no intersections or infinitesimal intersections, B2 belongs to [ y2, y3], the road difficulty is general, the road difficulty is difficult, the road difficulty is general, the road difficulty comprises common road types such as straight roads, curved roads and u-turns, few special roads such as tidal lanes and non-paved roads are few, the number of intersections is general level, B3 belongs to [ y3, y4], the road difficulty is difficult, the road types include common road types such as straight roads, curved roads and u-turns, and the road difficulty is high due to the fact that the tidal lanes and the non-paved roads are more and the number of intersections are great, the B4 belongs to [ y4], the road difficulty is very difficult, and the road difficulty is difficult due to the fact that the roads are common road types such as straight roads, the curved roads and the U-turns are many.
Specifically, the values of y1, y2, y3 and y4 can be set by the user according to specific conditions.
S22, acquiring real road signals acquired by the automatic driving vehicle at a second road section of each road condition grade;
the real road signal is a signal collected by hardware equipment when a vehicle carrying the automatic driving system runs on an open area road, and the real road signal of the second road section of each road condition grade can be stored in a memory after being collected.
Here, the real road signal may include: laser radar signals, camera signals, radar input signals and high-precision position finder signals.
Laser radar signal Ri: the method comprises the following steps that a point cloud data set original signal acquired by a laser radar installed on an automatic driving vehicle running on an actual road is Ri, and 1 to n laser radars can be carried by one vehicle according to different arrangement schemes;
camera signal Vi: the method comprises the following steps that a camera installed on an automatic driving vehicle drives on an actual road to acquire an original signal of image data Vi, and at least 2 cameras are usually arranged on one vehicle;
radar input signal Li: the radar installed on the automatic driving vehicle runs on an actual road and acquires data original signals Li, the radar works in a millimeter wave band, the working frequency is 30-100 GHz, and 2-n radars are usually arranged on one vehicle according to a design scheme;
high-precision locator signal Ti: a vehicle track signal acquired by a radar installed on an automatic driving vehicle running on an actual road is Ti, and longitude, latitude, elevation, vehicle speed in the north, the south and east, pitch angle, roll angle, course angle and the like are usually acquired;
in the acquisition, all the above signals need to be time-aligned, that is, the same sampling frequency is adopted, the acquisition is started at the same time, and the acquisition is ended at the same time.
And S23, intercepting and splicing real road signals acquired by the second road sections with different road condition grades according to the proportion of the length of the first road section with the corresponding road condition grade to the length of the road model to be driven by the automatic driving vehicle to obtain input signals of the vehicle-mounted computing platform.
According to the difference of the using audience and the product direction, the development directions of the automatic driving system are different, so that after the road condition grade is selected in the table 1 according to the road model to be driven by the automatic driving vehicle, the actual road signal of the second road section with the same road condition grade in the road model can be selected to be combined;
such as: after user use survey, an automatic driving system finds that the K3 level situation accounts for about 77% of the automatic driving range, the K5 level situation accounts for about 18% of the automatic driving range, and the K7 level situation accounts for about 5% of the automatic driving range. Therefore, the real road signal of the corresponding second road section can be selected for intercepting and splicing, as shown in fig. 2.
Preferably, when the real road signals collected from the second road segments with different road condition grades are spliced, the real road signals are spliced according to the appearance sequence of the first road segments with the corresponding road condition grades on the road to be driven by the automatic driving vehicle.
S3, acquiring a working condition model of the vehicle-mounted computing platform;
the operating condition model herein may include: the vehicle-mounted computing platform comprises two indexes, namely a vehicle-mounted computing platform mounting position and a running environment factor of an automatic driving vehicle carrying the vehicle-mounted computing platform.
S4, setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model;
the method for setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model comprises the following steps:
s41, acquiring the working condition grade of the vehicle-mounted computing platform according to the working condition model, dividing the working condition of the vehicle-mounted computing platform into multiple working condition grades according to the mounting position of the vehicle-mounted computing platform and the running environment factor of the automatic driving vehicle carrying the vehicle-mounted computing platform, wherein each working condition grade is correspondingly preset with the specific mounting position of the vehicle-mounted computing platform and the running environment factor value range of the automatic driving vehicle carrying the vehicle-mounted computing platform;
there are various methods for dividing the operating conditions into multiple operating condition classes, and the method adopted in the embodiment is as follows:
and dividing the vehicle into multiple working condition grades according to the two indexes of the installation position of the vehicle-mounted computing platform and the running environment factor of the automatic driving vehicle carrying the vehicle-mounted computing platform.
As shown in table 2, the mounting position of the in-vehicle computing platform is denoted by P, the running environment temperature of the autonomous vehicle mounting the in-vehicle computing platform is denoted by C, and the operation level is denoted by N.
TABLE 2
| P1 | P2 | |
| C1 | N1 | N2 |
| C2 | N2 | N3 |
If the number of the installation positions of the vehicle-mounted computing platform is only two, the installation positions of the vehicle-mounted computing platform can be divided into two stages P1-P2, the running environment factors of the automatic driving vehicle carrying the vehicle-mounted computing platform can be divided into two stages C1-C2 according to whether the running environment is severe or not, and the working conditions can be divided into 3 working condition levels of N1-N3 by combining P in different stages with C in different stages.
Specifically, P1 may indicate that the mounting position of the vehicle-mounted computing platform is in the front cabin, and P2 may indicate that the mounting position of the vehicle-mounted computing platform is in the trunk.
Specifically, C1 may indicate that the running environment temperature of the autonomous vehicle equipped with the vehicle-mounted computing platform is an adaptive environment temperature, and the range of values is [ z1, z2], C2 may indicate that the running environment temperature of the autonomous vehicle equipped with the vehicle-mounted computing platform is a severe environment temperature, and the range of values is [ z0, z 1)' u (z 2, z3], where the values of z0, z1, z2, and z3 may be set by a user according to specific situations.
And S42, setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition grades of the vehicle-mounted computing platform, wherein the initial temperature and humidity of the environment where different vehicle-mounted computing platforms are located are preset correspondingly in different working condition grades.
And selecting the working condition grade N according to the actual condition survey of the audience.
For example: the main country for selling the automatic driving vehicle is Thailand, the annual temperature range of the automatic driving vehicle is within the range of 15-45 ℃, the temperature is comfortable, accordingly, the driving environment factor is selected to be C1, the vehicle-mounted computing platform is installed in the front cabin, accordingly, P1 is selected, and the selected working condition grade is N1 by integrating the conditions.
The initial temperature and humidity of the environment where the vehicle-mounted computing platform is located are preset correspondingly to different working condition grades, so that the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located can be obtained after the working condition grade N is selected.
S5, importing the input signal into a vehicle-mounted computing platform, wherein the vehicle-mounted computing platform simulates the operation situation of a real driving process;
and S6, acquiring an output signal of the vehicle-mounted computing platform.
Whether the vehicle-mounted computing platform can work normally, well and faultlessly under the preset path and the temperature and humidity environment can be judged by observing whether the output signal is correct or not.
The invention also provides a temperature and humidity adaptability test system of the industrial control hardware of the automatic driving vehicle, which comprises the following components:
a road model acquisition module for acquiring a road model on which an autonomous vehicle is to travel;
an input signal generation module for generating an input signal of an on-board computing platform of an autonomous vehicle from a road model;
the working condition acquisition module is used for acquiring a working condition model of the vehicle-mounted computing platform;
the environment setting module is used for setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model;
the signal leading-in module is used for leading input signals into the vehicle-mounted computing platform and simulating the operation situation of the vehicle-mounted computing platform in the real driving process;
and the output signal acquisition module is used for acquiring the output signal of the vehicle-mounted computing platform.
In another embodiment, the method for generating input signals for an on-board computing platform of an autonomous vehicle from a road model by the input signal generation module comprises:
the method comprises the steps that the proportion of the length of each first road section in a road model to be driven by an automatic driving vehicle is obtained, the road model to be driven by the automatic driving vehicle is divided into at least one first road section according to road condition grades, each first road section corresponds to one road condition grade, the road condition grades are obtained by dividing road conditions according to preset conditions, and the number of the road condition grades is multiple;
acquiring real road signals acquired by the automatic driving vehicle at a second road section of each road condition grade;
and intercepting and splicing real road signals acquired by the second road sections with different road condition grades according to the proportion of the length of the first road section with the corresponding road condition grade to the length of the road model to be driven by the automatic driving vehicle to obtain input signals of the vehicle-mounted computing platform.
In another embodiment, when the input signal generation module splices the real road signals collected by the second road segments with different road condition grades, the real road signals are spliced according to the appearance sequence of the first road segments with the corresponding road condition grades on the road to be driven by the automatic driving vehicle.
In another embodiment, the real road signal acquired by the input signal generation module includes: laser radar signals, camera signals, radar input signals and high-precision position finder signals.
In another embodiment, the method for dividing the road condition into a plurality of road condition classes according to the preset condition includes:
and dividing the road condition into a plurality of road condition grades according to two indexes of traffic flow density and control difficulty of the driving route in the driving process, wherein each road condition grade corresponds to a preset traffic flow density value range and a preset control difficulty value range of the driving route.
In another embodiment, the calculation formula of the traffic flow density a during driving is as follows:
wherein L is the length of the road segment, m is the number of the traffic participants, L i The number of the i-th traffic participants.
In another embodiment, the calculation formula of the control difficulty B of the driving route is as follows:
wherein R is the length of the road section, and n is the number of road type categoriesB is the number of types of road junctions, r j Road length, S, for class j road type a The number of intersections of the type of the a-th intersection.
In another embodiment, the operating condition model of the vehicle-mounted computing platform acquired by the operating condition acquiring module includes: the vehicle-mounted computing platform comprises two indexes, namely a vehicle-mounted computing platform installation position and a running environment factor of an automatic driving vehicle carrying the vehicle-mounted computing platform;
the method for setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located by the environment setting module according to the working condition model comprises the following steps:
acquiring the working condition grade of the vehicle-mounted computing platform according to the working condition model, wherein the working condition of the vehicle-mounted computing platform is divided into multiple working condition grades according to the mounting position of the vehicle-mounted computing platform and two indexes of the running environment factor of the automatic driving vehicle carrying the vehicle-mounted computing platform, and each working condition grade corresponds to the preset specific mounting position of the vehicle-mounted computing platform and the running environment factor value range of the automatic driving vehicle carrying the vehicle-mounted computing platform;
and setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition grades of the vehicle-mounted computing platform, wherein the initial temperature and humidity of the environment where different vehicle-mounted computing platforms are located are preset correspondingly in different working condition grades.
The invention also provides a temperature and humidity adaptability testing device of the industrial control hardware of the automatic driving vehicle, which comprises the following components:
the test box is used for loading the vehicle-mounted computing platform; and
an electronic device, the electronic device comprising: the electronic equipment comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions which can be executed by the at least one processor, the instructions are executed by the at least one processor, so that the at least one processor executes the temperature and humidity adaptability test method, and the electronic equipment is provided with an interface which is connected with an in-vehicle computing platform;
set up the temperature and humidity sensor in the test box and adjust the final controlling element of temperature and humidity in the test box, final controlling element can include: the electric heater, the refrigerator, the humidifier, the fan and the like, and the execution device is connected with the electronic equipment.
When the device is used, the vehicle-mounted computing platform is placed in the test box, the electronic equipment is connected with the vehicle-mounted computing platform, real-time temperature and humidity in the test box can be known through the temperature and humidity sensor, the electronic equipment can adjust the temperature and humidity in the test box to the temperature and humidity of a simulated driving environment by calling the execution device, input signals of the vehicle-mounted computing platform are generated, after the electronic equipment guides the input signals into the vehicle-mounted computing platform, when the vehicle-mounted computing platform simulates the operation situation of the vehicle-mounted computing platform in the real driving process, output signals of the vehicle-mounted computing platform can be obtained through the electronic equipment.
While embodiments of the invention have been described above, it is not intended to be limited to the details shown, described and illustrated herein, but is to be accorded the widest scope consistent with the principles and novel features herein disclosed, and to such extent that such modifications are readily available to those skilled in the art, and it is not intended to be limited to the details shown and described herein without departing from the general concept as defined by the appended claims and their equivalents.
Claims (9)
1. Temperature and humidity adaptability test method for industrial control hardware of automatic driving vehicle is characterized by comprising the following steps:
acquiring a road model to be driven by an automatic driving vehicle;
generating an input signal of a vehicle-mounted computing platform of the autonomous vehicle according to the road model;
acquiring a working condition model of the vehicle-mounted computing platform;
setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model;
importing the input signal into a vehicle-mounted computing platform, wherein the vehicle-mounted computing platform simulates the operation situation of a real driving process;
acquiring an output signal of a vehicle-mounted computing platform;
the method for generating the input signal of the vehicle-mounted computing platform of the automatic driving vehicle according to the road model comprises the following steps:
the method comprises the steps that the proportion of the length of each first road section in a road model to be driven by an automatic driving vehicle is obtained, the road model to be driven by the automatic driving vehicle is divided into at least one first road section according to road condition grades, each first road section corresponds to one road condition grade, the road condition grades are obtained by dividing the road conditions according to preset conditions, and the number of the road condition grades is multiple;
acquiring real road signals acquired by the automatic driving vehicle at a second road section of each road condition grade;
and intercepting and splicing real road signals acquired by the second road sections with different road condition grades according to the proportion of the length of the first road section with the corresponding road condition grade to the length of the road model to be driven by the automatic driving vehicle to obtain input signals of the vehicle-mounted computing platform.
2. The method for testing temperature and humidity adaptability of industrial control hardware of an autonomous vehicle of claim 1 wherein when splicing the real road signals collected from the second road segments of different road condition grades, the splicing is further performed according to the appearance sequence of the first road segments of the corresponding road condition grades on the road to be driven by the autonomous vehicle.
3. The method of claim 1, wherein the real road signal comprises: laser radar signals, camera signals, radar input signals and high-precision position finder signals.
4. The method for testing the temperature and humidity adaptability of the industrial control hardware of the autonomous vehicle as claimed in claim 1, wherein the method for dividing the road condition into a plurality of road condition grades according to the preset condition comprises:
and dividing the road condition into a plurality of road condition grades according to two indexes of traffic flow density and control difficulty of the driving route in the driving process, wherein each road condition grade corresponds to a preset traffic flow density value range and a preset control difficulty value range of the driving route.
5. The temperature of industrial control hardware of autonomous vehicle as recited in claim 4The humidity adaptability test method is characterized in that a calculation formula of traffic flow density A in the driving process is as follows:
wherein L is the length of the road segment, m is the number of the traffic participants, L i The number of the ith traffic participants.
6. The temperature and humidity adaptability test method of industrial control hardware of the automatic driving vehicle as claimed in claim 4, characterized in that the calculation formula of the control difficulty B of the driving route is as follows:
wherein R is the length of the road segment, n is the number of the types of the road, b is the number of the types of the intersection, R j Road length, S, for class j road type a The number of intersections of the type of the a-th intersection.
7. The temperature and humidity adaptability test method of industrial control hardware of an autonomous vehicle as recited in claim 1, characterized in that the operating condition model comprises: the vehicle-mounted computing platform comprises two indexes, namely a vehicle-mounted computing platform installation position and a running environment factor of an automatic driving vehicle carrying the vehicle-mounted computing platform;
the method for setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model comprises the following steps:
acquiring the working condition grade of the vehicle-mounted computing platform according to the working condition model, wherein the working condition of the vehicle-mounted computing platform is divided into multiple working condition grades according to the mounting position of the vehicle-mounted computing platform and two indexes of the running environment factor of the automatic driving vehicle carrying the vehicle-mounted computing platform, and each working condition grade corresponds to the preset specific mounting position of the vehicle-mounted computing platform and the running environment factor value range of the automatic driving vehicle carrying the vehicle-mounted computing platform;
and setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition grades of the vehicle-mounted computing platform, wherein the initial temperature and humidity of the environment where different vehicle-mounted computing platforms are located are preset correspondingly in different working condition grades.
8. Temperature and humidity adaptability test system of automatic driving vehicle industrial control hardware, its characterized in that includes:
a road model acquisition module for acquiring a road model on which an autonomous vehicle is to travel;
an input signal generation module for generating an input signal of an on-board computing platform of an autonomous vehicle from a road model;
the working condition acquisition module is used for acquiring a working condition model of the vehicle-mounted computing platform;
the environment setting module is used for setting the initial temperature and humidity of the environment where the vehicle-mounted computing platform is located according to the working condition model;
the signal import module is used for importing the input signal into a vehicle-mounted computing platform, and the vehicle-mounted computing platform simulates the operation situation of the real driving process;
the output signal acquisition module is used for acquiring an output signal of the vehicle-mounted computing platform;
the method for generating the input signal of the vehicle-mounted computing platform of the automatic driving vehicle by the input signal generation module according to the road model comprises the following steps:
the method comprises the steps that the proportion of the length of each first road section in a road model to be driven by an automatic driving vehicle is obtained, the road model to be driven by the automatic driving vehicle is divided into at least one first road section according to road condition grades, each first road section corresponds to one road condition grade, the road condition grades are obtained by dividing road conditions according to preset conditions, and the number of the road condition grades is multiple;
acquiring real road signals acquired by the automatic driving vehicle at a second road section of each road condition grade;
and intercepting and splicing real road signals acquired by the second road sections with different road condition grades according to the proportion of the length of the first road section with the corresponding road condition grade to the length of the road model to be driven by the automatic driving vehicle to obtain input signals of the vehicle-mounted computing platform.
9. Temperature and humidity adaptability testing arrangement of automatic driving vehicle industrial control hardware, its characterized in that includes:
the test box is used for loading the vehicle-mounted computing platform;
an electronic device, the electronic device comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method of any of claims 1-7, the electronic device having an interface to interface with an in-vehicle computing platform; and
the temperature and humidity sensor is arranged in the test box, and the execution device is used for adjusting the temperature and humidity in the test box and is connected with the electronic equipment.
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