CN109991024B - Three-level automatic driving vehicle over-bending capability test method - Google Patents
Three-level automatic driving vehicle over-bending capability test method Download PDFInfo
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Abstract
The invention discloses a method for testing the bending passing capacity of a three-level automatic driving vehicle, which comprises the following steps of: step 1, mounting a driving robot, a GPS positioning device, a data synchronization card and an industrial personal computer on a test vehicle, and respectively connecting the data synchronization card with the driving robot, the GPS positioning device and an OBD interface of the test vehicle; step 2, collecting an original data set [ t, GPS, v, M ]; step 3, converting the original data set [ t, GPS, v, M ] into a sample data set [ r, v, M '], wherein r is a turning radius, and M' is torque required by steering; step 4, constructing a BP neural network model and training the BP neural network model; step 5, after the training of the neural network model is completed, verifying the BP neural network model by using a verification set; and 6, testing, recording and storing the test data. The invention can greatly improve the safety of the test of the over-bending capability of the three-level automatic driving vehicle.
Description
Technical Field
The invention relates to the technical field of automobile testing, in particular to a method for testing the bending passing capacity of a three-level automatic driving vehicle.
Background
At present, automatic driving tests aiming at vehicle bending capacity mainly adopt real vehicles and real vehicle speeds. As shown in fig. 6 (a), the autonomous vehicle needs to perform a vehicle cornering ability test on the autonomous algorithm and the corresponding actuator under a situation where the curve radius is 500 meters and the vehicle speed is 110 km/h. As the software, hardware, communication and other systems in the development stage are unstable, once a control algorithm is wrong or an execution mechanism is out of control, a tester does not have enough reaction time to process an emergency, and once the fault occurs, the personal safety of the tester is seriously threatened. In addition, due to the fact that the vehicle speed is high, all operations need to be extremely high in skill and specialty, and each testing task needs to be completed by a professional tester.
Therefore, it is necessary to develop a three-level auto-steering vehicle cornering ability test method.
Disclosure of Invention
The invention aims to provide a method for testing the over-bending capacity of a three-level automatic driving vehicle, which aims to solve the problems of high risk and high safety risk in a high-speed working condition test.
The invention relates to a method for testing the bending passing capacity of a three-level automatic driving vehicle, which comprises the following steps of:
step 1, mounting a driving robot, a GPS positioning device, a data synchronization card and an industrial personal computer on a test vehicle, and respectively connecting the data synchronization card with the driving robot, the GPS positioning device and an OBD interface of the test vehicle;
step 2, carrying out multiple tests on a vehicle driven by a driving robot under different curve radiuses r and different vehicle speeds v, and acquiring a timestamp t, a current track GPS, a current vehicle speed v and a current output steering torque M of the steering robot by using a data synchronization card to obtain an original data set [ t, GPS, v, M ];
step 3, converting the original data set [ t, GPS, v, M ] into a sample data set [ r, v, M '], wherein r is a turning radius, and M' is torque required by steering;
step 4, constructing a BP neural network model, wherein input parameters of an input layer are a turning radius r and a current vehicle speed v, and output parameters of an output layer are steering torque; dividing the sample data set into a training set and a verification set according to a proportion, importing the training set into a BP neural network model, and training the BP neural network model;
step 5, after the training of the neural network model is completed, verifying the BP neural network model by using a verification set, if the BP neural network model meets the test requirement, entering step 6, and if the BP neural network model does not meet the test requirement, entering step 2;
step 6, inputting the turning radius expected to be tested and the speed expected to be tested into an industrial personal computer, and connecting a CAN bus used for transmitting speed information on a test vehicle with the industrial personal computer provided with a BP neural network model; the experimental vehicle is driven at the vehicle speed of not less than 10km/h for testing, the difference value between the steering torque required by the expected test vehicle speed and the steering torque required at the current vehicle speed is calculated through a BP neural network model, the difference value is applied to a steering wheel of the experimental vehicle by a driving robot, and test data are recorded and stored.
Further, in step 3, the discrete data of the current trajectory GPS in each set of data sets is converted into a continuous curve, the curvature of the current point is obtained by differentiating every other as on the continuous curve and deriving the time, and all curvatures are averaged and converted into the turning radius r.
Further, in the step 3, the current vehicle speed v and the current output steering torque M of the steering robot in each group of data sets are converted into continuous curves, the continuous curves are sampled every as and averaged, and a corresponding relation set of the turning radius r, the current vehicle speed v and the torque M 'required by steering, namely a sample data set [ r, v, M' ], is obtained by combining the turning radius r.
Further, in the step 2, 182 tests are performed on the vehicle driven by the driving robot under the conditions that the radius r of the curve is 150m, 200m, 250m, 300m, 350m, 400m, 450m, 500m, 550m, 600m, 650m, 700m, 750m, 800m, the speed v of the curve is 60km/h, 65km/h, 70km/h, 75km/h, 80km/h, 85km/h, 90km/h, 95/h, 100km/h, 105km/h, 110km/h, 115km/h and 120 km/h.
The invention has the following advantages:
(1) the test of the high-speed working condition with the vehicle speed of 10 km/h-120 km/h and the turning radius of 150 m-800 m can be simulated by using the low-speed working condition with any vehicle speed of not less than 10km/h and turning radius of not less than 50 m. Because the speed of the test vehicle is low, even if software or hardware errors occur, a tester has enough reaction time and operates the vehicle again, and the safety of the test task is greatly improved.
(2) The test task can be carried out only by simply training ordinary testers and even developers, and the development efficiency can be greatly improved.
In conclusion, the method is simple, high in efficiency, reliable and feasible, can completely simulate the torque feedback performance of the vehicle in the high-speed curve under the working condition of the low-speed curve, and can effectively support the research, development and verification of the algorithm.
Drawings
FIG. 1 is a diagram of the system architecture of the present invention;
FIG. 2 is a flow chart of data processing in the present invention;
FIG. 3 is a model diagram of a BP neural network according to the present invention;
FIG. 4 is a flow chart of the working logic of the present invention;
FIG. 5 is a schematic diagram of the test according to the present invention;
FIG. 6 is a graph comparing a conventional test method with the method of the present invention;
fig. 7 is a BP neural network training flowchart.
Detailed Description
The present invention will be described in detail with reference to the accompanying drawings.
In this embodiment, a method for testing the bending capability of a three-level automatic driving vehicle includes the following steps:
step 1, installing a driving robot, a GPS positioning device, a data synchronization card and an industrial personal computer on a test vehicle, connecting the driving robot and the data synchronization card by using an Ethernet, connecting the GPS positioning device and the data synchronization card by using the Ethernet, and connecting the test vehicle and the data synchronization card by using a CAN bus and an OBD interface of the test vehicle, which is shown in figure 1.
In the embodiment, the driving robot adopts Vehico-CC800, has the functions of accurately controlling the speed of the vehicle and accurately controlling the torque of the steering wheel, and can be installed on a test vehicle.
The industrial personal computer adopts Oxford Technical Solutions Ltd-RT3000, can carry a BP neural network model, and can be directly connected with the data synchronization board card.
The data synchronization board card simultaneously supports synchronous acquisition of data and synchronous output of the data.
The GPS positioning device adopts a high-precision differential GPS positioning device.
And 2, carrying out multiple tests on a vehicle driven by the driving robot under different curve radiuses r and different vehicle speeds v, and acquiring the timestamp t, the current track GPS, the current vehicle speed v and the current output steering torque M of the steering robot by using a data synchronization card after the driving robot drives stably to obtain an original data set [ t, GPS, v, M ].
In this example, 182 tests were carried out using a driving robot to drive a vehicle at a curve radius r of 150m, 200m, 250m, 300m, 350m, 400m, 450m, 500m, 550m, 600m, 650m, 700m, 750m, 800m, a vehicle speed v of 60km/h, 65km/h, 70km/h, 75km/h, 80km/h, 85km/h, 90km/h, 95km/h, 100km/h, 105km/h, 110km/h, 115km/h, 120 km/h. The original data set [ t, GPS, v, M ] obtained by the test has 182 groups of data in total.
In this embodiment, the stable driving means that the vehicle can still run on the arc having the turning radius Am after the steering wheel is turned to a certain angle when the turning radius Am is used.
Step 3, converting the original data set [ t, GPS, v, M ] into a sample data set [ r, v, M '], referring to FIG. 2, wherein r is the turning radius, and M' is the torque required by steering; the method specifically comprises the following steps:
converting the discrete data of the current track GPS in each group of data set into a continuous curve by using a second-order retainer, differentiating every as (such as 0.5 s) on the continuous curve, then deriving the time to obtain the curvature of the current point, averaging all curvatures, and then converting the reciprocal into a turning radius r;
converting the current vehicle speed v and the current output steering torque M of the steering robot in each data set into continuous curves by using a second-order retainer, sampling every as (for example: 0.5 s) of the continuous curves, averaging, and combining the turning radius r to obtain a corresponding relation set of the turning radius r, the current vehicle speed v and the torque M 'required by steering, namely a sample data set [ r, v, M' ].
Step 4, as shown in fig. 3, constructing a BP neural network model, wherein the input parameters of an input layer are the turning radius r and the current vehicle speed v, and the output parameters of an output layer are the steering torque; the sample data set is proportionally divided into a training set and a verification set, as shown in fig. 4, the training set is imported into a BP neural network model, and the BP neural network model is trained.
In this embodiment, the sample data set includes 182 sets of data, and is divided into 172 training sets and 10 verification sets.
And 5, after the training of the neural network model is completed, verifying the BP neural network model by using a verification set, if the BP neural network model meets the test requirement, entering a step 6, and if the BP neural network model does not meet the test requirement, entering a step 2 until the BP neural network model meets the test requirement.
In this embodiment, the principle and process of training using the BP neural network model and using the steering torque corresponding to the speed and the steering radius as a sample are as follows:
the sample data set [ r, v, M '], where [ r, v ] is the input, [ M' ] is the expected sample output, let [ M "] be the actual output.
When the error signal k = [ M '] - [ M' ];
the excitation function in the BP neural network model adopts a logic function f (x) =1/(1+ exp (-in2)), wherein in2 is data input into a hidden layer by an input layer, and the value of the data is obtained by adding the sum of the products of vehicle speed and the current turning radius and corresponding deviation weight respectively and an offset value;
the weight w (n + 1) = w (n) +. aw (n) between the hidden layer J and the input layer P for the next iteration;
wherein: w (n + 1) is the nth weight before correction, w (n) is the nth weight before correction, and Δ w (n) is the corresponding correction amount; the iterative training process is shown in fig. 7.
And 6, inputting parameters (vehicle speed and turning radius) of the high-speed working condition to be tested in the overbending capability test procedure into an industrial personal computer, and then operating the system to finish the test of simulating the high-speed working condition with the vehicle speed of 10 km/h-120 km/h and the turning radius of 150 m-800 m by using any low-speed working condition with the vehicle speed of not less than 10km/h and the turning radius of not less than 50 m. The method specifically comprises the following steps:
as shown in fig. 5, the turning radius of the expected test and the vehicle speed of the expected test are input into an industrial personal computer, and a CAN bus for transmitting vehicle speed information on a test vehicle is connected with the industrial personal computer provided with a BP neural network model; the driver smoothly accelerated the test vehicle to 30 km/h and kept stable for 3s, see (b) in fig. 6; the difference value between the steering torque required by the expected test vehicle speed and the steering torque required by the current vehicle speed is calculated through a BP neural network model, the driving robot applies the difference value to a steering wheel of the test vehicle, see figure 7, the torque characteristic of the steering wheel of the test vehicle is the same as the torque characteristic of the test vehicle under the expected test vehicle speed and the expected test turning radius, and the test data are recorded and stored and can be used for subsequent tests.
Claims (4)
1. A three-level automatic driving vehicle over-bending capability test method is characterized by comprising the following steps:
step 1, mounting a driving robot, a GPS positioning device, a data synchronization card and an industrial personal computer on a test vehicle, and respectively connecting the data synchronization card with the driving robot, the GPS positioning device and an OBD interface of the test vehicle;
step 2, carrying out multiple tests on a vehicle driven by a driving robot under different curve radiuses r and different vehicle speeds v, and acquiring a timestamp t, a current track GPS, a current vehicle speed v and a current output steering torque M of the steering robot by using a data synchronization card to obtain an original data set [ t, GPS, v, M ];
step 3, converting the original data set [ t, GPS, v, M ] into a sample data set [ r, v, M '], wherein r is a turning radius, and M' is torque required by steering;
step 4, constructing a BP neural network model, wherein input parameters of an input layer are a turning radius r and a current vehicle speed v, and output parameters of an output layer are steering torque; dividing the sample data set into a training set and a verification set according to a proportion, importing the training set into a BP neural network model, and training the BP neural network model;
step 5, after the training of the neural network model is completed, verifying the BP neural network model by using a verification set, if the BP neural network model meets the test requirement, entering step 6, and if the BP neural network model does not meet the test requirement, entering step 2;
step 6, inputting the turning radius expected to be tested and the speed expected to be tested into an industrial personal computer, and connecting a CAN bus used for transmitting speed information on a test vehicle with the industrial personal computer provided with a BP neural network model; the experimental vehicle is driven at the vehicle speed of not less than 10km/h for testing, the difference value between the steering torque required by the expected test vehicle speed and the steering torque required at the current vehicle speed is calculated through a BP neural network model, the difference value is applied to a steering wheel of the experimental vehicle by a driving robot, and test data are recorded and stored.
2. The three-level autonomous vehicle cornering ability test method of claim 1, wherein: in the step 3, the discrete data of the current track GPS in each group of data sets is converted into a continuous curve, the curvature of the current point is obtained by differentiating the continuous curve every 0.5s and then deriving the time, and all curvatures are converted into the turning radius r after averaging.
3. The three-level autonomous vehicle cornering ability test method of claim 2, wherein: in the step 3, the current vehicle speed v and the current output steering torque M of the steering robot in each group of data sets are converted into continuous curves, the continuous curves are sampled every 0.5s and averaged, and a corresponding relation set of the turning radius r, the current vehicle speed v and the torque M 'required by steering, namely a sample data set [ r, v, M' ], is obtained by combining the turning radius r.
4. The three-level autonomous vehicle cornering ability test method of any of claims 1 to 3, wherein: in the step 2, the vehicle is driven by the driving robot to perform 182 times of tests under the conditions that the radius r of the curve is 150m, 200m, 250m, 300m, 350m, 400m, 450m, 500m, 550m, 600m, 650m, 700m, 750m and 800m, the vehicle speed v is 60km/h, 65km/h, 70km/h, 75km/h, 80km/h, 85km/h, 90km/h, 95km/h, 100km/h, 105km/h, 110km/h, 115km/h and 120 km/h.
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Application publication date: 20190709 Assignee: CHONGQING CHANGAN NEW ENERGY AUTOMOBILE TECHNOLOGY Co.,Ltd. Assignor: Chongqing Changan Automobile Co.,Ltd. Contract record no.: X2021500000014 Denomination of invention: Test method for cornering ability of three-level autonomous vehicles Granted publication date: 20201229 License type: Common License Record date: 20211014 |