CN114579088A - Unmanned algorithm development method based on data mining and test closed loop - Google Patents
Unmanned algorithm development method based on data mining and test closed loop Download PDFInfo
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Abstract
The invention discloses an unmanned algorithm development method based on data mining and test closed loops, which comprises the following steps: (1) the method comprises the following steps of requirement analysis, software design, software development, software verification, software integration test, software release and mileage test; (2) continuously acquiring various data generated in the testing process during software integration testing and mileage testing; (3) digging problem data from the collected data to form a problem library; (4) and performing secondary development on the software based on the problem library to repair the problem and form a development verification closed loop. The invention adds a development verification closed loop and a demand closed loop on the basis of a conventional development mode, and iteratively upgrades the algorithm through a question library and a scene library respectively, thereby improving the reliability and the upgrading efficiency of the algorithm.
Description
Technical Field
The invention relates to an unmanned algorithm development method based on data mining and test closed loops, and belongs to the technical field of automatic driving.
Background
The unmanned algorithm has high complexity, and the general development adopts a V flow to develop and verify according to the requirements of projects. However, for the high-level unmanned algorithm, in order to reduce the development cost, a development mode of platform software and a mode of platform software plus project adaptation are considered. The development of platform software is generally test-driven, and problems found in drive tests can be input as development requirements, so that a closed loop of development and test is achieved.
At present, a pure manual mode is used for developing and testing a closed loop, and a large amount of road test mileage and manual problem judgment are relied on. This approach is highly dependent on the experience of the tester, and if the proportion of problems is found to be reduced, more testing mileage is required to make up, and the overall process is less efficient. Testing is often the bottleneck in research and development when developing new unmanned algorithms.
Disclosure of Invention
The invention aims to solve the technical problem that the existing unmanned driving algorithm is relatively inefficient in developing and testing closed loops in a pure manual mode.
In order to achieve the aim, the invention provides an unmanned algorithm development method based on data mining and test closed loop, which comprises the following steps: (1) the method comprises the following steps of requirement analysis, software design, software development, software verification, software integration test, software release and mileage test; (2) continuously collecting various data generated in the testing process during the software integration testing and the mileage testing; (3) digging problem data from the collected data to form a problem library; (4) and performing secondary development on the software based on the problem library to repair the problem and form a development verification closed loop.
Further, the step (4) is followed by the following steps: (5) carrying out scene extraction from the problem library to form a scene library; (6) mining optimizable points of an algorithm from the scene library; (7) and providing the optimizable point to a demand analysis side to form a demand closed loop.
Further, in the step (5), valuable scene data is further extracted from the full-scale library formed by the collected data and added into the scene library.
Further, the scene refining process in the step (5) is as follows: the problem is first attributed and then several typical scenarios are selected to join the scenario library for the same reason problem.
Further, the typical scene includes at least a problem phenomenon, a road category, a weather condition, a lighting condition, and a time.
Further, the problem phenomena include sudden braking, line pressing driving, unexpected sudden braking and vehicle snaking, the road categories include expressways, urban roads and ramps, the weather conditions include sunny days, cloudy days, rainy days and snowy days, and the lighting conditions include daytime and nighttime.
Further, the optimizable point in step (6) is based on the cause of formation of each problem.
Further, the mining mode of the problem data in the step (3) comprises manual problem mining, automatic problem mining and comparison data mining.
Further, the manual problem mining includes intercepting problem data in a tagged manner.
Further, the automated problem mining includes data rationality checking and comparison against data.
Compared with the prior art, the invention has the beneficial effects that:
1. the data can generate higher value, and the automatic mining algorithm can fully utilize the large calculation power of the data center to achieve the coverage rate of the test problems which cannot be achieved by manpower, namely, a plurality of problems which cannot be found by the manpower can be automatically mined.
2. A development verification closed loop and a demand closed loop are added on the basis of a conventional development mode, and the algorithm is iteratively upgraded through a problem library and a scene library respectively, so that the reliability and the upgrading efficiency of the algorithm are improved.
3. The scene library can be continuously accumulated and used as a research and development support of a higher-level iterative algorithm.
Drawings
FIG. 1 is a flow chart of one embodiment of the present invention;
FIG. 2 is a functional block diagram of automated problem mining in one embodiment of the present invention.
Detailed Description
The technical solution of the present invention is further explained with reference to the accompanying drawings and the specific embodiments.
As shown in FIG. 1, one embodiment of the present invention is an unmanned algorithm development method based on data mining and test closed loop, comprising the following steps: (1) the method comprises the following steps of requirement analysis, software design, software development, software verification, software integration test, software release and mileage test; (2) continuously acquiring various data generated in the testing process during software integration testing and mileage testing; (3) digging out problem data from the collected data to form a problem library; (4) performing secondary development on software based on the problem library to repair problems and form a development verification closed loop; (5) carrying out scene extraction from the problem library to form a scene library; (6) mining optimizable points of an algorithm from the scene library; (7) and providing the optimizable point to a demand analysis side to form a demand closed loop. The mileage test in the step (2) comprises an unmanned mileage test and a manned mileage test, wherein the manned mileage test adopts a shadow mode to collect data.
In one embodiment of the invention, in the step (5), valuable scene data is further extracted from a full-volume library formed by the collected full-volume data and added into the scene library. For example, the sensor data and the algorithm intermediate result data during the drive test for 8 hours and 8 hours are recorded as the full data.
In an embodiment of the present invention, the scene refinement in step (5) is performed by: the problem is first attributed and then several typical scenarios are selected to join the scenario library for the same reason problem.
In one embodiment of the invention, the typical scenario includes at least the following dimensions: problem phenomena, road category, weather conditions, lighting conditions and time.
In one embodiment of the invention, said problematic phenomena include sudden braking, traffic congestion, unexpected sudden braking and vehicle hunting, said road categories include expressways, urban roads and ramps, said weather conditions include sunny, cloudy, rainy and snowy days, and said lighting conditions include daytime and nighttime. The sudden braking refers to normal braking when an obstacle exists in the front, and unexpected sudden braking is caused by mistakenly recognizing the obstacle in the front.
In one embodiment of the present invention, the optimizable point in step (6) is based on the cause of formation of each problem. For example, unexpected hard braking is caused by a false recognition of an obstacle in front, possibly because the camera range is not accurate for recognizing a distant object in the near, so that the camera range recognition algorithm needs to be optimized for the scene, and the algorithm needs to be optimized after attribution for each problem.
In one embodiment of the present invention, the mining manner of the problem data in the step (3) includes manual problem mining, automatic problem mining and comparison data mining. The automatic data mining is to check the algorithm result data, and the comparison data mining is to compare the unmanned algorithm result data with the manned data.
In one embodiment of the invention, the manual problem mining includes intercepting problem data in a tagged manner. For example, when it is found that an automatically driven vehicle suddenly brakes suddenly without accident, or suddenly does not run in the middle of a road and deviates to the left or right, or the vehicle snakes, a tester can record the time and specific phenomena of the problem on the acquisition software, namely, the time and the specific phenomena are marked, so that the vehicle can be quickly found for further analysis in the following process. The problem data obtained by labeling is intercepted from the full data.
In one embodiment of the invention, the automated problem mining includes data reasonableness checking and comparison against data. Wherein the comparison data comprises truth data and driver behavior data. When the truth value data is collected, besides the vehicle data, a set of GPS equipment is installed on the target vehicle as an obstacle, and information such as the position, the speed, the acceleration and the like of the target vehicle is also recorded as the truth value. The driver behavior data comprises driving behaviors such as stepping on an accelerator, stepping on a brake and turning a steering wheel.
As shown in fig. 2, the data sources for automated problem mining include environmental raw data, algorithm result data, and control data. Sources of the comparison data include collected truth data, driver behavior data, manual annotations based on images and point clouds. There are different mining modes for different data sources.
And (3) performing target recognition and semantic segmentation on the image and point cloud data through a deep learning model by deep learning detection. Firstly, a data segment with contrast data or labeled data is selected, the difference between a target detection result and the contrast data is compared, and a part with larger difference is put into a question bank. And adding the subsequent model into a scene in the problem library for optimization, and reasoning the environmental original data corresponding to the part of data by using the new model after optimization until the difference with the control group is small enough.
The sensor comparison detection mainly aims at perception fusion, and potential problem points are found by comparing the difference of recognition results among different sensors. Different sensors have different advantages and disadvantages for the identification of different information, for example, the laser radar range finding is accurate, and the camera judges the category more accurately. This comparison may optimize the sensing algorithm performance of other sensors by more accurate sensors.
The rationality detection finds obvious and unreasonable logics for the output results of all algorithm modules, such as no output for a long time, jump of position or speed, jump of obstacles and the like. For example, an obstacle on the right before 100ms suddenly runs to the left after 100ms, which belongs to the obstacle jump phenomenon.
The problematic parts are found from the calculation power through the data center. The check rule is manually made and gradually supplemented and perfected along with the iteration of the algorithm.
The driver contrast detection mainly faces the situation that the shadow mode records the algorithm control information and the driver control information at the same time, and finds out the behavior difference between the two. The shadow mode means that the driver controls the vehicle, but the algorithm is also running synchronously (only the result is calculated, the vehicle is not controlled), and the operation behavior of the driver can be obtained and compared with the control decision of the algorithm. And finding out the possibly unreasonable algorithm control behavior by comparing the acceleration and deceleration time of the driver with the braking time, and then confirming whether the problem is a planning problem or a control problem through the verification of the planning.
All the problem mining modes need strong calculation support, and manual participation is not needed between data acquisition and problem library generation.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. An unmanned algorithm development method based on data mining and test closed loop is characterized by comprising the following steps:
(1) the method comprises the following steps of requirement analysis, software design, software development, software verification, software integration test, software release and mileage test;
(2) continuously acquiring various data generated in the testing process during software integration testing and mileage testing;
(3) digging problem data from the collected data to form a problem library;
(4) and performing secondary development on the software based on the problem library to repair the problem and form a development verification closed loop.
2. The method for developing unmanned algorithm based on data mining and test closed loop according to claim 1, wherein the step (4) is further followed by the steps of:
(5) carrying out scene extraction from the problem library to form a scene library;
(6) mining optimizable points of an algorithm from the scene library;
(7) and providing the optimizable point to a demand analysis side to form a demand closed loop.
3. The closed-loop data mining and testing-based unmanned algorithm development method according to claim 2, wherein in the step (5), valuable scene data is further extracted from a full-scale library formed by collected data and added into the scene library.
4. The method for developing unmanned algorithm based on data mining and test closed loop according to claim 2, wherein the scene refinement process in step (5) is as follows: the problem is first attributed and then several typical scenarios are selected to join the scenario library for the same reason problem.
5. The method of claim 4, wherein the typical scenario includes at least problem phenomena, road category, weather conditions, lighting conditions and time.
6. The method of claim 5, wherein the problem phenomena include hard braking, line-pressing driving, unexpected hard braking and vehicle hunting, the road categories include expressways, urban roads and ramps, the weather conditions include sunny days, cloudy days, rainy days and snowy days, and the lighting conditions include day and night.
7. The method for developing unmanned aerial vehicle algorithm based on data mining and test closed loop of claim 2, wherein the optimizable point in step (6) is based on the cause of formation of each problem.
8. The method for developing unmanned aerial vehicle algorithm based on data mining and test closed loop of claim 1, wherein the mining manner of the problem data in the step (3) comprises manual problem mining, automatic problem mining and comparison data mining.
9. The method of claim 8, wherein the manual problem mining comprises intercepting problem data in a tagged manner.
10. The method of claim 8, wherein the automated problem mining comprises data plausibility checking and comparison against data.
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