CN110888417A - Real-time simulation and test method for control system of automatic driving truck - Google Patents
Real-time simulation and test method for control system of automatic driving truck Download PDFInfo
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- G05B23/00—Testing or monitoring of control systems or parts thereof
- G05B23/02—Electric testing or monitoring
- G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
- G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
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Abstract
The invention relates to a real-time simulation and test method for a control system of an automatic driving truck, which comprises the following steps: s100: establishing a vehicle dynamic model; s200: carrying out simulation test and real vehicle test by using a control algorithm; s300: perfecting an input scene library; s400: perfecting a vehicle dynamics model base; s500: designing a control algorithm; s600: carrying out automatic traversal test by using a Monte Carlo targeting method; s700: and carrying out real vehicle test. The method can verify the stability of the control algorithm in the life cycle of the whole vehicle, improve the stability and robustness of the control algorithm design and greatly shorten the development cycle of the algorithm, and can save a large amount of manpower and material resources and reduce the accident risk of real vehicle testing by transplanting most of the control algorithm design and verification on simulation.
Description
Technical Field
The invention belongs to the field of automatic driving of motor vehicles, and particularly relates to a real-time simulation and test method for a control system of an automatic driving truck.
Background
According to the grade division, the automatic driving is divided into five grades, the first grade is driving support and can realize horizontal (steering) or longitudinal (accelerator brake) control, the second grade is partial automatic driving and can realize horizontal and longitudinal control, the third grade is conditional automatic driving and can realize automatic driving under partial conditions, the fourth grade is high automatic driving, the automatic driving can realize automatic driving of most road sections, but a certain special scene needs to be managed by people for driving, and the fifth grade is completely unmanned driving, namely, manual management is not needed.
The design of advanced automatic driving more than L4 (four-level) and the current L2 (two-level) auxiliary driving make higher requirements on a control system, traditional auxiliary driving LKA (lane keeping assist), ACC (full speed adaptive cruise) design pays more attention to the comfort of passengers, using scenes are limited, factors such as the need for taking over by a driver are limited, the design of the control system does not need to consider too much requirements on stability and robustness, the boundary of some control system designs, such as emergency obstacle avoidance or wet and slippery road surfaces and other working conditions, are not allowed to enter automatic driving, but the advanced automatic driving does not have the assistance of the driver and needs to cover more comprehensive scenes and working conditions, the higher requirements on the robustness and reliability of the design of the vehicle control system are made, and the biggest problem brought to us is how to ensure that the design of the control system is stable under different working conditions, how to ensure that the control system is stable, how to avoid a vehicle out of control, etc., as the vehicle ages and wears.
The present invention has been made in view of the above circumstances.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a real-time simulation and test method for a control system of an automatic driving truck, which is characterized in that a dynamic model, an interference model and a noise model with high enough precision are established, so that a dynamic model library and an input scene library are further perfected on the basis of good consistency of real vehicle test and control simulation, and all dynamic boundaries are considered, so that the test control system can cover all scenes, and all factors to be considered are verified in the design simulation stage of a control algorithm.
The technical scheme of the invention is as follows: a real-time simulation and test method for a control system of an automatic driving truck comprises the following steps:
s100: establishing a vehicle dynamic model;
s200: carrying out simulation test and real vehicle test by using a control algorithm, repeatedly carrying out iterative optimization, judging whether the real vehicle test and the simulation test reach the performance consistency, and entering the next step if the real vehicle test and the simulation test reach the performance consistency; if not, returning to the previous step S100;
s300: an input scene library is perfected, and the scene library can cover enough driving scenes;
s400: perfecting a vehicle dynamics model base;
s500: designing a control algorithm, and designing the control algorithm and verifying the stability margin according to the dynamic model under the normal working condition;
s600: carrying out automatic traversal test by using a Monte Carlo targeting method, judging whether the analysis simulation result meets the requirements of robustness and stability of the control system, and if so, entering the next step; if not, returning to the step S500;
s700: and (5) carrying out real vehicle test, finishing the control algorithm design if the design requirements are met, and returning to the step S500 to carry out the control algorithm design of the next round if the design requirements are not met.
Further, in step S100, an error interference model and a noise model are established. The simulation process is more consistent with the actual scene.
Further, the scene library of step S300 includes the following scenes:
1) different acceleration-cruise-deceleration scenarios;
2) following scenes at different speeds;
3) an AEB scenario;
4) a car following scene;
5) the method comprises the following steps of (1) leading a vehicle to cut into a scene in an emergency;
6) scenes of curves with different turning radii at different speeds;
7) and (5) emergency obstacle avoidance scenes.
Further, the dynamical model library in step S400 includes:
1) a dynamic model under a normal working condition;
2) dynamic models of wet and slippery road surfaces with different numbers of attachment systems;
3) the boundaries of tire dynamics models caused by tire wear, etc. under different loads;
4) the performance boundaries of a brake, a steering wheel linear control system, an engine power model and the like in the whole life cycle of the vehicle.
The invention has the following beneficial effects:
1. the robustness of the control algorithm is fully verified, and the stability of the control system design is improved.
2. The research and development period can be shortened, and the cost is saved.
3.A large amount of real vehicle tests are saved, the accident rate of the real vehicle tests is reduced, and a large amount of manpower and material resources are saved.
4. Most algorithm designs are not influenced by test environment and regions, and iteration efficiency is improved.
Drawings
FIG. 1 is a schematic diagram of a method for real-time simulation and testing of a control system for an autonomous truck in accordance with the present invention.
FIG. 2 is a workflow diagram of one embodiment of a method for real-time simulation and testing of a control system of an autonomous truck in accordance with the present invention.
FIG. 3 is a simulation scenario set inclusion diagram of a method for real-time simulation and testing of a control system of an autonomous driving truck in accordance with the present invention.
FIG. 4 is a vehicle model containment map of a method for real-time simulation and testing of an autonomous truck's control system in accordance with the present invention.
Detailed Description
The present invention will be further described with reference to the accompanying fig. 1-4, wherein the following embodiments are described by way of illustration and not by way of limitation.
The working principle of the invention is shown in figure 1 and mainly divided into a plurality of parallel parts:
1. controlling an input scene library;
2. a control algorithm node;
3.a dynamic node;
4. monte Carlo automatic test evaluation tool.
The invention mainly focuses on how to more perfectly and efficiently design a vehicle control system and verify the stability and robustness, firstly, a more accurate vehicle dynamics model, an input error interference model and an output error noise model are established, a simulation platform is verified and perfected, and the simulation performance with better consistency with the real vehicle test is achieved, then, on the basis, a scene library for controlling an input scene library and a scene library for the vehicle dynamics model are perfected and controlled to sufficiently cover the change of model parameters and the scenes which can be experienced in the life cycle of the whole vehicle, and finally, an automatic test and evaluation tool, namely a Monte Carlo targeting method is utilized to automatically test a control algorithm covering all scene libraries and dynamics libraries, and whether the stability and robustness of the control algorithm meet the design requirements is evaluated.
Referring to fig. 2, there is shown an embodiment of a real-time simulation and testing method for a control system of an autonomous truck according to the present invention, the real-time simulation and testing method comprising the steps of:
s100: establishing a vehicle dynamic model; vehicle dynamics under normal conditions are modeled. In order to make the simulation more accurate, an error disturbance model at the input and a noise model at the output are added.
S200: carrying out simulation test and real vehicle test by using a control algorithm, repeatedly carrying out iterative optimization, judging whether the real vehicle test and the simulation test reach the performance consistency, and entering the next step if the real vehicle test and the simulation test reach the performance consistency; if not, returning to the previous step S100, and reestablishing the vehicle dynamics model, the error disturbance model and the noise model.
S300: and the input scene library is perfected, and the scene library can cover enough driving scenes. The scope of the scene library is shown in fig. 3, and the simulation scene set generally includes scenes of a real vehicle, including but not limited to weather, conditions, and functions, such as:
1. different acceleration-cruise-deceleration scenarios;
2. following scenes at different speeds;
AEB (automatic Emergency braking System) scenario;
4. a car following scene;
5. the method comprises the following steps of (1) leading a vehicle to cut into a scene in an emergency;
6. scenes of curves with different turning radii at different speeds;
7. and (5) emergency obstacle avoidance scenes.
S400: and (5) perfecting a vehicle dynamics model library. The range of the model library is shown in fig. 4, and a simulation scene set generally includes scenes of parameter changes of the whole life cycle of the real vehicle under different working conditions, and in general, the vehicle dynamics model library includes the following conditions:
1. a dynamic model under a normal working condition;
2. dynamic models of wet and slippery road surfaces with different numbers of attachment systems;
3. the boundaries of tire dynamics models caused by tire wear, etc. under different loads;
4. the performance boundaries of a brake, a steering wheel linear control system, an engine power model and the like in the whole life cycle of the vehicle.
S500: and designing a control algorithm, and designing the control algorithm and verifying the stability margin according to the dynamic model under the normal working condition.
S600: carrying out automatic traversal test by using a Monte Carlo targeting method, judging whether the analysis simulation result meets the requirements of robustness and stability of the control system, and if so, entering the next step; if not, return to step S500. And (3) carrying out automatic simulation and verification by using a control algorithm aiming at various combinations of all scenes and dynamic models by using the idea of Monte Carlo targeting.
S700: and (5) carrying out real vehicle test, finishing the control algorithm design if the design requirements are met, and returning to the step S500 to carry out the control algorithm design of the next round if the design requirements are not met.
The real-time simulation and test method for the control system of the automatic driving truck can verify the stability of the control algorithm in the life cycle of the whole vehicle, improve the stability and robustness of the control algorithm design, greatly shorten the research and development cycle of the algorithm, transplant most of the design and verification of the control algorithm to be completed on simulation, save a large amount of manpower and material resources and reduce the accident risk of real vehicle test.
The above-described embodiments are intended to illustrate rather than to limit the invention, and any modifications and variations of the present invention are within the spirit of the invention and the scope of the claims.
Claims (4)
1. A real-time simulation and test method for a control system of an automatic driving truck is characterized by comprising the following steps:
s100: establishing a vehicle dynamic model;
s200: carrying out simulation test and real vehicle test by using a control algorithm, repeatedly carrying out iterative optimization, judging whether the real vehicle test and the simulation test reach the performance consistency, and if so, entering the next step; if not, returning to the previous step S100;
s300: an input scene library is perfected, and the scene library can cover enough driving scenes;
s400: perfecting a vehicle dynamics model base;
s500: designing a control algorithm, and designing the control algorithm and verifying the stability margin according to the dynamic model under the normal working condition;
s600: carrying out automatic traversal test by using a Monte Carlo targeting method, judging whether the analysis simulation result meets the requirements of robustness and stability of the control system, and if so, entering the next step; if not, returning to the step S500;
s700: and (5) carrying out real vehicle test, finishing the control algorithm design if the design requirements are met, and returning to the step S500 to carry out the control algorithm design of the next round if the design requirements are not met.
2. The method for real-time simulation and testing of a control system of an autonomous truck as recited in claim 1, further comprising establishing an error disturbance model and a noise model in step S100.
3. The method for real-time simulation and testing of a control system of an autonomous truck as claimed in claim 1 or 2, characterized in that the library of scenarios of step S300 comprises the following scenarios:
1) different acceleration-cruise-deceleration scenarios;
2) following scenes at different speeds;
3) an AEB scenario;
4) a car following scene;
5) the method comprises the following steps of (1) leading a vehicle to cut into a scene in an emergency;
6) scenes of curves with different turning radii at different speeds;
7) and (5) emergency obstacle avoidance scenes.
4. The method for real-time simulation and testing of a control system of an autonomous truck as claimed in any of claims 1 to 3, characterized in that the library of dynamical models in step S400 comprises:
1) a dynamic model under a normal working condition;
2) dynamic models of wet and slippery road surfaces with different numbers of attachment systems;
3) the boundaries of tire dynamics models caused by tire wear, etc. under different loads;
4) the performance boundaries of a brake, a steering wheel linear control system, an engine power model and the like in the whole life cycle of the vehicle.
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| CN201911293262.0A CN110888417A (en) | 2019-12-16 | 2019-12-16 | Real-time simulation and test method for control system of automatic driving truck |
| PCT/CN2020/098279 WO2021120575A1 (en) | 2019-12-16 | 2020-06-24 | Real-time simulation and test method for control system of autonomous driving vehicle |
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| CN112925221A (en) * | 2021-01-20 | 2021-06-08 | 重庆长安汽车股份有限公司 | Auxiliary driving closed loop test method based on data reinjection |
| WO2021120575A1 (en) * | 2019-12-16 | 2021-06-24 | Suzhou Zhijia Science & Technologies Co., Ltd. | Real-time simulation and test method for control system of autonomous driving vehicle |
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Application publication date: 20200317 |