CN109446690B - LDW function detection method - Google Patents
LDW function detection method Download PDFInfo
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- CN109446690B CN109446690B CN201811321032.6A CN201811321032A CN109446690B CN 109446690 B CN109446690 B CN 109446690B CN 201811321032 A CN201811321032 A CN 201811321032A CN 109446690 B CN109446690 B CN 109446690B
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- 238000001514 detection method Methods 0.000 title claims abstract description 23
- 238000012360 testing method Methods 0.000 claims abstract description 20
- 230000003068 static effect Effects 0.000 claims abstract description 15
- 230000007613 environmental effect Effects 0.000 claims abstract description 3
- 238000000034 method Methods 0.000 claims description 4
- 239000000725 suspension Substances 0.000 claims description 4
- 238000005562 fading Methods 0.000 claims description 3
- 230000001133 acceleration Effects 0.000 claims description 2
- 230000005540 biological transmission Effects 0.000 claims 1
- 238000011161 development Methods 0.000 abstract description 10
- 238000004088 simulation Methods 0.000 description 6
- 238000012795 verification Methods 0.000 description 4
- 208000003443 Unconsciousness Diseases 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 238000002845 discoloration Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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Abstract
The invention discloses a LDW function detection method, which comprises the following steps: establishing static environment data, a host vehicle model, dynamic environment data, a host vehicle motion trail and sensor data; establishing a plurality of test scenes according to the static environment data, the main vehicle model, the dynamic environment data, the main vehicle motion trail and the sensor data; the LDW function detection is carried out in any test scene, which comprises the steps of downloading an actual road route on an open source map and importing the actual road route into simulation software; in the simulation software, changing the actual road route into two-way three lanes; intercepting environmental information in a preset recorded road video; and importing a vehicle model to be tested, and performing LDW function detection. The LDW function detection method provided by the invention can simulate the working conditions which are difficult to meet in practice, such as limit working conditions, bad weather and the like, and dangerous driving is avoided. In the same way, the problems can be found in advance in the early development stage, and the development period is shortened.
Description
Technical Field
The invention relates to the technical field of intelligent transportation, in particular to an LDW function detection method.
Background
The LDW function is short for lane departure warning system, and is an advanced safety function configuration related to intelligent driving. The vehicle has the main effects that when the vehicle deviates from a lane in the driving process in an unconscious way, the warning reminding is carried out on the driver, and accidents caused by the lack of concentration or fatigue driving of the driver are prevented.
Safe driving is a trend of future development, and in order to ensure the safety of functions, multiple rounds of test verification are required in the development process. The verification mode includes software simulation test, hardware-in-loop test, site test and road verification. The software simulation test can simulate severe working conditions such as storm weather and the like, problems can be found in the early development stage, and the functional safety can be ensured by targeted verification in the later development stage.
In simulation software, various elements such as roads, traffic signals, identification lines, pedestrians, vehicles and the like are established to form a scene of LDW function simulation analysis, and a dynamics model of a host vehicle is established in the software, wherein the dynamics model comprises performance parameters of chassis systems such as steering, braking, engines, suspensions, tires and the like, so that the performance of the host vehicle is effectively simulated, and after the LDW scene modeling is completed, the simulation analysis is carried out by combining a control strategy algorithm.
The software performs ring simulation analysis, the established scene is an ideal scene, and roads, traffic flows, environments and main vehicle control are all designed and planned by engineers. The simulation analysis of the LDW function cannot be a conventional simulation analysis, unlike the CAE analysis software, which inputs parameters, builds a model, and finally calculates the results. The LDW function needs to detect false alarm missing report of its function, and it is not just that modeling simulation is successful to pass, which indicates that the LDW function completes detection, which needs more complex scene test cases.
Disclosure of Invention
The invention aims to provide an LDW function detection method, which aims to solve the problems in the prior art and improve the test precision.
The invention provides a LDW function detection method, which comprises the following steps:
establishing static environment data, a host vehicle model, dynamic environment data, a host vehicle motion trail and sensor data;
establishing a plurality of test scenes according to the static environment data, the main vehicle model, the dynamic environment data, the main vehicle motion trail and the sensor data;
performing LDW function detection in any test scenario, comprising:
downloading an actual road route on an open source map, and importing the actual road route into simulation software; in the simulation software, changing the actual road route into two-way three lanes;
intercepting environmental information in a preset recorded road video;
and importing a vehicle model to be tested, and performing LDW function detection.
Preferably, the static environment data includes road data, weather data, tree data, house data, and traffic sign signal data.
Preferably, building the host vehicle model includes inputting performance parameters of the steering system, braking system, suspension system, engine, driveline, and tires.
Preferably, the dynamic environment parameters include pedestrian data, electric vehicle data, and incoming and outgoing vehicle data.
Preferably, establishing the host vehicle motion profile includes setting a lateral velocity of the host vehicle away from the lane line.
Preferably, the lateral velocity is between 0.1m/s and 0.8m/s.
Preferably, the sensor data includes radar data and camera data.
According to the LDW function detection method provided by the invention, the static environment data, the main vehicle model, the dynamic environment data, the main vehicle motion trail and the sensor data are established, a plurality of test scenes are established according to the static environment data, the main vehicle model, the dynamic environment data, the main vehicle motion trail and the sensor data, and the LDW function detection is carried out in any test scene, so that the working conditions which are difficult to meet in practice such as limit working conditions, bad weather and the like can be simulated, and dangerous driving is avoided. In the same way, the problems can be found in advance in the early development stage, and the development period is shortened.
Drawings
Fig. 1 is a flowchart of an LDW function detection method according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
As shown in fig. 1, an embodiment of the present invention provides a method for detecting an LDW function, including:
s100, static environment data, a main vehicle model, dynamic environment data, a main vehicle motion track and sensor data are established.
Wherein the static environment data includes road data, weather data, tree data, house data, and traffic sign signal data. The road data are established by setting the number of lanes, lane lines, identification lines, fences and the like according to the actual road conditions.
The host vehicle model is built to include performance parameters of the input steering system, brake system, suspension system, engine, driveline, and tires. And the dynamic performance of the vehicle is verified, and the accuracy of the vehicle is verified through the acceleration and deceleration working conditions.
The dynamic environment parameters comprise pedestrian data, electric vehicle data and vehicle data, and the environments are key to causing interference factors of the LDW alarm detection function.
Establishing the host vehicle motion profile includes setting a lateral velocity of the host vehicle away from the lane line.
Preferably, the lateral velocity is between 0.1m/s and 0.8m/s.
The sensor data includes radar data and camera data.
And S200, establishing a plurality of test scenes according to the static environment data, the main vehicle model, the dynamic environment data, the motion trail of the main vehicle and the sensor data.
S300, performing LDW function detection in any test scene. And establishing an automatic test program to complete test analysis of all scenes.
Wherein S300 includes:
s310, downloading an actual road route on an open source map and importing the actual road route into simulation software; in the simulation software, the actual road route is changed into two-way three lanes.
S320, intercepting environment information in a preset recorded road video.
The traffic flow, the people flow and the road sign of the set road section are taken and are set in simulation software in the same way, and then the damage, the fading percentage and the like of the lane line are set according to the actual road condition. If the discoloration and the breakage are all found, 50% of each is set.
S330, importing a vehicle model to be tested, and performing LDW function detection.
According to the LDW function detection method provided by the embodiment of the invention, the static environment data, the main vehicle model, the dynamic environment data, the main vehicle motion trail and the sensor data are established, a plurality of test scenes are established according to the static environment data, the main vehicle model, the dynamic environment data, the main vehicle motion trail and the sensor data, and the LDW function detection is carried out in any test scene, so that the working conditions which are difficult to meet in practice such as limit working conditions, bad weather and the like can be simulated, and dangerous driving is avoided. In the same way, the problems can be found in advance in the early development stage, and the development period is shortened.
The foregoing detailed description of the preferred embodiments of the present invention will be presented in terms of a detailed description of the preferred embodiments of the invention, but the invention is not limited to the details of the preferred embodiments of the invention, and is intended to cover all modifications and equivalent arrangements of the present invention which are within the spirit and scope of the present invention as defined by the appended drawings.
Claims (1)
1. A method for detecting LDW functions, comprising:
establishing static environment data, a host vehicle model, dynamic environment data, a host vehicle motion trail and sensor data;
establishing a plurality of test scenes according to the static environment data, the main vehicle model, the dynamic environment data, the main vehicle motion trail and the sensor data;
the static environment data comprises road data, weather data, tree data, house data and traffic sign signal data; setting the number of lanes, lane lines, identification lines and fences according to the actual road conditions;
establishing a host vehicle model including inputting performance parameters of a steering system, a braking system, a suspension system, an engine, a transmission system, and tires; verifying the dynamic performance of the vehicle, and verifying the accuracy of the vehicle through the acceleration and deceleration working conditions;
the dynamic environment parameters comprise pedestrian data, electric vehicle data and coming and going vehicle data; the pedestrian data, the electric vehicle data and the coming and going vehicle data are factors causing interference of the LDW alarm detection function;
establishing a motion trail of the host vehicle includes setting a lateral speed of the host vehicle away from the lane line; the lateral velocity is 0.1m/s-0.8m/s;
the sensor data comprises radar data and camera data;
performing LDW function detection in any test scenario, comprising:
downloading an actual road route on an open source map, and importing the actual road route into simulation software; in the simulation software, changing the actual road route into two-way three lanes;
intercepting environmental information in a preset recorded road video; taking traffic flow, people flow and road signs of a set road section, performing the same setting in simulation software, and setting the damage and fading percentages of the lane lines according to actual road conditions; if the fading and the damage are all the same, setting 50% respectively;
and importing a vehicle model to be tested, and performing LDW function detection.
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| CN201811321032.6A CN109446690B (en) | 2018-11-07 | 2018-11-07 | LDW function detection method |
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| CN201811321032.6A CN109446690B (en) | 2018-11-07 | 2018-11-07 | LDW function detection method |
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| CN109446690A CN109446690A (en) | 2019-03-08 |
| CN109446690B true CN109446690B (en) | 2024-01-23 |
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| US20230159033A1 (en) * | 2021-11-19 | 2023-05-25 | Motional Ad Llc | High fidelity data-driven multi-modal simulation |
| CN114047742B (en) * | 2022-01-13 | 2022-04-15 | 杭州宏景智驾科技有限公司 | Intelligent piloting advanced driver assistance hardware in-loop test system and method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102011081892A1 (en) * | 2011-08-31 | 2013-02-28 | Robert Bosch Gmbh | Method for lane monitoring and lane monitoring system for a vehicle |
| CN108737955A (en) * | 2018-04-28 | 2018-11-02 | 交通运输部公路科学研究所 | LDW/LKA test evaluation system and methods based on virtual lane line |
| CN108763733A (en) * | 2018-05-24 | 2018-11-06 | 北京汽车集团有限公司 | driving simulation test method, device and system |
-
2018
- 2018-11-07 CN CN201811321032.6A patent/CN109446690B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102011081892A1 (en) * | 2011-08-31 | 2013-02-28 | Robert Bosch Gmbh | Method for lane monitoring and lane monitoring system for a vehicle |
| CN108737955A (en) * | 2018-04-28 | 2018-11-02 | 交通运输部公路科学研究所 | LDW/LKA test evaluation system and methods based on virtual lane line |
| CN108763733A (en) * | 2018-05-24 | 2018-11-06 | 北京汽车集团有限公司 | driving simulation test method, device and system |
Non-Patent Citations (2)
| Title |
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| 智能车场景建模及仿真;郑磊等;《汽车技术》;20181024(第10期);全文 * |
| 用于车辆电子控制***开发的仿真环境;管欣等;《科学技术与工程》;20100528(第15期);全文 * |
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