CN114326443A - MIL simulation test method and system for ADAS and readable storage medium - Google Patents
MIL simulation test method and system for ADAS and readable storage medium Download PDFInfo
- Publication number
- CN114326443A CN114326443A CN202210044955.1A CN202210044955A CN114326443A CN 114326443 A CN114326443 A CN 114326443A CN 202210044955 A CN202210044955 A CN 202210044955A CN 114326443 A CN114326443 A CN 114326443A
- Authority
- CN
- China
- Prior art keywords
- simulation
- model
- vehicle
- adas
- mil
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Traffic Control Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention particularly relates to an MIL simulation test method and system for ADAS and a readable storage medium. The method comprises the following steps: constructing a simulation scene model and acquiring simulation scene information; constructing a dynamic model for simulating the response of the vehicle; the ADAS algorithm to be tested is used as a tested model to be respectively connected with the simulation scene model and the dynamic model, and the simulation scene model is connected with the dynamic model to form a model interface closed loop; generating a driving control command, inputting the driving control command into a dynamic model, and generating corresponding vehicle state parameters; and inputting the simulation scene information and the whole vehicle state parameters into a tested model, generating a corresponding control request instruction through the tested model and inputting the control request instruction into a dynamic model so as to realize the simulation test of the ADAS algorithm to be tested. The invention also correspondingly discloses a simulation test system and a readable storage medium. The MIL simulation test method can be used for quickly verifying the ADAS algorithm in the early development stage and quickly performing iterative test after the algorithm is corrected.
Description
Technical Field
The invention relates to the technical field of ADAS simulation test, in particular to an MIL simulation test method and system for ADAS and a readable storage medium.
Background
With the development of the intelligent internet automobile industry, an Advanced Driving Assistance System (ADAS) gradually becomes a standard configuration of an automobile driving system, and is a hot spot of automobile industry research in recent years. The verification of the collision avoidance capability of the vehicle under dangerous working conditions in the driving process is the core of the ADAS active safety field test. The dangerous working conditions mainly comprise a large number of scenes such as severe weather environments, complex road traffic conditions, typical traffic accidents and the like, and are a key part for verifying the safe driving control strategy in the automatic driving automobile testing process.
The existing ADAS ECU test system does not use an automatic mode for testing, and the test efficiency is low. Therefore, Chinese patent with publication number CN110412374A discloses an ADAS HIL test system based on multiple sensors, which comprises an ADAS scene simulation host, a video signal injection module, an ultrasonic signal injection module, an ADAS ECU controller and a real-time industrial controller. The test system is based on an ADAS hardware-in-loop test system of multiple sensors, tests of multiple ADAS ECUs can be performed in an in-loop simulation mode, supported simulation sensors comprise cameras, millimeter wave radars, ultrasonic radars and laser radars, and sensor models in the test system can be configured in an ADAS scene simulation host to simulate sensors of multiple different types and parameters.
The ADAS HIL test system in the prior art enables various scene test results of the ADAS ECU to be reflected on the scene simulation host, and the test process uses automatic test, so that the test efficiency is high. The HIL test (in-loop test of controller hardware) has the advantage that the overall behavior of the controller, including bottom layer software, application layer functions, communication drivers, etc., can be verified more completely, but the test method has the disadvantage of being too dependent on the maturity of the controller. However, for the development of the ADAS algorithm, it is more desirable to perform the test verification work of the ADAS algorithm, i.e. the MIL test (model in loop test), before the hardware of the controller is ready, so that the problem of exposing the algorithm (software) early in the development can be solved to perform fast iterative optimization, thereby improving the development efficiency. Therefore, how to design a method capable of quickly verifying the ADAS algorithm in the early stage of its development is an urgent technical problem to be solved.
Disclosure of Invention
Aiming at the defects of the prior art, the technical problems to be solved by the invention are as follows: how to provide an MIL simulation test method for ADAS to quickly verify the ADAS algorithm in the early development stage and quickly perform iterative test after the algorithm is corrected, so that the development efficiency of the ADAS algorithm is improved.
In order to solve the technical problems, the invention adopts the following technical scheme:
an MIL simulation test method for ADAS comprises the following steps:
s1: constructing a simulation scene model and acquiring corresponding simulation scene information;
s2: constructing a dynamic model for simulating the response of the vehicle;
s3: the ADAS algorithm to be tested is used as a tested model to be respectively connected with the simulation scene model and the dynamic model, and the simulation scene model is connected with the dynamic model to form a model interface closed loop;
s4: generating a driving control instruction based on the simulation scene model and inputting the driving control instruction to the dynamic model, so that the dynamic model can complete response based on the driving control instruction and generate corresponding whole vehicle state parameters;
s5: and inputting the simulation scene information and the whole vehicle state parameters into a tested model, generating a corresponding control request instruction through the tested model and inputting the control request instruction into the dynamic model, so that the dynamic model can complete response based on the control request instruction to realize the simulation test of the ADAS algorithm to be tested.
Preferably, in step S1, the simulation scene model is constructed based on the unmanned simulation software.
Preferably, in step S1, the simulation scenario information includes, but is not limited to, road environment information, target vehicle information, own vehicle information, traffic sign information, and traffic flow information; road environment information includes, but is not limited to, lane line type and road curvature; the target vehicle information includes, but is not limited to, a target vehicle type, a target vehicle relative distance, a target vehicle relative speed, and a target vehicle travel track; the self-vehicle information includes, but is not limited to, the relative speed of the self-vehicle and the running track of the self-vehicle.
Preferably, in step S1, when the simulation scene model is constructed, the sensors of the host vehicle may be arranged and the sensor parameters may be customized.
Preferably, in step S2, a dynamic model is constructed based on the automobile system simulation software.
Preferably, in step S4, the driving control command includes, but is not limited to, an accelerator opening degree, a braking force magnitude request, and a steering angle request.
Preferably, in step S4, the vehicle state parameters include, but are not limited to, a vehicle speed, a vehicle acceleration, a vehicle wheel speed signal, a vehicle yaw rate, a vehicle steering angle, and a vehicle brake pedal state.
Preferably, in step S4, the dynamic model is further used to generate and input the vehicle operation posture to the simulation scenario model, so that the simulation scenario model can realize the visual display of the vehicle operation posture.
The invention also discloses an MIL simulation test system for ADAS, which is implemented based on the MIL simulation test method of the invention and specifically comprises the following steps:
the unmanned simulation module is used for constructing a simulation scene model, acquiring corresponding simulation scene information and generating a driving control instruction based on the simulation scene model;
the automobile system simulation module is used for constructing a dynamic model for simulating the response of the automobile, finishing the response based on the driving control instruction through the dynamic model and generating corresponding whole automobile state parameters;
and the tested algorithm module is used for generating a corresponding control request instruction according to the simulation scene information and the whole vehicle state parameters and inputting the control request instruction to the automobile system simulation module so that the dynamic model completes response based on the control request instruction.
The invention also discloses a readable storage medium on which a computer management class program is stored, which when executed by a processor implements the steps of the MIL simulation test method for ADAS of the invention.
Compared with the prior art, the MIL simulation test method for ADAS has the following beneficial effects:
according to the invention, the simulation scene model and the dynamic model are constructed, the model interface closed loop is formed by the simulation scene model and the dynamic model and the ADAS algorithm to be tested, and then the MIL simulation test of the ADAS algorithm is realized in a response simulation mode, so that the ADAS algorithm can be quickly verified in the early development stage, and the algorithm is quickly subjected to iterative test after being corrected, thereby improving the development efficiency of the ADAS. Meanwhile, the model interface closed-loop framework has a simple structure and fast data transfer, and can improve the MIL simulation test efficiency of the ADAS algorithm. In addition, the invention generates the whole vehicle state parameter through the driving control instruction, and further generates the control request instruction through the simulation scene information and the whole vehicle state parameter, so that the simulation test of the ADAS algorithm can be effectively realized.
Drawings
For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
FIG. 1 is a logic block diagram of the MIL simulation test method for ADAS;
FIG. 2 is a schematic diagram of a model interface closed loop;
FIG. 3 is a schematic diagram of an AEB algorithm simulation test experiment.
Detailed Description
The following is further detailed by the specific embodiments:
the first embodiment is as follows:
the embodiment discloses an MIL simulation test method for ADAS.
As shown in fig. 1, the MIL simulation test method for ADAS includes the following steps:
s1: constructing a simulation scene model and acquiring corresponding simulation scene information;
s2: constructing a dynamic model for simulating the response of the vehicle;
s3: connecting an ADAS (advanced driver assistance System) algorithm to be tested as a tested model with a simulation scene model and a dynamic model respectively, and connecting the simulation scene model with the dynamic model to form a model interface closed loop as shown in FIG. 2;
s4: generating a driving control instruction based on the simulation scene model and inputting the driving control instruction to the dynamic model, so that the dynamic model can complete response based on the driving control instruction and generate corresponding whole vehicle state parameters;
s5: and inputting the simulation scene information and the whole vehicle state parameters into a tested model, generating a corresponding control request instruction through the tested model and inputting the control request instruction into the dynamic model, so that the dynamic model can complete response based on the control request instruction to realize the simulation test of the ADAS algorithm to be tested.
It should be noted that the MIL simulation test method of the present invention may generate a corresponding software code or software service in a program programming manner, and further may be run and implemented on a server and a computer.
According to the invention, the simulation scene model and the dynamic model are constructed, the model interface closed loop is formed by the simulation scene model and the dynamic model and the ADAS algorithm to be tested, and then the MIL simulation test of the ADAS algorithm is realized in a response simulation mode, so that the ADAS algorithm can be quickly verified in the early development stage, and the algorithm is quickly subjected to iterative test after being corrected, thereby improving the development efficiency of the ADAS. Meanwhile, the model interface closed-loop framework has a simple structure and fast data transfer, and can improve the MIL simulation test efficiency of the ADAS algorithm. In addition, the invention generates the whole vehicle state parameter through the driving control instruction, and further generates the control request instruction through the simulation scene information and the whole vehicle state parameter, so that the simulation test of the ADAS algorithm can be effectively realized.
Specifically, the ADAS algorithm includes, but is not limited to, AEB algorithm, LKA algorithm, ELKA algorithm, and LDW algorithm, and each algorithm is developed based on simulink and operates in a simulink environment.
Specifically, the simulation scene information includes, but is not limited to, road environment information, target vehicle information, own vehicle information, traffic sign information, and traffic flow information; road environment information includes, but is not limited to, lane line type and road curvature; the target vehicle information includes, but is not limited to, a target vehicle type, a target vehicle relative distance, a target vehicle relative speed, and a target vehicle travel track; the self-vehicle information includes, but is not limited to, the relative speed of the self-vehicle and the running track of the self-vehicle.
Specifically, when the simulation scene model is constructed, the sensors of the vehicle can be configured and the sensor parameters can be customized.
Specifically, the driving control commands include, but are not limited to, an accelerator opening, a braking force magnitude request, and a steering angle request.
Specifically, the vehicle state parameters include, but are not limited to, vehicle speed, vehicle acceleration, vehicle wheel speed signals, vehicle yaw rate, vehicle steering angle, and vehicle brake pedal state.
In a specific implementation process, the dynamic model is further used for generating a vehicle running posture and inputting the vehicle running posture into the simulation scene model, so that the simulation scene model can realize visual display of the vehicle running posture.
In a specific implementation process, a simulation scene model is constructed based on prescan software (unmanned simulation software), and a corresponding prescan module is generated in a simulink environment. The Prescan is based on a Matlab simulation platform and supports traffic scene modeling, sensor modeling, driver behavior modeling and the like, wherein the traffic scene comprises a road model, an environment model, road users, weather illumination and the like, and the sensor model comprises various cameras, millimeter wave radars, laser radars and the like.
In the specific implementation process, a dynamic model is constructed based on Carsim software (automobile system simulation software), and a corresponding Carsim dynamic module is generated in a simulink environment. The Carsim software (dynamic module) can support 27 degrees of freedom, can well simulate the response of a vehicle to driver input, road surface input and aerodynamic input, further can truly simulate the response of the vehicle to a driver input instruction or an ADAS algorithm control request instruction, has the characteristics of convenience in use, rapidness in operation, accuracy in simulation, good expansibility and the like, and can be conveniently integrated in a Matlab environment.
According to the invention, a simulation scene model is constructed through prescan software, a corresponding sensor is configured, and sensor parameters are customized, so that the accuracy of an MIL simulation test can be effectively ensured. Meanwhile, the dynamic model is constructed through the Carsim software, so that the response of the vehicle can be simulated quickly and accurately, and the accuracy of the MIL simulation test can be ensured.
To better illustrate the feasibility of the MIL simulation test method of the present invention, the following experiment is disclosed in this example.
The experiment takes an AEB algorithm simulation test as an example, and explains the use flow of an MIL simulation test environment. The test scenario is selected from the CCRs scenarios in the CNCAP-AEB regulation test scenario, i.e., the scenario where the target vehicle is stationary and the rear vehicle (own vehicle) and the front vehicle (target vehicle) collide with each other, as shown in fig. 3.
Firstly, establishing a simulation scene:
newly building prescan engineering, and building a CCRs test scene according to the figure 3. Scene elements are as follows:
the length of the road is 300 meters, the road is a single lane, and the width of the lane is 3.5 m;
selecting an Audi A3 as a target vehicle, wherein the speed is 0km/h, and the target vehicle is positioned in the center of a lane and is 150m away from the starting point of the lane;
selecting an Audi A8 as a self vehicle, and setting a speed curve as 50km/h to run forwards at a constant speed;
setting a self-vehicle dynamics model as a self-definition, and designating an absolute path of a Carsim-Sfuntion file;
adding a radar sensor on a self-vehicle, and setting a detection range;
and secondly, scene compiling:
setting the simulation frequency to be 1000 Hz, clicking a compiling button on a toolbar to compile the scene, and further generating an operation parameter file and a model;
step three, calling simulink:
clicking an invoke Simulink Run Mode button on a toolbar to start matlab and Simulink, and opening an automatically generated Simulink model;
step four, adding a Carsim kinetic model:
clicking the simulink library, finding a Carsim Sfunction module and dragging the module to an opened simulink model;
fifthly, adding an AEB algorithm module:
adding an AEB algorithm module to the opened simulink model;
sixthly, closing a model interface loop:
interface between Prescan module and Carsim dynamics module:
referring to fig. 2, driving control commands, i.e., signals of accelerator opening, braking force, and steering request angle, output by the Prescan driver model module are connected to an input port of Carsim; the vehicle operation posture output by the Carsim dynamics module is fed back to the Prescan module in real time, namely, signals of vehicle speed, wheel speed, Roll, Pitch, Yaw and the like are connected to a receiving module corresponding to the Prescan module;
interface between Prescan module and AEB algorithm module:
referring to fig. 2, the simulation scene information output by the Prescan module is connected to the target input port of the AEB algorithm module;
interface between Carsim dynamics Module and AEB Algorithm Module
Referring to fig. 2, the state parameters of the entire vehicle, such as speed, acceleration, wheel speed signals, yaw rate, steering angle, brake pedal state, etc., calculated in real time by the Carsim dynamics module are connected to the input port of the AEB algorithm module, and meanwhile, the control request instruction request output by the AEB algorithm is connected to the input port of the Carsim dynamics module for closed-loop control;
and seventhly, operating a simulation model:
clicking a running button of simulink, and running the real model according to the frequency of 1000 Hz;
and eighthly, verifying the operation result:
and observing the function enabling signal and the deceleration strip request signal output by the AEB algorithm module through an oscilloscope module in the simulink, and verifying whether the AEB function algorithm is triggered when the vehicle approaches a front static vehicle at the speed of 50 km/h.
Example two:
the embodiment discloses an MIL simulation test system for ADAS.
The MIL simulation test system for ADAS is implemented based on the MIL simulation test method of the invention, and comprises the following steps:
the pre-scan module (unmanned simulation module) is used for constructing a simulation scene model, acquiring corresponding simulation scene information and generating a driving control instruction based on the simulation scene model;
the system comprises a Carsim dynamics module (automobile system simulation module) and a control module, wherein the Carsim dynamics module is used for constructing a dynamics model for simulating the response of the automobile, finishing the response based on a driving control instruction through the dynamics model and generating corresponding state parameters of the whole automobile;
and the tested algorithm module (ADAS algorithm module) is used for generating a corresponding control request instruction according to the simulation scene information and the whole vehicle state parameters and inputting the control request instruction to the Carsim dynamics module so that the dynamics module completes response based on the control request instruction.
It should be noted that the MIL simulation test system of the present invention operates in a simulink operating environment, wherein the ADAS algorithm module is respectively connected to the simulation scenario model and the dynamic model, and connects the simulation scenario model and the dynamic model to form a model interface closed loop as shown in fig. 2.
According to the invention, a simulation scene model and a dynamic model are constructed through a prescan module and a Carsim dynamic module, and a model interface closed loop is formed with an ADAS algorithm module, so that an MIL simulation test of the ADAS algorithm is realized in a response simulation mode, the ADAS algorithm can be quickly verified in the early stage of development, and a quick iteration test is carried out after the algorithm is corrected, so that the development efficiency of the ADAS can be improved. Meanwhile, the model interface closed-loop framework has a simple structure and fast data transfer, and can improve the MIL simulation test efficiency of the ADAS algorithm. In addition, the invention generates the whole vehicle state parameter through the driving control instruction, and further generates the control request instruction through the simulation scene information and the whole vehicle state parameter, so that the simulation test of the ADAS algorithm can be effectively realized.
Example three:
the embodiment discloses a readable storage medium, on which a computer management class program is stored, and the computer management class program realizes the steps of the MIL simulation test method for ADAS of the present invention when being executed by a processor. The readable storage medium can be a device with readable storage function such as a U disk or a computer.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting the technical solutions, and those skilled in the art should understand that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all that should be covered by the claims of the present invention.
Claims (10)
1. An MIL simulation test method for ADAS is characterized by comprising the following steps:
s1: constructing a simulation scene model and acquiring corresponding simulation scene information;
s2: constructing a dynamic model for simulating the response of the vehicle;
s3: the ADAS algorithm to be tested is used as a tested model to be respectively connected with the simulation scene model and the dynamic model, and the simulation scene model is connected with the dynamic model to form a model interface closed loop;
s4: generating a driving control instruction based on the simulation scene model and inputting the driving control instruction to the dynamic model, so that the dynamic model can complete response based on the driving control instruction and generate corresponding whole vehicle state parameters;
s5: and inputting the simulation scene information and the whole vehicle state parameters into a tested model, generating a corresponding control request instruction through the tested model and inputting the control request instruction into the dynamic model, so that the dynamic model can complete response based on the control request instruction to realize the simulation test of the ADAS algorithm to be tested.
2. The MIL simulation test method for ADAS of claim 1, wherein: in step S1, a simulation scene model is constructed based on the unmanned simulation software.
3. The MIL simulation test method for ADAS of claim 1, wherein: in step S1, the simulation scenario information includes, but is not limited to, road environment information, target vehicle information, own vehicle information, traffic sign information, and traffic flow information; road environment information includes, but is not limited to, lane line type and road curvature; the target vehicle information includes, but is not limited to, a target vehicle type, a target vehicle relative distance, a target vehicle relative speed, and a target vehicle travel track; the self-vehicle information includes, but is not limited to, the relative speed of the self-vehicle and the running track of the self-vehicle.
4. The MIL simulation test method for ADAS of claim 1, wherein: in step S1, when the simulation scene model is constructed, the sensor of the host vehicle can be configured and the sensor parameters can be customized.
5. The MIL simulation test method for ADAS of claim 1, wherein: in step S2, a dynamic model is constructed based on the automobile system simulation software.
6. The MIL simulation test method for ADAS of claim 1, wherein: in step S4, the driving control command includes, but is not limited to, an accelerator opening, a braking force magnitude request, and a steering angle request.
7. The MIL simulation test method for ADAS of claim 1, wherein: in step S4, the vehicle state parameters include, but are not limited to, a vehicle speed, a vehicle acceleration, a vehicle wheel speed signal, a vehicle yaw rate, a vehicle steering angle, and a vehicle brake pedal state.
8. The MIL simulation test method for ADAS of claim 1, wherein: in step S4, the dynamic model is further used to generate and input the vehicle operation posture to the simulation scenario model, so that the simulation scenario model can realize the visual display of the vehicle operation posture.
9. An MIL simulation test system for ADAS, implemented based on the MIL simulation test method in claim 1, specifically comprising:
the unmanned simulation module is used for constructing a simulation scene model, acquiring corresponding simulation scene information and generating a driving control instruction based on the simulation scene model;
the automobile system simulation module is used for constructing a dynamic model for simulating the response of the automobile, finishing the response based on the driving control instruction through the dynamic model and generating corresponding whole automobile state parameters;
and the tested algorithm module is used for generating a corresponding control request instruction according to the simulation scene information and the whole vehicle state parameters and inputting the control request instruction to the automobile system simulation module so that the dynamic model completes response based on the control request instruction.
10. A readable storage medium, having stored thereon a computer management class program, which when executed by a processor, performs the steps of the MIL simulation test method for ADAS according to any of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210044955.1A CN114326443B (en) | 2022-01-14 | 2022-01-14 | MIL simulation test method and system for ADAS and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210044955.1A CN114326443B (en) | 2022-01-14 | 2022-01-14 | MIL simulation test method and system for ADAS and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114326443A true CN114326443A (en) | 2022-04-12 |
| CN114326443B CN114326443B (en) | 2023-09-29 |
Family
ID=81027368
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210044955.1A Active CN114326443B (en) | 2022-01-14 | 2022-01-14 | MIL simulation test method and system for ADAS and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114326443B (en) |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110333730A (en) * | 2019-08-12 | 2019-10-15 | 安徽江淮汽车集团股份有限公司 | Verification method, platform and the storage medium of automatic Pilot algorithm expectation function safety |
| CN110673590A (en) * | 2019-10-21 | 2020-01-10 | 安徽江淮汽车集团股份有限公司 | Method, device and equipment for judging reliability of automatic driving system and storage medium |
| US20200019165A1 (en) * | 2018-07-13 | 2020-01-16 | Kache.AI | System and method for determining a vehicles autonomous driving mode from a plurality of autonomous modes |
| CN110794810A (en) * | 2019-11-06 | 2020-02-14 | 安徽瑞泰智能装备有限公司 | Method for carrying out integrated test on intelligent driving vehicle |
| CN110794813A (en) * | 2019-11-18 | 2020-02-14 | 上海顺试汽车科技有限公司 | In-loop real-time linkage testing method and system for electromechanical parts |
| CN112698582A (en) * | 2020-12-28 | 2021-04-23 | 联创汽车电子有限公司 | ADAS ECU simulation test method and system |
| WO2021089787A1 (en) * | 2019-11-08 | 2021-05-14 | Zf Friedrichshafen Ag | Method, apparatus and computer program for enabling a sensor system for detecting objects in an environment of a vehicle |
| 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 |
| CN112987703A (en) * | 2021-05-19 | 2021-06-18 | 北京博科测试系统股份有限公司 | System and method for developing and testing in-loop automatic driving of whole vehicle in laboratory |
| CN113065257A (en) * | 2021-04-08 | 2021-07-02 | 中国第一汽车股份有限公司 | Automatic generation method and device of test case, computer equipment and medium |
-
2022
- 2022-01-14 CN CN202210044955.1A patent/CN114326443B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200019165A1 (en) * | 2018-07-13 | 2020-01-16 | Kache.AI | System and method for determining a vehicles autonomous driving mode from a plurality of autonomous modes |
| CN110333730A (en) * | 2019-08-12 | 2019-10-15 | 安徽江淮汽车集团股份有限公司 | Verification method, platform and the storage medium of automatic Pilot algorithm expectation function safety |
| CN110673590A (en) * | 2019-10-21 | 2020-01-10 | 安徽江淮汽车集团股份有限公司 | Method, device and equipment for judging reliability of automatic driving system and storage medium |
| CN110794810A (en) * | 2019-11-06 | 2020-02-14 | 安徽瑞泰智能装备有限公司 | Method for carrying out integrated test on intelligent driving vehicle |
| WO2021089787A1 (en) * | 2019-11-08 | 2021-05-14 | Zf Friedrichshafen Ag | Method, apparatus and computer program for enabling a sensor system for detecting objects in an environment of a vehicle |
| CN110794813A (en) * | 2019-11-18 | 2020-02-14 | 上海顺试汽车科技有限公司 | In-loop real-time linkage testing method and system for electromechanical parts |
| CN112698582A (en) * | 2020-12-28 | 2021-04-23 | 联创汽车电子有限公司 | ADAS ECU simulation test method and system |
| 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 |
| CN113065257A (en) * | 2021-04-08 | 2021-07-02 | 中国第一汽车股份有限公司 | Automatic generation method and device of test case, computer equipment and medium |
| CN112987703A (en) * | 2021-05-19 | 2021-06-18 | 北京博科测试系统股份有限公司 | System and method for developing and testing in-loop automatic driving of whole vehicle in laboratory |
Non-Patent Citations (1)
| Title |
|---|
| 庄琼倩，等: "基于Prescan的自然驾驶场景构建与研究", 《汽车实用技术》, vol. 46, no. 07, pages 30 - 31 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114326443B (en) | 2023-09-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111006879B (en) | Port environment-oriented man-machine hybrid driving automatic driving test method and test platform thereof | |
| CN109801534A (en) | Driving behavior hardware-in-the-loop test system based on automatic Pilot simulator | |
| CN112613169B (en) | Expected function safety analysis method for misoperation of automatic driving vehicle | |
| CN111795832B (en) | Intelligent driving vehicle testing method, device and equipment | |
| Bühler et al. | Evolutionary functional testing | |
| CN112115600A (en) | Simulation system for automatically driving vehicle | |
| CN110209146B (en) | Test method, device and equipment for automatic driving vehicle and readable storage medium | |
| CN113515463B (en) | Automatic testing method and device, computer equipment and storage medium | |
| CN112997060A (en) | Method and system for modifying a control unit of an autonomous vehicle | |
| CN112685289A (en) | Scene generation method, and scene-based model in-loop test method and system | |
| Gietelink et al. | Development of a driver information and warning system with vehicle hardware-in-the-loop simulations | |
| CN110764494A (en) | Human-computer hybrid driving automatic driving virtual test platform based on SCANeR | |
| CN112035951A (en) | Simulation platform and simulation method for automatic driving algorithm verification | |
| CN112671487B (en) | Vehicle testing method, server and testing vehicle | |
| Pfeffer et al. | Continuous development of highly automated driving functions with vehicle-in-the-loop using the example of Euro NCAP scenarios | |
| Stević et al. | Development of ADAS perception applications in ROS and" Software-In-the-Loop" validation with CARLA simulator | |
| CN116562175B (en) | Method for evaluating influence of vehicle configuration on safety of automatic driving system | |
| CN111767630A (en) | Virtual simulation test method and device applied to intelligent driving | |
| CN114326443B (en) | MIL simulation test method and system for ADAS and readable storage medium | |
| Miura et al. | Converting driving scenario framework for testing self-driving systems | |
| Zhang et al. | Development and verification of traffic confrontation simulation test platform based on PanoSim | |
| CN114637274A (en) | Automatic emergency brake test system and method | |
| Fan et al. | A Co-Simulation Platform for Powertrain Controls Development | |
| Di Russo et al. | Experimental evaluation of longitudinal control for automated vehicles through vehicle-in-the-loop testing | |
| Jezierska-Krupa et al. | Design method of ADAS for urban electric vehicle based on virtual prototyping |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |