CN112965917B - Test method, device, equipment and storage medium for automatic driving - Google Patents
Test method, device, equipment and storage medium for automatic driving Download PDFInfo
- Publication number
- CN112965917B CN112965917B CN202110404324.1A CN202110404324A CN112965917B CN 112965917 B CN112965917 B CN 112965917B CN 202110404324 A CN202110404324 A CN 202110404324A CN 112965917 B CN112965917 B CN 112965917B
- Authority
- CN
- China
- Prior art keywords
- trajectory
- travel
- autonomous vehicle
- planned
- vehicle
- 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.)
- Active
Links
- 238000003860 storage Methods 0.000 title claims abstract description 26
- 238000010998 test method Methods 0.000 title claims abstract description 9
- 238000012360 testing method Methods 0.000 claims abstract description 84
- 230000000007 visual effect Effects 0.000 claims abstract description 46
- 238000000034 method Methods 0.000 claims abstract description 36
- 230000009471 action Effects 0.000 claims description 27
- 230000004044 response Effects 0.000 claims description 13
- 238000004590 computer program Methods 0.000 claims description 12
- 230000008859 change Effects 0.000 claims description 11
- 230000001133 acceleration Effects 0.000 claims description 9
- 230000007613 environmental effect Effects 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 16
- 230000006870 function Effects 0.000 description 14
- 238000012545 processing Methods 0.000 description 11
- 238000004088 simulation Methods 0.000 description 10
- 230000006399 behavior Effects 0.000 description 6
- 230000008569 process Effects 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000033001 locomotion Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 239000000243 solution Substances 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 230000008676 import Effects 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- 239000012085 test solution Substances 0.000 description 2
- 102000016550 Complement Factor H Human genes 0.000 description 1
- 108010053085 Complement Factor H Proteins 0.000 description 1
- 102000003712 Complement factor B Human genes 0.000 description 1
- 108090000056 Complement factor B Proteins 0.000 description 1
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 230000003542 behavioural effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003086 colorant Substances 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- BTCSSZJGUNDROE-UHFFFAOYSA-N gamma-aminobutyric acid Chemical compound NCCCC(O)=O BTCSSZJGUNDROE-UHFFFAOYSA-N 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3684—Test management for test design, e.g. generating new test cases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/36—Preventing errors by testing or debugging software
- G06F11/3668—Software testing
- G06F11/3672—Test management
- G06F11/3688—Test management for test execution, e.g. scheduling of test suites
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Traffic Control Systems (AREA)
Abstract
According to example embodiments of the present disclosure, test methods, apparatus, devices, and computer-readable storage media for autopilot are provided. The test method for automatic driving includes setting at least one test scenario for a virtual vehicle based on travel data of an automatic driving vehicle in a real environment. Each test scene comprises a running state of the virtual vehicle and a test environment in which the virtual vehicle is located. The method further includes obtaining configuration information for trajectory planning, the configuration information specifying a plurality of factors related to a driving cost of the trajectory. The method further includes generating a planned trajectory for the virtual vehicle in the at least one test scenario based on the configuration information. The method further includes presenting a first visual representation of the planned trajectory and a second visual representation of a driving cost of the planned trajectory. In this way, it is possible to support adjusting the cost configuration related to the cost of the trajectory in the test, thereby optimizing the trajectory planning function of the autopilot system.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of autopilot and, more particularly, relate to a test method, apparatus, device, computer readable storage medium, and program product for autopilot.
Background
Autopilot is a technique of sensing the surroundings of a vehicle, planning the movement track of the vehicle, and controlling the vehicle to reach a specified target by using a computer instead of or in addition to a human driver. An autopilot system in a broad sense typically comprises two parts, namely a software system and a hardware system. The hardware system includes various sensors for sensing the environment and actuators for causing the vehicle to perform a driving action. The software system comprises various modules for information fusion, path planning, behavior decision and motion control. An important function of the software system is to generate trajectories for autonomous vehicles. Therefore, during the development of a software system, tests need to be performed to enable the software system to generate efficient and safe trajectories.
Disclosure of Invention
According to an example embodiment of the present disclosure, a test solution for autopilot is provided.
In a first aspect of the present disclosure, a test method for autopilot is provided. The method includes setting at least one test scenario for the virtual vehicle based on travel data of the autonomous vehicle in the real environment. Each of the at least one test scenario includes a travel state of the virtual vehicle and a test environment in which the virtual vehicle is located. The method further includes obtaining configuration information for trajectory planning, the configuration information specifying a plurality of factors related to a driving cost of the trajectory. The method further includes generating a planned trajectory for the virtual vehicle in the at least one test scenario based on the configuration information. The method further includes presenting a first visual representation of the planned trajectory and a second visual representation of a driving cost of the planned trajectory.
In a second aspect of the present disclosure, a test device for autopilot is provided. The apparatus includes a scenario setting module configured to set at least one test scenario for a virtual vehicle based on travel data of an autonomous vehicle in a real environment, each of the at least one test scenario including a travel state of the virtual vehicle and a test environment in which the virtual vehicle is located. The apparatus further includes a configuration obtaining module configured to obtain configuration information for trajectory planning, the configuration information specifying a plurality of factors related to a running cost of the trajectory. The apparatus further includes a trajectory generation module configured to generate a planned trajectory for the virtual vehicle in the at least one test scenario based on the configuration information. The apparatus further includes an information presentation module configured to present a first visual representation of the planned trajectory and a second visual representation of a cost of travel of the planned trajectory.
In a third aspect of the present disclosure, an electronic device is provided that includes one or more processors; and storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement a method according to the first aspect of the present disclosure.
In a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method according to the first aspect of the present disclosure.
In a fifth aspect of the present disclosure, there is provided a computer program product comprising computer executable instructions, wherein the computer executable instructions when executed by a processor implement the method according to the first aspect of the present disclosure.
It should be understood that what is described in this summary is not intended to limit the critical or essential features of the embodiments of the disclosure nor to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of embodiments of the present disclosure will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, wherein like or similar reference numerals designate like or similar elements, and wherein:
FIG. 1 illustrates a schematic diagram of an example environment in which various embodiments of the present disclosure may be implemented;
FIG. 2 illustrates an example of a user interface according to some embodiments of the present disclosure;
FIG. 3 illustrates a flow chart of a test method for autopilot in accordance with some embodiments of the present disclosure;
FIG. 4 illustrates an example of a second region of a user interface according to some embodiments of the present disclosure;
FIG. 5 illustrates a flow chart of a method of setting up a test scenario, according to some embodiments of the present disclosure;
FIG. 6 illustrates another example of a second region of a user interface according to some embodiments of the present disclosure;
FIG. 7 illustrates yet another example of a user interface according to some embodiments of the present disclosure;
FIG. 8 illustrates a schematic block diagram of a test apparatus for autopilot in accordance with some embodiments of the present disclosure; and
FIG. 9 illustrates a block diagram of a computing device capable of implementing various embodiments of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure.
In describing embodiments of the present disclosure, the term "comprising" and its like should be taken to be open-ended, i.e., including, but not limited to. The term "based on" should be understood as "based at least in part on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.
As briefly described above, one important function of the software system in an autopilot system is to generate a trajectory for an autopilot vehicle. In track generation, the total cost of the track needs to be considered. The total cost of the track is related to the different behaviors of the autonomous vehicle, and the cost terms corresponding to these behaviors constitute the total cost. For example, the cost term corresponding to the trajectory colliding with the pedestrian is a, the cost term corresponding to the trajectory pressing against the lane boundary is B, the cost term corresponding to the speed limit exceeding the speed limit region is C, and so on. The total cost of the track is the sum of these cost terms. In trajectory generation, the total cost of the planned trajectory can be continually reduced by iteration. In other words, a trajectory that is less misplaced, i.e., a trajectory that has a lower overall cost, may be determined to be a reasonable trajectory.
How to set the cost items corresponding to different behaviors is a critical issue for trajectory planning. In conventional solutions, the cost term is typically set empirically. This solution is largely limited by subjective experience and is thus disadvantageous for generating optimized trajectories. In addition, in conventional solutions, the test is typically directly performed on an automated driving vehicle that is performing the drive test, and the relationship between the trajectory and the cost cannot be intuitively provided to the tester.
According to embodiments of the present disclosure, a test solution for autopilot is presented that aims to address one or more of the above problems and other potential problems. In this scheme, at least one test scenario for a virtual vehicle is set based on travel data of an autonomous vehicle in a real environment. Each of the set at least one test scenario includes a running state of the virtual vehicle and a test environment in which the virtual vehicle is located. Configuration information for trajectory planning is obtained. The configuration information specifies a plurality of factors related to the running cost of the track, such as a plurality of cost items. Based on the configuration information, a planned trajectory is generated for the virtual vehicles in the at least one test scenario. A visual representation of the planned trajectory and a visual representation of the running cost of the planned trajectory are presented.
According to the test scheme for autopilot presented herein, the trajectory planning function may be tested with the travel data in the real environment and the planned trajectory and associated cost information visually presented. Thus, it is possible to support adjustment of the cost configuration related to the cost of the track in the test, thereby making the calculated cost more reliable. In this way, the trajectory planning function of the autopilot system may be optimized. Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.
FIG. 1 illustrates a schematic diagram of an example environment 100 in which various embodiments of the present disclosure may be implemented. In general, the example environment 100 includes a real environment 101 and a test environment (not shown) built by a computing device 102. The real environment 101 includes pedestrians 112 waiting through roads, trees 113 on the sides of roads, and vehicles 111 (which are also referred to as "autonomous vehicles") in which the vehicle-mounted system 110 is deployed. The vehicle 111 travels in the real environment 101. The pedestrians 112, trees 113, roads, etc. constitute an external environment where the vehicle 111 travels. The vehicle 111 may perform a running test in the real environment 101, or may actually run in the real environment 101. It should be understood that the real environment 101 shown in fig. 1 is merely illustrative and is not intended to limit the scope of the present disclosure.
The in-vehicle system 110 deployed on the vehicle 111 (e.g., an in-vehicle terminal or other in-vehicle device of the vehicle 111) may include at least a portion of a software system for autonomous driving. For example, the in-vehicle system 110 may include various modules (not shown) for information fusion, path planning, behavioral decision-making, motion control. While the vehicle 111 is traveling in the real environment 101, the in-vehicle system 110 may generate and record environmental information related to the real environment 101. For example, a sensing device (e.g., lidar, camera, etc.) mounted on the vehicle 111 may sense and collect environmental data, and the in-vehicle system 110 may generate environmental information based on the environmental data. Such environmental information may include various information about the external environment of the vehicle 111 while traveling, such as information indicating pedestrians 112, information indicating roads, and the like.
In addition to the environmental information, the in-vehicle system 110 may also generate and record travel information related to the travel action of the vehicle 111. The travel information described herein may refer to various information required to reproduce the travel state of the vehicle 111 in the real environment 101. The travel information may include one or more travel actions made by the vehicle 111 over time during travel, a start time and/or an end time of the one or more travel actions, a trajectory followed by the one or more travel actions, a trigger for the one or more travel actions, and the like.
The in-vehicle system 110 may generate and record the travel information 131 in a variety of suitable ways. In some embodiments, for each travel action of the vehicle 111, travel information related to the travel action, such as a start time of the travel action, a trajectory followed by the travel action, and the like, may be recorded. In such an embodiment, the travel information is recorded in accordance with the travel action.
The computing device 102 may obtain or store the travel information and environmental information generated and recorded by the in-vehicle system 110 as at least a portion of the travel data 130 of the autonomous vehicle in the real environment. The computing device 102 may also obtain or store configuration information 140 for trajectory planning.
The configuration information 140 specifies at least a plurality of factors related to the running cost of the track. These factors are, for example, considered as a plurality of cost terms that make up the total cost of the track, and thus may also be referred to herein as "cost terms". The configuration information 140 may indicate the weight of each of the plurality of factors and the manner in which the contribution to the total cost is made. The contribution may refer to a mathematical form of the corresponding factors in the calculation of the total cost, such as cubic, quadratic, linear, etc.
These factors related to the running cost can be divided into four types. The first type of factor relates to the ability of an autonomous vehicle to perform a driving maneuver. In other words, the first type of factor may be a kinematic restriction of the autonomous vehicle. As an example, the first type of factors may include, but are not limited to, maximum speed, maximum forward acceleration, maximum lateral acceleration, range of attitude angles, and the like.
The second type of factor relates to the consumption of the autonomous vehicle to perform the driving action. For example, the second type of factor may be the energy that an autonomous vehicle will expend following a trajectory. As an example, the second type of factors may include, but are not limited to, planned forward acceleration, lateral acceleration, angle change, etc. induced consumption.
The third type of factor relates to a travel action that the autonomous vehicle is prohibited from performing. In other words, the third type of factor may be a strong constraint on the behavior of the autonomous vehicle. As an example, the third type of factors may include, but are not limited to, violations of traffic rules, requested link speed limits, link boundary limits, and so forth.
The fourth type of factor relates to the travel actions that the autonomous vehicle is restricted from performing. In other words, the fourth type of factor may be a weak constraint on the behavior of the autonomous vehicle. As an example, the fourth type of factors may include, but are not limited to, a preferred speed range, lane boundaries, comfort requirements, and the like.
In some embodiments, the configuration information 140 may be obtained from the in-vehicle system 110. In such an embodiment, the factors (including the weights and contribution) specified by the configuration information 140 are used by the in-vehicle system 110 in generating the trajectory for the vehicle 111. In some embodiments, configuration information 140 may be generated by any suitable algorithm or specified by a user.
A trajectory optimizer 120 is deployed at the computing device 102. The trajectory optimizer 120 may provide or draw a user interface 150 via a display unit of the computing device 102. A user, such as a developer of an autopilot system, may interact with the trajectory optimizer 120 through the user interface 150, for example, to adjust various cost terms.
FIG. 2 illustrates one example of a user interface 150 provided by the trajectory optimizer 120. In general, the user interface 150 may include a first region 210, a second region 220, a third region 230, and a trigger button "start".
The first region 210 is used to present a visual representation of the planned trajectory generated by the trajectory optimizer 120 based on the travel data 130 and the configuration information 140. The first region 210 may also present a visual representation of other information (e.g., constraints) related to the planned trajectory. The second area 220 is used to present options to the user regarding the test scenario and configuration information. To this end, the second area 220 includes a test scene panel 221 and a configuration information panel 222. The third region 230 is used to present a visual representation of the running cost of the planned trajectory. For example, the third region 230 may display the contribution of each cost item to the running cost separately. The trigger button "start" is used to trigger trajectory generation in response to a user click. The contents presented by the first region 210, the second region 220, and the third region 230 will be described in detail below.
With continued reference to fig. 1. The environment 100 shown in fig. 1 is merely exemplary. Trajectory optimizer 120 may be implemented or distributed across multiple computing devices. Alternatively, trajectory optimizer 120 may receive travel data 130 and configuration information 140 from other devices. Computing device 102 may be any device having computing capabilities. As non-limiting examples, computing device 102 may be any type of fixed, mobile, or portable computing device, including but not limited to a desktop computer, a laptop computer, a notebook computer, a netbook computer, a tablet computer, a multimedia computer, a mobile phone, and the like; all or a portion of the components of computing device 102 may be distributed across the cloud.
In order to more clearly understand the test scheme for autopilot provided by the embodiments of the present disclosure, the embodiments of the present disclosure will be further described with reference to fig. 3. FIG. 3 illustrates a flowchart of an example test method 300 for autopilot in accordance with an embodiment of the present disclosure. The method 300 may be implemented by the trajectory optimizer 120 of fig. 1. For ease of discussion, the method 300 will be described in connection with FIG. 1.
At block 310, the trajectory optimizer 120 sets at least one test scenario for the virtual vehicle based on the travel data 130 of the autonomous vehicle in the real environment 101. Each test scene comprises a running state of the virtual vehicle and a test environment in which the virtual vehicle is located. The travel data 130 may include data of a plurality of real scenes. The data of each real scene may include travel information and environment information in the real scene, as described with reference to fig. 1. The data for each real scene may be stored in a file. Such files are also referred to as scene files.
In some embodiments, the at least one test scene set may be a reproduction of a real scene. For example, the trajectory optimizer 120 may read data of a real scene in a scene file and set a test scene using the read data.
Refer to fig. 4. Fig. 4 illustrates an example of a second region 220 of the user interface 150 according to some embodiments of the present disclosure. When the test scene panel 221 is presented, the second region 220 includes a sub-region 410 related to the real scene, which in turn includes a button "load real scene" for the user to select to load real scene. In response to the user clicking on the button "load real scene," the track optimizer 120 may present a directory including one or more scene files for selection by the user. Thus, batch import of scene files may be supported.
If it is recognized that the user selects a certain scene file, the trajectory optimizer 120 may parse the scene file to obtain data of a real scene and set a test scene based on the data. Accordingly, the name of the selected scene file may be presented in the "name" column of table 415. At this time, the "status" column may display, for example, "not started" or the like.
If it is recognized that the user selects a directory including a plurality of scene files, the trajectory optimizer 120 may parse each scene file under the directory to obtain data of a corresponding real scene, and set a plurality of test scenes based on the parsed data. Accordingly, the names of the individual scene files in the selected directory may be presented in table 415. At this time, the "status" column may display, for example, "not started".
In such an embodiment, by reproducing the real scene in the real environment, the travel in the real scene can be restored. In this way, debugging can be performed for specific problems in the real scene.
Alternatively or additionally, in some embodiments, the at least one test scenario set may be a simulated scenario. The simulated scene may be determined by changing the real scene. In such an embodiment, the test scene panel 221 may include sub-regions 420 related to the simulated scene.
Such an embodiment is described below with reference to fig. 5. Fig. 5 illustrates a flow chart of a method 500 of setting up a test scenario according to some embodiments of the present disclosure. Method 500 may be considered a specific implementation of block 310.
At block 510, the trajectory optimizer 120 determines constraints related to the driving environment of the autonomous vehicle from the driving data 130. For example, the trajectory optimizer 120 may determine constraints from a scene file selected by a user. Constraints described herein may refer to any condition or parameter that limits or affects the travel of an autonomous vehicle.
The constraint may be used to constrain the geometry of the road on which the autonomous vehicle is traveling. For example, such constraints may specify the width of the road, whether there is a turn, the angle of the turn (e.g., a right angle bend, an S-shape), etc. Alternatively or additionally, the constraint may be used to constrain the speed of the vehicle on the road. For example, such constraints may specify a speed limit, such as a maximum speed, specified by the road. Alternatively or additionally, the constraint may be used to constrain the distance of the autonomous vehicle relative to another vehicle on the road. Such constraints may specify the distance of the autonomous vehicle relative to the preceding vehicle, the speed relative to the preceding vehicle, etc. Such constraints may be, for example, adaptive cruise (ACC) parameters.
At block 520, the trajectory optimizer 120 changes constraints based on user input. For example, the trajectory optimizer 120 may change the geometry of the road, the upper limit of speed, the parameters of the ACC, etc., based on user input.
In some embodiments, the user input may instruct loading of a pre-generated simulation configuration to change the constraints. The pre-generated simulation configuration may specify a policy to change one or more constraints. Refer to fig. 4. The sub-region 420 may include a button "load simulation configuration" for the user to select to load the simulation configuration. If the button "load simulation configuration" is identified as being clicked, the trajectory optimizer 120 may read the pre-generated simulation configuration and change one or more constraints according to the specified policy. For example, the angle of the turn of the road may be strategically increased.
Alternatively or additionally, in some embodiments, the user input may indicate that a simulated configuration is to be generated to change the constraints. As shown in fig. 4, sub-region 420 may include a button "generate simulation configuration" for the user to select to generate a simulation configuration. If the button "generate simulation configuration" is identified as being clicked, the trajectory optimizer 120 may receive constraints from the user that are desired to be changed. As an example, trajectory optimizer 120 may receive user input in form 425 specifying constraints that the user desires to change. For example, the input may specify the ACC parameter and its value to be changed.
At block 530, the trajectory optimizer 120 sets at least one test scenario based on the changed constraints. The unchanged conditions or parameters may maintain data in the scene file. The trajectory optimizer 120 may set up a test scenario using the documented generated simulation configuration and data in the maintained scenario file.
In such an embodiment, scenes not included in the real environment may be set by changing one or more constraints. In this way, a combination of test scenarios desired by the user can be simulated. In this way, all possible scenes can be covered as much as possible. Testing in more scenarios helps to fully optimize the trajectory planning function of the autopilot system.
With continued reference to fig. 3. At block 320, the trajectory optimizer 120 obtains configuration information 140 for trajectory planning. The configuration information 140 specifies a plurality of factors related to the running cost of the track. These factors may be the four types of factors described with reference to fig. 1. The configuration information 140 may indicate the weight of each of the plurality of factors and the manner in which the contribution to the total cost (e.g., quadratic, linear, sigmoid, etc.). As an example, the weight of the maximum speed among the factors of the first type may be 5000, and the contribution manner may be quadratic. The weight of comfort in the fourth type of factor may be 25 and the contribution may be linear.
Configuration information 140 may be read from one or more configuration files. Alternatively or additionally, the configuration information 140 may be user entered or modified. Refer to fig. 6. Fig. 6 illustrates another example of a second region 220 of a user interface according to some embodiments of the present disclosure. For example, after setting the test scenario, in response to the user clicking on the tab of the configuration information panel 222, the trajectory optimizer 120 may present the configuration information panel 222 as shown in FIG. 6.
The configuration information panel 222 may include a button "add configuration" for the user to select add configuration information. For example, in response to the button being clicked, the trajectory optimizer 120 may receive configuration information entered by the user in the sub-region 610, such as weights for the individual cost items. As another example, in response to the button being clicked, the trajectory optimizer 120 may import a file containing configuration information from outside.
Alternatively or additionally, the configuration information panel 222 may include a button "load configuration" for the user to select to load configuration information. For example, in response to the button being clicked, the trajectory optimizer 120 may present one or more configuration files to the user for selection by the user. Then, the method comprises the steps of. The trajectory optimizer 120 may load the configuration file selected by the user and parse the configuration information therein.
Alternatively or additionally, the configuration information panel 222 may include a button "modify configuration" for the user to select to modify configuration information. For example, in response to the button being clicked, the trajectory optimizer 120 may receive a modification to one or more cost items entered by the user in the sub-region 610. For example, the user input may indicate that the weight of the maximum speed is modified from 5000 to 3000. As another example, the user input may indicate to modify the comfort contribution from linear to quadratic.
With continued reference to fig. 3. At block 330, the trajectory optimizer 120 generates a planned trajectory for the virtual vehicle in the at least one test scenario based on the configuration information 140. For example, in response to determining that the button "start" shown in FIG. 2 is clicked, the trajectory optimizer 120 begins generating a planned trajectory for the virtual vehicle in one or more test scenarios.
The trajectory optimizer 120 may include a trajectory generation module that is the same as or similar to the module used to generate the trajectory in the in-vehicle system 110. At block 330, the trajectory generation module may generate a planned trajectory for the virtual vehicle based on the configuration information 140. To utilize the trajectory generation module, the trajectory optimizer 120 may load a configuration related to the trajectory generation model, which may be obtained, for example, from the in-vehicle system 110.
At block 340, the trajectory optimizer 120 presents a visual representation of the planned trajectory (also referred to as a "first visual representation") and a visual representation of the running cost of the planned trajectory (also referred to as a "second visual representation"). The trajectory optimizer 120 may present the first and second visual representations in any suitable manner in the user interface 150.
Refer to fig. 7. Fig. 7 illustrates yet another example of a user interface 150 according to some embodiments of the present disclosure. After generating the planned trajectory, the trajectory optimizer 120 may present a visual representation 710 of the planned trajectory in the first region 210. Generally, in this example, visual representation 710 is a line depicting the orientation of the planned path. In addition, visual representation 710 may additionally include information of velocity, acceleration, attitude angle, etc. at each point in the planned trajectory. For example, in response to a user clicking on a point on visual representation 710, trajectory optimizer 120 may display a speed, acceleration, attitude angle, etc. at that point.
Additionally, in some embodiments, the trajectory optimizer 120 may also present a visual representation of other information related to planning the trajectory in the first region 210. Such other information may include time-varying conditions that make up the test scene, time-invariant conditions, time-varying instructions generated for the test scene, time-invariant instructions, and the like. In the example of fig. 7, a visual representation 711 of the boundary of a road and a visual representation 712 of an obstacle on the road are shown.
After generating the planned trajectory, the trajectory optimizer 120 may present a visual representation of the travel cost of the planned trajectory in the third region 230. The trajectory optimizer 120 may present a visual representation of the total cost of travel, as well as a visual representation of a plurality of factors (i.e., a plurality of cost items) related to the cost of travel.
In the example of fig. 7, the trajectory optimizer 120 displays a graph 730 of the cost of travel in the third region 230. Graph 730 may include a curve representing total running costs and curves representing individual cost terms.
The trajectory optimizer 120 may present a plurality of interface elements corresponding to the plurality of cost items and present a contribution of the corresponding cost item to the total trajectory cost in association with each of the plurality of interface elements. The contributions described herein may refer to the value of the corresponding cost term for the planned trajectory, or the proportion of the corresponding cost term in the total driving cost.
In the example of fig. 7, the trajectory optimizer 120 presents interface elements corresponding to cost items in the sub-region 720. Fig. 7 shows the sub-region 720 on an enlarged scale. Interface elements 701 through 709 correspond to factor a, factor B, factor C, factor D, factor E, factor F, factor G, factor H, factor I, respectively. In some embodiments, interface elements 701-709 may have different colors to more intuitively distinguish between different factors. Immediately following the interface element, the values of the corresponding factors in the planned trajectory are displayed. In this example, the values of factor C and factor G, i.e., the costs generated by factor C and factor G, are 10 and 5, respectively, while the values of the other factors are zero. It should be understood that the values of the factors shown in fig. 7 are merely exemplary and are not intended to limit the scope of the present disclosure.
In this way, a user (e.g., a developer of an autopilot system) may be made to directly perceive the role of different factors in the planned trajectory. The factor may also be highlighted after the user clicks on the interface element corresponding to the factor.
Further, in some embodiments, after generating a planned trajectory for a test scenario, a scenario file for setting the test scenario may be identified accordingly. For example, the "status" column of table 415 shown in fig. 4 may display an element corresponding to the scene file such as "tested".
In some embodiments, the trajectory optimizer 120 may support real-time modification of the configuration information 140. For example, a user may modify one or more factors, such as modifying weights, contribution, or other possible parameters of one or more factors, through the user interface 150.
In response to presenting the visual representation of the cost of travel, the trajectory optimizer 120 may receive user input. The user input may indicate factors that the user desires to modify. The trajectory optimizer 120 may adjust at least one of a plurality of factors based on the user input. That is, the trajectory optimizer 120 may modify factors that the user desires to modify according to user input. Next, the trajectory optimizer 120 may update the planned trajectory generated for the virtual vehicle based on the adjusted factors and present a visual representation of the difference between the pre-updated planned trajectory and the updated planned trajectory. In such an embodiment, the cost configuration may be changed in real-time and the track generated in real-time. In this way, the adjustment of the cost configuration can be performed efficiently.
As an example, after viewing the contribution of the presented factors to the total running cost, the user may desire to modify a certain factor, such as its weight, manner of contribution, or additional parameters, etc. The user may click on the button "modify configuration" in the second area 220 shown in fig. 6, and enter or specify factors for which modification is desired. Upon receiving user input, the trajectory optimizer 120 may save the modified factors to generate modified configuration information and generate a new planned trajectory based on the modified configuration information. The trajectory optimizer 120 may present a visual representation of the new planned trajectory in the first region 210. The visual representation may be displayed superimposed with the visual representation 710 shown in fig. 7. In this way, the user can be intuitively presented with the distinction of the two planned trajectories.
With continued reference to fig. 3. If multiple test scenarios are set at block 310, blocks 320 through 340 may be performed sequentially for the set multiple test scenarios. For example, blocks 320 through 340 may be performed sequentially in the order in which the test scenarios are arranged in table 415.
In some embodiments, the method 300 may also include additional steps or blocks. For each of the plurality of test scenarios, it is determined whether the reference properties of the planned trajectory generated at block 330 are better than the reference properties of the real trajectory corresponding to that test scenario. The reference attribute as used herein may be any suitable metric for evaluating the quality of a track, such as a lateral clearance (clearance), a change in velocity, a change in acceleration, and the like. The scope of the present disclosure is not limited in this respect.
If it is determined that the reference properties of the planned trajectory are better than the reference properties of the real trajectory for more than the threshold number of test scenes of the plurality of test scenes, the planned trajectory may be considered to be better than the real trajectory. In this case, the trajectory optimizer 120 may output configuration information used to generate the planned trajectory for generating the trajectory for the autonomous vehicle. That is, such configuration information is a cost configuration that performs well for most test scenarios.
The above-described procedure may be performed for a plurality of configurations of the running cost, i.e., a plurality of combinations of cost items. This helps to find a cost configuration with better generalization performance. Such a cost configuration may be applicable to most autopilot scenarios.
As can be seen from the above description, embodiments according to the present disclosure can test a trajectory planning function using travel data in a real environment and visually present a planned trajectory and associated cost information. Thus, it is possible to support adjustment of cost configuration in testing in order to find a more reliable cost configuration scheme. This may facilitate optimizing the trajectory planning function of the autopilot system. Further, it should be understood that the number and shape, relative positions, values, etc. of interface elements in the user interfaces shown in fig. 1,2, 4, 6, and 7 are exemplary and are not intended to limit the scope of the present disclosure.
Fig. 8 illustrates a schematic block diagram of a test apparatus 800 for autopilot in accordance with some embodiments of the present disclosure. Apparatus 800 may be included on computing device 102 of fig. 1. For example, apparatus 800 may be used to implement trajectory optimizer 120 shown in fig. 1.
As shown in fig. 8, the apparatus 800 includes a loop scenario setting module 810 configured to set at least one test scenario for a virtual vehicle based on travel data of an autonomous vehicle in a real environment. The apparatus 800 further comprises a configuration obtaining module 820 configured to obtain configuration information for trajectory planning, the configuration information specifying a plurality of factors related to a driving cost of the trajectory. The apparatus 800 further comprises a trajectory generation module 830 configured to generate a planned trajectory for the virtual vehicle in the at least one test scenario based on the configuration information. The apparatus 800 further comprises an information presentation module 840 configured to present a first visual representation of the planned trajectory and a second visual representation of the running cost of the planned trajectory.
In some embodiments, the apparatus 800 further comprises: a track evaluation module configured to determine, for each of the at least one test scenario, whether a reference attribute of the planned track is better than a reference attribute of a real track corresponding to each test scenario; and a configuration output module configured to output configuration information for generating a trajectory for the autonomous vehicle if it is determined that the reference properties of the planned trajectory are better than the reference properties of the real trajectory for more than a threshold number of the at least one test scenario.
In some embodiments, the scene setting module 820 includes: a condition determination module configured to determine a constraint condition related to a running environment of the autonomous vehicle from the running data; a condition changing module configured to change the constraint condition based on the first user input; and a condition utilization module configured to set at least one test scenario based on the changed constraint condition.
In some embodiments, the constraint is used to constrain at least one of: the geometry of the road on which the autonomous vehicle is traveling, the speed of the vehicle on the road, or the distance of the autonomous vehicle relative to another vehicle on the road.
In some embodiments, the apparatus 800 further comprises: an input receiving module configured to receive a second user input in response to presenting the second visual representation; a factor adjustment module configured to adjust at least one factor of the plurality of factors based on the second user input; a track updating module configured to update a planned track generated for the virtual vehicle based on the adjusted at least one factor; and a difference presentation module configured to present a third visual representation of a difference between the planned trajectory and the updated planned trajectory.
In some embodiments, the information presentation module 840 includes: an interface element presentation module configured to present a plurality of interface elements corresponding to a plurality of factors; and a numerical presentation module configured to present, in association with each interface element of the plurality of interface elements, a contribution of the corresponding factor to a running cost of the planned trajectory.
In some embodiments, the plurality of factors relates to at least one of: the ability of an autonomous vehicle to perform a travel action, consumption of the autonomous vehicle to perform a travel action, a travel action that the autonomous vehicle is prohibited from performing, or a travel action that the autonomous vehicle is restricted from performing.
Fig. 9 shows a schematic block diagram of an example device 900 that may be used to implement embodiments of the present disclosure. Device 900 may be used to implement computing device 102 of fig. 1. As shown, the device 900 includes a Central Processing Unit (CPU) 901, which can perform various suitable actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM) 902 or loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the device 900 can also be stored. The CPU 901, ROM 902, and RAM 903 are connected to each other through a bus 904. An input/output (I/O) interface 905 is also connected to the bus 904.
Various components in device 900 are connected to I/O interface 905, including: an input unit 906 such as a keyboard, a mouse, or the like; an output unit 907 such as various types of displays, speakers, and the like; a storage unit 908 such as a magnetic disk, an optical disk, or the like; and a communication unit 909 such as a network card, modem, wireless communication transceiver, or the like. The communication unit 909 allows the device 900 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunications networks.
The processing unit 901 performs the various methods and processes described above, such as any of the processes 300 and 500. For example, in some embodiments, any of the processes 300 and 500 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 900 via the ROM 902 and/or the communication unit 909. When the computer program is loaded into RAM 903 and executed by CPU 901, one or more steps of any of processes 300 and 500 described above may be performed. Alternatively, in other embodiments, CPU 901 may be configured to perform any of processes 300 and 500 by any other suitable means (e.g., by means of firmware).
The present disclosure may be methods, apparatus, systems, and/or computer program products. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C ++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (13)
1. A test method for autopilot, comprising:
determining, based on travel data of an autonomous vehicle in a real environment, a constraint condition related to the travel environment of the autonomous vehicle from the travel data, changing the constraint condition based on a first user input, and setting at least one test scenario for a virtual vehicle based on the changed constraint condition, each of the at least one test scenario including a travel state of the virtual vehicle and a test environment in which the virtual vehicle is located;
Obtaining configuration information for track planning, wherein the configuration information specifies a plurality of factors related to the running cost of a track and the contribution mode of each factor of the plurality of factors to the total cost; the plurality of factors includes a first type of factor related to an ability of the autonomous vehicle to perform a travel action, a second type of factor related to consumption of the autonomous vehicle to perform a travel action, a third type of factor related to a travel action prohibited from being performed by the autonomous vehicle, and a fourth type of factor related to a travel action restricted from being performed by the autonomous vehicle;
Generating a planned track for the virtual vehicle in the at least one test scenario based on the configuration information;
Determining, for each of the at least one test scenario, whether a reference attribute of the planned trajectory is better than the reference attribute of a real trajectory corresponding to the each test scenario; if it is determined for more than a threshold number of the at least one test scenario that the reference attribute of the planned trajectory is better than the reference attribute of the real trajectory, outputting the configuration information for generating a trajectory for an autonomous vehicle; and
Presenting a first visual representation of the planned trajectory and a second visual representation of a driving cost of the planned trajectory;
Receiving a second user input in response to presenting the second visual representation; adjusting at least one factor of the plurality of factors based on the second user input; updating the planned trajectory generated for the virtual vehicle based on the adjusted at least one factor; a third visual representation of a difference between the planned trajectory and the updated planned trajectory is presented.
2. The method of claim 1, wherein the constraint is used to constrain at least one of:
the geometry of the road on which the autonomous vehicle is traveling,
The speed of the vehicle on the road, or
The distance of the autonomous vehicle relative to the other vehicles on the road.
3. The method of claim 1, wherein presenting the second visual representation comprises:
Presenting a plurality of interface elements corresponding to the plurality of factors; and
A contribution of a corresponding factor to the driving cost of the planned trajectory is presented in association with each interface element of the plurality of interface elements.
4. The method of claim 1, wherein the travel data includes travel information and environmental information.
5. The method of claim 1, wherein the first visual representation comprises a line depicting an orientation of a planned path, and a velocity, acceleration, attitude angle at each point in the planned trajectory.
6. A test device for autopilot, comprising:
A scenario setting module configured to determine, from travel data of an autonomous vehicle in a real environment, a constraint condition related to the travel environment of the autonomous vehicle from the travel data, change the constraint condition based on a first user input, and set at least one test scenario for a virtual vehicle based on the changed constraint condition, each of the at least one test scenario including a travel state of the virtual vehicle and a test environment in which the virtual vehicle is located;
A configuration obtaining module configured to obtain configuration information for trajectory planning, the configuration information specifying a plurality of factors related to a running cost of a trajectory, and a contribution manner of each of the plurality of factors to a total cost; the plurality of factors includes a first type of factor related to an ability of the autonomous vehicle to perform a travel action, a second type of factor related to consumption of the autonomous vehicle to perform a travel action, a third type of factor related to a travel action prohibited from being performed by the autonomous vehicle, and a fourth type of factor related to a travel action restricted from being performed by the autonomous vehicle;
a track generation module configured to generate a planned track for the virtual vehicle in the at least one test scenario based on the configuration information;
A track evaluation module configured to determine, for each of the at least one test scenario, whether a reference attribute of the planned track is better than the reference attribute of a real track corresponding to the each test scenario; a configuration output module configured to output the configuration information for generating a trajectory for an autonomous vehicle if it is determined for more than a threshold number of the at least one test scenario that the reference attribute of the planned trajectory is better than the reference attribute of the real trajectory; and
An information presentation module configured to present a first visual representation of the planned trajectory and a second visual representation of a driving cost of the planned trajectory;
An input receiving module configured to receive a second user input in response to presenting the second visual representation; a factor adjustment module configured to adjust at least one factor of the plurality of factors based on the second user input; a track updating module configured to update the planned track generated for the virtual vehicle based on the adjusted at least one factor; a difference presentation module configured to present a third visual representation of a difference between the planned trajectory and the updated planned trajectory.
7. The apparatus of claim 6, wherein the constraint is for constraining at least one of:
the geometry of the road on which the autonomous vehicle is traveling,
The speed of the vehicle on the road, or
The distance of the autonomous vehicle relative to the other vehicles on the road.
8. The apparatus of claim 6, wherein the information presentation module comprises:
an interface element presentation module configured to present a plurality of interface elements corresponding to the plurality of factors; and
A numerical presentation module configured to present, in association with each interface element of the plurality of interface elements, a contribution of a corresponding factor to the running cost of the planned trajectory.
9. The apparatus of claim 6, wherein the travel data comprises travel information and environmental information.
10. The apparatus of claim 6, wherein the first visual representation comprises a line depicting an orientation of a planned path, and a velocity, acceleration, attitude angle at each point in the planned trajectory.
11. An electronic device, the device comprising:
One or more processors; and
Storage means for storing one or more programs that when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1-5.
12. A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the method of any of claims 1-5.
13. A computer program product comprising computer executable instructions which, when executed by a processor, implement the method of any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110404324.1A CN112965917B (en) | 2021-04-15 | 2021-04-15 | Test method, device, equipment and storage medium for automatic driving |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110404324.1A CN112965917B (en) | 2021-04-15 | 2021-04-15 | Test method, device, equipment and storage medium for automatic driving |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112965917A CN112965917A (en) | 2021-06-15 |
| CN112965917B true CN112965917B (en) | 2024-08-27 |
Family
ID=76280603
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110404324.1A Active CN112965917B (en) | 2021-04-15 | 2021-04-15 | Test method, device, equipment and storage medium for automatic driving |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112965917B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113268428B (en) * | 2021-06-16 | 2023-08-18 | 一汽解放汽车有限公司 | Test method and system of fleet management system, storage medium and electronic device |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112230632A (en) * | 2020-10-28 | 2021-01-15 | 北京航迹科技有限公司 | Method, apparatus, device and storage medium for automatic driving |
| CN112577506A (en) * | 2020-10-30 | 2021-03-30 | 上汽大众汽车有限公司 | Automatic driving local path planning method and system |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190286151A1 (en) * | 2018-03-14 | 2019-09-19 | GM Global Technology Operations LLC | Automated driving systems and control logic for cloud-based scenario planning of autonomous vehicles |
| CN109764881B (en) * | 2018-12-07 | 2021-05-07 | 北京掌行通信息技术有限公司 | Unmanned vehicle testing method and device, electronic equipment and medium |
| CN112513766A (en) * | 2020-02-26 | 2021-03-16 | 深圳市大疆创新科技有限公司 | Method, tracking device, storage medium and computer program product for path planning |
| CN112417756B (en) * | 2020-11-13 | 2023-11-17 | 清华大学苏州汽车研究院（吴江） | Interactive simulation test system of automatic driving algorithm |
-
2021
- 2021-04-15 CN CN202110404324.1A patent/CN112965917B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112230632A (en) * | 2020-10-28 | 2021-01-15 | 北京航迹科技有限公司 | Method, apparatus, device and storage medium for automatic driving |
| CN112577506A (en) * | 2020-10-30 | 2021-03-30 | 上汽大众汽车有限公司 | Automatic driving local path planning method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112965917A (en) | 2021-06-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109649402B (en) | Autonomous vehicle policy generation | |
| US11840259B2 (en) | Method for simulating obstacle in unmanned simulation scene | |
| CN111625950B (en) | Automatic driving simulation scene reconstruction method, device, equipment and medium | |
| CN110209146B (en) | Test method, device and equipment for automatic driving vehicle and readable storage medium | |
| JP7274515B2 (en) | Sensor solution determination method, device, equipment and storage medium | |
| US20220343157A1 (en) | Robust reinforcement learning for continuous control with model misspecification | |
| CN111652153B (en) | Scene automatic identification method and device, unmanned vehicle and storage medium | |
| US20210398360A1 (en) | Generating Content Based on State Information | |
| CN114355793A (en) | Training method and device of automatic driving planning model for vehicle simulation evaluation | |
| JP2022538275A (en) | Parameter arrangement method and device, electronic device and storage medium | |
| CN112965917B (en) | Test method, device, equipment and storage medium for automatic driving | |
| US20210269058A1 (en) | Automatic scenario generator using a computer for autonomous driving | |
| CN114506343A (en) | Trajectory planning method, device, equipment, storage medium and automatic driving vehicle | |
| CN115454843A (en) | Unmanned simulation device and automatic test method | |
| CN112230632B (en) | Method, apparatus, device and storage medium for automatic driving | |
| Liu et al. | Dynamic motion planner with trajectory optimisation for automated highway lane‐changing driving | |
| Avilés et al. | Probabilistic logic Markov decision processes for modeling driving behaviors in self-driving cars | |
| CN108944921A (en) | A kind of longitudinally controlled method and apparatus for vehicle | |
| CN113343425A (en) | Simulation test method and device | |
| CN111738046B (en) | Method and apparatus for calibrating a physics engine of a virtual world simulator for learning of a deep learning-based apparatus | |
| US20140089234A1 (en) | Interactive visualization of multi-objective optimization | |
| CN116795720A (en) | Unmanned driving system credibility evaluation method and device based on scene | |
| Baumfalk et al. | A SUMO extension for norm-based traffic control systems | |
| US20240202393A1 (en) | Motion planning | |
| Krupitzer et al. | A modular simulation framework for analyzing platooning coordination |
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 |