A method, device and electronic device for detecting vehicle path planning in a tunnel

Abstract

The present invention provides a method, device and electronic device for detecting vehicle path planning in a tunnel. The method includes the following steps: performing data playback on a scene data file through a re-injection tool; when a test tool receives a specified signal generated by the data playback of the re-injection tool, determining the current driving mode of the vehicle based on a pre-collected working condition data set of the vehicle itself; if the current driving mode is an automatic navigation assisted driving mode, judging a first signal representing the road type in the scene data file; when the first signal characterizes the road type as a highway main road, judging a second signal representing whether to enter the tunnel in the scene data file; when the second signal is a signal characterizing entering the tunnel, detecting the current path planning method of the vehicle itself to obtain a detection result. The purpose is to solve the problems of low test efficiency and high cost of existing detection methods.

Description

Technical Field

[0001] The present invention belongs to the technical field of automobile automatic driving system testing, and specifically relates to a method, device and electronic equipment for detecting vehicle path planning in a tunnel. Background Art

[0002] In recent years, the function of autonomous driving has gradually penetrated into people's car life and gradually become inseparable from driving. As an important part of strategic emerging industries, autonomous driving is a landmark product in the process from the Internet era to the artificial intelligence era. However, with the development of autonomous driving technology, the functional architecture design is becoming more and more complex, the number of sub-functions is increasing, and the amount of data is increasing. As a result, more and more functional logic problems arise. In the automatic navigation assisted driving mode, the path planning method of the vehicle on the highway is map planning. After the vehicle enters the tunnel, due to the interference of the GPS positioning signal, the positioning result is delayed, and there may be a risk of deviation from the center of the lane and a collision. At this time, the path planning of the vehicle should be switched from map planning to lane line planning.

[0003] In terms of the path planning method for detecting vehicles after entering tunnels on highways, the actual vehicle test method is currently mainly used to verify and iteratively test ADAS algorithms. The invention patent with publication number CN111811833A discloses an actual vehicle test system for intelligent driving vehicles, including intelligent traffic participants, a test base, an intelligent driving vehicle under test, and an intelligent controller. Through this actual vehicle road test method, there are problems such as high randomness of problem verification and iterative testing, a large amount of manpower and material resources consumed, and low efficiency. Therefore, it is particularly important to find a test method that improves test efficiency and reduces test costs. Summary of the invention

[0004] In view of this, an object of the present invention is to provide a method, device and electronic equipment for detecting vehicle path planning in a tunnel, so as to solve the problems of low testing efficiency and high cost of existing detection methods.

[0005] The present invention solves the above technical problems by the following technical means:

[0006] In a first aspect, an embodiment of the present application provides a method for detecting a vehicle path planning in a tunnel, comprising:

[0007] Get the scene data file of the vehicle;

[0008] Replaying the scene data file by using a re-injection tool;

[0009] When the test tool receives the designated signal generated by the data playback performed by the reinjection tool, the current driving mode of the vehicle is determined based on the pre-collected operating condition data set of the vehicle;

[0010] When the current driving mode is the assisted driving mode with automatic navigation, judge the first signal representing the road type in the scene data file;

[0011] When the first signal characterizes that the road type is the highway main road, judge the second signal representing whether to enter a tunnel in the scene data file;

[0012] When the second signal is a signal characterizing entering a tunnel, detect the current path planning method of the vehicle to obtain a detection result, where the current path planning method is determined by the working condition data set, and the detection result includes the result that the current path planning method is lane line planning or map planning.

[0013] Combined with the first aspect, in some optional embodiments, judging the current driving mode of the vehicle includes:

[0014] Compare the third signal output by the ADAS algorithm module from the working condition data set with the pre-stored signal characterizing the driving mode as the assisted driving mode with automatic navigation. When the third signal is the same as the signal characterizing the driving mode as the assisted driving mode with automatic navigation, execute the step of judging the first signal representing the road type in the scene data file.

[0015] Combined with the first aspect, in some optional embodiments, judging the first signal representing the road type in the scene data file includes:

[0016] Compare the first signal representing the road type in the scene data file with the pre-stored signal characterizing the road type as the highway main road. When the first signal is the same as the signal characterizing the road type as the highway main road, execute the step of judging the second signal representing whether to enter a tunnel in the scene data file.

[0017] Combined with the first aspect, in some optional embodiments, judge the second signal representing whether to enter a tunnel in the scene data file;

[0018] Compare the second signal representing whether to enter a tunnel in the scene data file with the pre-stored signal characterizing whether to enter a tunnel. When the second signal is the same as the signal characterizing entering a tunnel, execute the step of detecting the current path planning method of the vehicle.

[0019] Combined with the first aspect, in some optional embodiments, detecting the current path planning method of the vehicle to obtain a detection result includes:

[0020] Compare the fourth signal output by the ADAS algorithm module from the working condition data set with the pre - stored signals representing lane line planning and map planning. Among them, when the fourth signal is the same as the signal representing lane line planning or the signal representing map planning, output the detection result.

[0021] Combined with the first aspect, in some alternative embodiments, the method further includes:

[0022] When the current path planning method in the detection result is lane line planning, determine that the detection result is a result meeting expectations;

[0023] When the current path planning method in the detection result is map planning, determine that the detection result is a result not meeting expectations.

[0024] In a second aspect, an embodiment of the present application further provides a detection device for vehicle path planning in a tunnel, including:

[0025] An acquisition unit, configured to acquire the scenario data file of the vehicle itself;

[0026] A playback unit, configured to perform data playback on the scenario data file through a re - injection tool;

[0027] A determination unit, configured to determine the current driving mode of the vehicle itself based on the pre - collected working condition data set of the vehicle itself when the test tool receives a specified signal generated by the re - injection tool for the data playback;

[0028] A first judgment unit, configured to judge the first signal representing the road type in the scenario data file if the current driving mode is the automatic navigation assisted driving mode;

[0029] A second judgment unit, configured to judge the second signal representing whether entering the tunnel in the scenario data file when the first signal represents that the road type is the highway main road;

[0030] A detection unit, configured to detect the current path planning method of the vehicle itself to obtain a detection result when the second signal is a signal representing entering the tunnel, where the current path planning method is determined by the working condition data set, and the detection result includes the result that the current path planning method is lane line planning or map planning.

[0031] Combined with the second aspect, in some alternative embodiments, the detection unit is further configured to:

[0032] When the current path planning method in the detection result is lane line planning, determine that the detection result is a result meeting expectations;

[0033] When the current path planning method in the detection result is map planning, it is determined that the detection result does not meet the expected result.

[0034] In a third aspect, an embodiment of the present application further provides an electronic device, which includes a processor and a memory coupled to each other. A computer program is stored in the memory. When the computer program is executed by the processor, the electronic device is enabled to execute the above method.

[0035] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium. A computer program is stored in the computer-readable storage medium. When the computer program runs on a computer, the computer is enabled to execute the above method.

[0036] The present invention has the following advantages:

[0037] First, obtain the scenario data file of the vehicle itself, and then use the re-injection tool to perform data playback on the scenario data file. When the test tool receives a specified signal generated by the re-injection tool for data playback, determine the current driving mode of the vehicle itself based on the pre-collected working condition data set of the vehicle itself. If the current driving mode is the automatic navigation assisted driving mode, perform logical judgments on the first signal and the second signal obtained from the scenario data file in sequence. After the signals pass through the judgment steps of road type and whether to enter the tunnel, if the judgment results show that the first signal and the second signal are the same as the pre-stored signals representing the highway main road for road type and the pre-stored signal representing entering the tunnel respectively, then perform the last step, detect the path planning method. If it is detected that the fourth signal is the same as the pre-stored signal representing lane line planning, that is, the detection result meets the expected result. If it is detected that the fourth signal is the same as the pre-stored signal representing map planning, then the detection result does not meet the expected result; the method for detecting whether the path planning method of the vehicle in the tunnel is correct through re-injection testing can reuse the problem scenario data of the actual vehicle, based on this scenario, prevent the problems that have occurred from happening again, support problem reproduction, regression testing, and multiple rounds of version iteration testing, shorten the problem verification time, improve the testing efficiency, reduce the consumption of manpower and material resources, and reduce costs. Description of the Drawings

[0038] The present invention can be further illustrated by the non-limiting embodiments given in the drawings:

[0039] Figure 1 is a schematic flowchart of a method for detecting the path planning of a vehicle in a tunnel provided by an embodiment of the present invention;

[0040] Figure 2 is a block diagram of a device for detecting the path planning of a vehicle in a tunnel provided by an embodiment of the present invention.

[0041] Icons: 210 - Acquisition Unit; 220 - Playback Unit; 230 - Determination Unit; 240 - First Judgment Unit; 250 - Second Judgment Unit; 260 - Detection Unit. Detailed Implementation Manner

[0042] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that in the description of the drawings or the specification, similar or identical parts are all denoted by the same reference numerals, and the implementation manners not depicted or described in the drawings are the forms known to those of ordinary skill in the art. In the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0043] An embodiment of the present application provides an electronic device. The electronic device includes a processor and a memory coupled to each other. A computer program is stored in the memory. When the computer program is executed by the processor, the electronic device can execute the corresponding steps in the following vehicle path planning detection method in a tunnel.

[0044] The electronic device may be, but is not limited to, devices such as a personal computer, a server, etc. The processor and the memory are electrically connected. The processor is used to execute the computer program in the memory, and both the processor and the memory are existing hardware modules, so details are not elaborated herein.

[0045] As Figure 1 shown, the present application also provides a vehicle path planning detection method in a tunnel, which can be applied to the above-mentioned electronic device and executed or implemented by the electronic device for each step of the method. Among them, the vehicle path planning detection method in a tunnel may include the following steps:

[0046] Step 100, obtain the scene data file of the vehicle itself;

[0047] Step 110, perform data playback on the scene data file through a re-injection tool;

[0048] Step 120, when the test tool receives a specified signal generated by data playback by the re-injection tool, determine the current driving mode of the vehicle itself based on the pre-collected working condition data set of the vehicle itself;

[0049] Step 130, if the current driving mode is the assisted driving mode with automatic navigation, judge the first signal representing the road type in the scene data file;

[0050] Step 140, when the first signal characterizes that the road type is the main expressway, judge the second signal representing whether to enter the tunnel in the scene data file;

[0051] Step 150: When the second signal indicates entering the tunnel, detect the current path planning method of the vehicle to obtain a detection result. Here, the current path planning method is determined by the working condition data set, and the detection result includes the result that the current path planning method is lane line planning or map planning.

[0052] The following will elaborate on each step of the vehicle path planning detection method in the tunnel in detail as follows:

[0053] In step 100, an electronic device can pre-copy a scenario data file of the vehicle.

[0054] In step 110, the electronic device can place the scenario data file obtained in step 100 in the specified path of the replay tool in a virtual machine environment of a specified version, and then run the replay tool to start data playback; in this step, the scenario data file is input into the ADAS (Advanced Driver Assistance System) algorithm module integrated in the replay tool as the input of the ADAS algorithm module. The ADAS algorithm module receives the scenario data file and performs algorithm processing to output the corresponding operation result. Here, the replay tool is a common tool for data playback.

[0055] Before data playback, based on a test tool developed in Python (a computer programming language), develop a test script locally using the Python language. After the test script is developed, transplant the test tool containing the test script to a virtual machine environment of a specified version, and at the same time, compile and run the test tool. The test tool can be used to analyze and process the working condition data set of the vehicle.

[0056] For example, in step 120, when the pre-compiled and running test tool receives a specified signal generated by the data playback performed by the replay tool, use the test tool to determine the current driving mode of the vehicle based on the pre-collected working condition data set of the vehicle. In this embodiment, the current driving mode is a prior art and is a mode of intelligent driving, such as IACC (Integrated Adapted Cruise Control) and NID (Pilot Intelligent Driving Assistance System), and can compare the third signal output by the ADAS algorithm module according to the working condition data set with a pre-stored signal indicating that the driving mode is the automatic navigation assistance driving mode. Here, when the third signal is the same as the signal indicating that the driving mode is the automatic navigation assistance driving mode, execute step 130 to judge the first signal representing the road type in the scenario data file.

[0057] For example, the pre-stored signal value representing the driving mode as the automatic navigation assisted driving mode is 0. If the third signal value is 0, it indicates that the current driving mode is the automatic navigation assisted driving mode, and step 130 of judging the first signal representing the road type in the scene data file is continued; if the third signal value is not 0, the current driving mode is not the automatic navigation assisted driving mode, and step 120 is restarted.

[0058] When entering step 130, the first signal representing the road type in the scene data file is compared with the pre-stored signal representing the road type as the highway main road by using an electronic device. Among them, when the first signal is the same as the signal representing the road type as the highway main road, step 140 is executed to judge the second signal representing whether to enter the tunnel in the scene data file; when the first signal is not the same as the signal representing the road type as the highway main road, step 120 is returned and restarted from step 120.

[0059] For example, a signal value of 1 represents the highway main road, a signal value of 2 represents the urban expressway, and a signal value of 3 represents the ramp. If the first signal value is 1, it indicates that the road type is the highway main road, and the step of judging the second signal representing whether to enter the tunnel in the scene data file is continued; if the first signal value is not 1, but 2 or 3, it indicates that the road type is not the highway main road, and step 120 is returned and restarted from step 120.

[0060] When entering step 140, the second signal representing whether to enter the tunnel in the scene data file is compared with the pre-stored signal representing whether to enter the tunnel by using an electronic device. Among them, when the second signal is the same as the signal representing entering the tunnel, step 150 is executed to detect the current path planning method of the vehicle; when the second signal is not the same as the signal representing entering the tunnel, step 120 is returned and restarted from step 120.

[0061] For example, if the signal representing entering the tunnel is the signal of the distance of the vehicle from the tunnel, and its signal value is less than or equal to 0, it indicates that the vehicle starts to enter the tunnel, and step 150 of detecting the current path planning method of the vehicle is continued; if the signal value of the distance of the vehicle from the tunnel is greater than 0, it indicates that the vehicle has not entered the tunnel, and step 120 is returned and restarted from step 120.

[0062] When entering step 150, the fourth signal output by the ADAS algorithm module of the working condition data set is compared with the pre-stored signal representing the lane line planning and the signal representing the map planning by using an electronic device, wherein when the fourth signal is the same as the signal representing the lane line planning, the detection result is determined to be a result that meets the expectations; when the fourth signal is the same as the signal representing the map planning, the detection result is determined to be a result that does not meet the expectations. In this embodiment, the implementation principle of inferring the current path planning method is the prior art, and the corresponding fourth signal can be output by the ADAS algorithm module according to the working condition data set, and compared with the pre-stored signal representing the lane line planning and the signal representing the map planning, so it will not be repeated here.

[0063] For example, a signal value of 4 represents lane line planning, and a signal value of 5 represents map planning. If the fourth signal value is 4, it indicates that the current path planning method is lane line planning, and the detection result is determined to be in line with the expected result; if the fourth signal value is 5, it indicates that the current path planning method is map planning, and the detection result is determined to be not in line with the expected result.

[0064] The present application also provides a vehicle path planning detection device in a tunnel, the vehicle path planning detection device in a tunnel includes at least one software function module stored in a storage module or fixed in an operating system in the form of software or firmware. The processor is used to execute executable modules stored in the storage module, such as the software function modules and computer programs included in the vehicle path planning detection device in a tunnel.

[0065] like Figure 2 As shown, the vehicle path planning detection device in the tunnel includes an acquisition unit 210, a playback unit 220, a determination unit 230, a first judgment unit 240, a second judgment unit 250 and a detection unit 260, and the functions of each unit may be as follows:

[0066] An acquisition unit 210 is used to acquire a scene data file of the vehicle;

[0067] A playback unit 220, used to play back the scene data file through a re-injection tool;

[0068] The determination unit 230 is used to determine the current driving mode of the vehicle based on the pre-collected working condition data set of the vehicle when the test tool receives the designated signal generated by the data playback of the reinjection tool;

[0069] A first judgment unit 240 is used to judge the first signal indicating the road type in the scene data file if the current driving mode is the automatic navigation assisted driving mode;

[0070] The second determination unit 250 is configured to determine a second signal indicating whether to enter a tunnel in the scenario data file when the first signal characterizes that the road type is a highway main road;

[0071] The detection unit 260 is configured to detect the current path planning method of the vehicle itself when the second signal is a signal indicating entering a tunnel, so as to obtain a detection result, where the current path planning method is determined by a working condition data set, and the detection result includes a result that the current path planning method is lane line planning or map planning.

[0072] Optionally, the determination unit 230 can be configured to: when the current driving mode is an automatic navigation assisted driving mode, determine that a specified signal enters the first determination unit, that is, enter the loop logic determination in the test script, and jump out of the loop until the data playback is completed or the detection result is output after the script execution is completed, and the script operation ends.

[0073] For example, when the pre-compiled and running test tool receives the specified signal generated by the data playback by the re-injection tool, the test tool is used to determine the current driving mode of the vehicle itself based on the pre-collected working condition data set of the vehicle itself. In this embodiment, the current driving mode is a prior art and is a mode of intelligent driving, such as IACC, NID, and can compare the third signal output by the ADAS algorithm module according to the working condition data set with the pre-stored signal characterizing the driving mode as the automatic navigation assisted driving mode. Among them, when the third signal is the same as the signal characterizing the driving mode as the automatic navigation assisted driving mode, step 130 is executed to determine the first signal indicating the road type in the scenario data file.

[0074] For example, the signal value of the pre-stored signal characterizing the driving mode as the automatic navigation assisted driving mode is 0. If the third signal value is 0, it indicates that the current driving mode is the automatic navigation assisted driving mode, and continue to execute step 130 to determine the first signal indicating the road type in the scenario data file; if the third signal value is not 0, the current driving mode is not the automatic navigation assisted driving mode, and step 120 is restarted.

[0075] Optionally, the first determination unit 240 can be configured to: use an electronic device to compare the first signal indicating the road type in the scenario data file with the pre-stored signal characterizing the road type as the highway main road. Among them, when the first signal is the same as the signal characterizing the road type as the highway main road, step 140 is executed to determine the second signal indicating whether to enter a tunnel in the scenario data file; when the first signal is not the same as the signal characterizing the road type as the highway main road, return to step 120 and start again from step 120.

[0076] For example, a signal value of 1 represents a highway main road, a signal value of 2 represents an urban expressway, and a signal value of 3 represents a ramp. If the first signal value is 1, it indicates that the road type is a highway main road, and the step of judging the second signal indicating whether to enter a tunnel in the scene data file is continued; if the first signal value is not 1, but 2 or 3, it indicates that the road type is not a highway main road, and return to step 120 to start over from step 120.

[0077] Optionally, the second judging unit 250 can be used to: use the electronic device to compare the second signal indicating whether to enter a tunnel in the scene data file with the pre-stored signal indicating whether to enter a tunnel. Among them, when the second signal is the same as the signal indicating entering a tunnel, execute step 150 to detect the current path planning method of the vehicle; when the second signal is not the same as the signal indicating entering a tunnel, return to step 120 to start over from step 120.

[0078] For example, if the signal indicating entering a tunnel is the signal of the vehicle's distance from the tunnel, and its signal value is less than or equal to 0, it indicates that the vehicle starts to enter the tunnel, and continue to execute step 150 to detect the current path planning method of the vehicle; if the signal value of the vehicle's distance from the tunnel is greater than 0, it indicates that the vehicle has not entered the tunnel, and return to step 120 to start over from step 120.

[0079] Optionally, the detection unit 260 can be used to: use the electronic device to compare the fourth signal output by the working condition data set through the ADAS algorithm module with the pre-stored signals indicating lane line planning and map planning. Among them, when the fourth signal is the same as the signal indicating lane line planning, determine that the detection result is a result meeting expectations; when the fourth signal is the same as the signal indicating map planning, determine that the detection result is a result not meeting expectations. In this embodiment, the implementation principle of calculating the current path planning method is a prior art, and the corresponding fourth signal can be output through the ADAS algorithm module according to the working condition data set and compared with the pre-stored signals indicating lane line planning and map planning, so it will not be elaborated here.

[0080] For example, a signal value of 4 represents lane line planning, and a signal value of 5 represents map planning. If the fourth signal value is 4, it indicates that the current path planning method is lane line planning, and determine that the detection result is a result meeting expectations; if the fourth signal value is 5, it indicates that the current path planning method is map planning, and determine that the detection result is a result not meeting expectations.

[0081] The embodiment of the present application also provides a computer-readable storage medium, in which a computer program is stored. When the computer program runs on a computer, it causes the computer to execute the tunnel vehicle path planning detection method as described in the above embodiment.

[0082] Through the description of the above embodiments, those skilled in the art can clearly understand that this application can be implemented through hardware, or can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of this application can be embodied in the form of a software product, and this software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including several instructions to enable a computer device (which can be a personal computer, a control device, or a network device, etc.) to execute the methods described in various implementation scenarios of this application.

[0083] In summary, the embodiments of this application provide a method, a device, and an electronic device for detecting a vehicle path planning in a tunnel. In this solution, when it is necessary to detect the vehicle path planning method in the tunnel, the obtained scene data file of the vehicle is replayed through a re-injection tool. When the test tool receives a specified signal generated by the re-injection tool for the data replay, the current driving mode of the vehicle is determined based on the pre-collected working condition data set of the vehicle. When the current driving mode is the automatic navigation assisted driving mode, it is determined that the specified signal enters the first judgment unit to judge the first signal representing the road type in the scene data file. When the first signal is the same as the signal representing the highway main road for the road type, step 140 is executed to judge the second signal representing whether to enter the tunnel in the scene data file; when the first signal is not the same as the signal representing the highway main road for the road type, return to step 120 and start again from step 120. When the second signal is the same as the signal representing entering the tunnel, step 150 is executed to detect the current path planning method of the vehicle; when the second signal is not the same as the signal representing entering the tunnel, return to step 120 and start again from step 120. When the fourth signal is the same as the signal representing the lane line planning, it is determined that the detection result meets the expected result; when the fourth signal is the same as the signal representing the map planning, it is determined that the detection result does not meet the expected result.

[0084] In the embodiments provided in the present application, it should be understood that the disclosed devices, equipment, and methods can also be implemented in other ways. The device, equipment, and method embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architectures, functions, and operations of devices, methods, and computer program products according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the part of the module, program segment, or code contains one or more executable instructions for implementing the specified logical function. It should also be noted that each block in the block diagram and / or flowchart, as well as the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions. Additionally, in each embodiment of the present application, the various functional modules may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0085] The above description is only for the embodiments of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various changes and modifications. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A vehicle path planning and detection method in a tunnel, characterized in that, Including: Obtain the scenario data file of the vehicle itself; Perform data playback on the scenario data file through a re-injection tool; When the test tool receives a specified signal generated by the data playback performed by the re-injection tool, determine the current driving mode of the vehicle itself based on a pre-collected working condition data set of the vehicle itself; If the current driving mode is the automatic navigation assisted driving mode, judge the first signal representing the road type in the scenario data file; When the first signal characterizes that the road type is the highway main road, judge the second signal representing whether to enter the tunnel in the scenario data file; When the second signal is a signal characterizing entering the tunnel, detect the current path planning method of the vehicle itself to obtain a detection result, where the current path planning method is determined by the working condition data set, and the detection result includes the result that the current path planning method is lane line planning or map planning.

2. The method according to claim 1, wherein Judging the current driving mode of the vehicle itself includes: Compare the third signal output by the working condition data set through the ADAS algorithm module with a pre-stored signal characterizing the driving mode as the automatic navigation assisted driving mode. When the third signal is the same as the signal characterizing the driving mode as the automatic navigation assisted driving mode, perform the step of judging the first signal representing the road type in the scenario data file.

3. The method according to claim 1, wherein Judging the first signal representing the road type in the scenario data file includes: Compare the first signal representing the road type in the scenario data file with a pre-stored signal characterizing the road type as the highway main road. When the first signal is the same as the signal characterizing the road type as the highway main road, perform the step of judging the second signal representing whether to enter the tunnel in the scenario data file.

4. The method according to claim 1, wherein Judge the second signal representing whether to enter the tunnel in the scenario data file; Compare the second signal representing whether to enter the tunnel in the scenario data file with a pre-stored signal characterizing whether to enter the tunnel. When the second signal is the same as the signal characterizing entering the tunnel, perform the step of detecting the current path planning method of the vehicle itself.

5. The method according to claim 1, characterized in that, Detect the current path planning method of the vehicle itself to obtain a detection result, including: Compare the fourth signal output by the working condition data set through the ADAS algorithm module with a pre-stored signal characterizing lane line planning and a signal characterizing map planning. When the fourth signal is the same as the signal characterizing lane line planning or the signal characterizing map planning, output the detection result.

6. The method according to any one of claims 1-5, characterized in that, The method further includes: When the current path planning method in the detection result is lane line planning, determine that the detection result is a result that meets expectations; When the current path planning method in the detection result is map planning, determine that the detection result is a result that does not meet expectations.

7. A vehicle path planning and detection device in a tunnel, characterized in that, Including: An acquisition unit for acquiring the scenario data file of the vehicle itself; A playback unit for performing data playback on the scenario data file through a re-injection tool; A determination unit, configured to determine the current driving mode of the vehicle based on a pre-collected working condition data set of the vehicle when the test tool receives a specified signal generated by the data playback performed by the back-injection tool; A first judgment unit, configured to judge a first signal representing the road type in the scenario data file when the current driving mode is the automatic navigation assisted driving mode; A second judgment unit, configured to judge a second signal representing whether to enter a tunnel in the scenario data file when the first signal characterizes that the road type is a highway main road; A detection unit, configured to detect the current path planning method of the vehicle to obtain a detection result when the second signal is a signal characterizing entering a tunnel, wherein the current path planning method is determined by the working condition data set, and the detection result includes a result that the current path planning method is lane line planning or map planning.

8. The device according to claim 7, characterized in that, The detection unit is further configured to: When the current path planning method in the detection result is lane line planning, determine that the detection result is a result that meets expectations; When the current path planning method in the detection result is map planning, determine that the detection result is a result that does not meet expectations.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory that are coupled to each other, and a computer program is stored in the memory. When the computer program is executed by the processor, the electronic device executes the method according to any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, A computer program is stored in the computer-readable storage medium. When the computer program runs on a computer, the computer executes the method according to any one of claims 1-6.

Images