CN114550476A - Data processing method, vehicle management platform and computer readable storage medium - Google Patents
Data processing method, vehicle management platform and computer readable storage medium Download PDFInfo
- Publication number
- CN114550476A CN114550476A CN202111443647.8A CN202111443647A CN114550476A CN 114550476 A CN114550476 A CN 114550476A CN 202111443647 A CN202111443647 A CN 202111443647A CN 114550476 A CN114550476 A CN 114550476A
- Authority
- CN
- China
- Prior art keywords
- automatic driving
- obstacle
- point cloud
- processing method
- 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.)
- Pending
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 46
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 25
- 230000008447 perception Effects 0.000 claims abstract description 17
- 238000000034 method Methods 0.000 claims description 8
- 238000013507 mapping Methods 0.000 claims description 4
- 239000000126 substance Substances 0.000 claims description 2
- 238000012545 processing Methods 0.000 abstract description 8
- 230000000694 effects Effects 0.000 abstract description 3
- 238000012800 visualization Methods 0.000 abstract description 3
- 230000004888 barrier function Effects 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 8
- 238000001514 detection method Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 108010001267 Protein Subunits Proteins 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012806 monitoring device Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000000047 product Substances 0.000 description 1
- 239000013589 supplement Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/005—Handover processes
- B60W60/0053—Handover processes from vehicle to occupant
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/50—Barriers
Landscapes
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Medical Informatics (AREA)
- Mechanical Engineering (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- General Health & Medical Sciences (AREA)
- Transportation (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Life Sciences & Earth Sciences (AREA)
- Atmospheric Sciences (AREA)
- Human Computer Interaction (AREA)
- Automation & Control Theory (AREA)
- Traffic Control Systems (AREA)
Abstract
The application provides a data processing method, a vehicle management platform and a computer readable storage medium. The data processing method comprises the following steps: acquiring point cloud data returned by the automatic driving vehicle; acquiring barrier information in the point cloud data by adopting a preset perception algorithm; and displaying the obstacle information. Through the mode, the data processing method enables the vehicle management platform to perform sensing processing on the returned point cloud data by deploying the same sensing algorithm as that of the automatic driving vehicle on the vehicle management platform, so that the visualization effect is achieved, and the accuracy of judgment on the point cloud data by workers is improved.
Description
Technical Field
The present application relates to the field of automated driving vehicle management technologies, and in particular, to a data processing method, a vehicle management platform, and a computer-readable storage medium.
Background
An automatic driving automobile (Self-steering automobile) is also called an unmanned automobile, a computer-driven automobile or a wheeled mobile robot, and is an intelligent automobile which realizes unmanned driving through a computer system. The automatic driving automobile depends on the cooperation of artificial intelligence, visual calculation, radar, monitoring device and global positioning system, so that the computer can operate the motor vehicle automatically and safely without any active operation of human. In the driving process of the vehicle, the automatic driving system needs to know the surrounding traffic conditions and navigate according to the roads and the traffic conditions on the driving route so as to ensure the safe and normal driving of the vehicle.
In existing autopilot technology, an autopilot car typically returns point cloud data to a vehicle management platform. When the vehicle breaks down or does not meet the automatic driving condition, the remote takeover operation is carried out on the vehicle through the remote takeover platform, but for the vehicle in the remote takeover state, the remote takeover platform cannot guarantee whether the perception algorithm of the vehicle end still works normally, so that the perception of the obstacle has certain risk.
Disclosure of Invention
The application provides a data processing method, a vehicle management platform and a computer readable storage medium.
The application provides a data processing method, which comprises the following steps:
acquiring point cloud data returned by the automatic driving vehicle;
acquiring obstacle information in the point cloud data by adopting a preset perception algorithm which is the same as that of the automatic driving vehicle;
and displaying the obstacle information.
The data processing method further comprises the following steps:
acquiring a camera image returned by the automatic driving vehicle;
and mapping the obstacle information to the camera image for display.
Wherein the acquiring the camera image returned by the autonomous vehicle comprises:
acquiring a first camera image returned by the automatic driving vehicle, wherein the first camera image is acquired by the automatic driving vehicle according to a preset direction;
acquiring the obstacle direction of the obstacle based on the obstacle information, and generating a control instruction according to the obstacle direction;
and sending the control instruction to the automatic driving vehicle so as to enable the automatic driving vehicle to return a second camera image, wherein the second camera image is acquired by the automatic driving vehicle according to the control instruction and the obstacle direction.
After the obstacle information in the point cloud data is acquired by adopting a preset perception algorithm, the data processing method further comprises the following steps:
identifying a relative distance of an obstacle from the autonomous vehicle based on the obstacle information;
judging whether the relative distance is smaller than a first distance threshold value;
and if so, based on the prompt warning information.
Wherein the identifying a relative distance of an obstacle from the autonomous vehicle based on the obstacle information, the data processing method further comprises:
judging whether the relative distance is smaller than a second distance threshold value, wherein the second distance threshold value is smaller than the first distance threshold value;
and if so, carrying out emergency danger avoidance on the automatic driving vehicle based on the obstacle information.
Wherein the point cloud data comprises millimeter wave radar data, ultrasonic radar data and/or laser radar data.
Wherein, the point cloud data returned by the automatic driving vehicle is obtained, and the method comprises the following steps:
acquiring point cloud data in a preset distance range returned by the automatic driving vehicle;
the acquiring of the point cloud data returned by the automatic driving vehicle within the preset distance range comprises the following steps:
acquiring the real-time driving speed of the automatic driving vehicle;
judging whether the real-time driving speed is greater than or equal to a preset driving speed or not;
if yes, indicating the automatic driving vehicle to return point cloud data within a first preset distance range;
and if not, indicating the automatic driving vehicle to return point cloud data in a second preset distance range, wherein the second preset distance range is smaller than the first preset distance range.
The application also provides a vehicle management platform, which comprises a data acquisition module, an obstacle sensing module and an obstacle display module; wherein the content of the first and second substances,
the data acquisition module is used for acquiring point cloud data returned by the automatic driving vehicle;
the obstacle sensing module is used for acquiring obstacle information in the point cloud data by adopting a preset sensing algorithm;
and the obstacle display module is used for displaying the obstacle information.
The present application further provides another vehicle management platform, which includes a processor and a memory, wherein the memory stores program data, and the processor is used for executing the program data to realize the data processing method.
The present application also provides a computer-readable storage medium for storing program data which, when executed by a processor, is used to implement the data processing method described above.
The beneficial effect of this application is: the vehicle management platform acquires point cloud data returned by the automatic driving vehicle; acquiring barrier information in the point cloud data by adopting a preset perception algorithm; and displaying the obstacle information. Through the mode, the data processing method enables the vehicle management platform to perform sensing processing on the returned point cloud data by deploying the same sensing algorithm as that of the automatic driving vehicle on the vehicle management platform, so that the visualization effect is achieved, and the accuracy of judgment on the point cloud data by workers is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a data processing method provided herein;
FIG. 2 is a schematic flow chart diagram illustrating another embodiment of a data processing method provided herein;
FIG. 3 is a schematic flow chart diagram illustrating a data processing method according to another embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of an embodiment of a vehicle management platform provided herein;
FIG. 5 is a schematic structural diagram of another embodiment of a vehicle management platform provided herein;
FIG. 6 is a schematic structural diagram of an embodiment of a computer-readable storage medium provided in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The realization of the automatic driving function of the vehicle depends on the normal operation of each module of the vehicle, the remote connection is a means for operating the vehicle when the vehicle cannot enter an automatic driving mode, and in the state of the remote connection, the remote connection end cannot ensure whether a vehicle end sensing algorithm normally operates, and certain risk exists when the vehicle end directly operates based on the sensing result of the vehicle end. For the sensors and the algorithm, the stability of the bottom layer sensors is generally higher than that of the algorithm, and therefore, the safety of remote operation based on the data of the sensors transmitted back by the vehicle end is higher than the sensing result transmitted back by the vehicle end.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a data processing method according to an embodiment of the present disclosure.
The data processing method is applied to a vehicle management platform, wherein the vehicle management platform can be a server, and can also be a system formed by the server and terminal equipment in a mutual matching mode. Accordingly, each part, such as each unit, sub-unit, module, and sub-module, included in the vehicle management platform may be entirely disposed in the server, or may be disposed in the server and the terminal device, respectively.
Further, the server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of software or software modules, for example, software or software modules for providing a distributed server, or may be implemented as a single software or software module, which is not limited herein. In some possible implementations, the data processing method of the embodiments of the present application may be implemented by a processor calling computer readable instructions stored in a memory.
Specifically, as shown in fig. 1, the data processing method in the embodiment of the present application specifically includes the following steps:
step S11: and acquiring point cloud data returned by the automatic driving vehicle.
In the embodiment of the present application, different types of sensors are mounted on the autonomous vehicle, for example, a laser radar, an ultrasonic radar, a millimeter wave radar, and the like, and correspondingly, the point cloud data returned by the autonomous vehicle to the vehicle management platform includes but is not limited to: millimeter wave radar data, ultrasonic radar data, laser radar data, and the like.
The point cloud data may reflect the motion of the content detected by the sensor through a time sequence relationship, and may reflect the position information of each position point in the detection area, and a set of position points, such as shape information of the point cloud. Because the point cloud data can embody the three-dimensional information of the detection area, compared with a camera image, the point cloud data can only embody two-dimensional information, the point cloud data has a more visual effect on the obstacle detection, and the detection accuracy is higher. Therefore, in the embodiment of the present application, the vehicle management platform selects the point cloud data to detect the obstacle information of the detection area.
Step S12: and acquiring obstacle information in the point cloud data by adopting a preset perception algorithm which is the same as that of the automatic driving vehicle.
In the embodiment of the application, the vehicle management platform detects the obstacle information reflected in the point cloud data by adopting a preset perception algorithm. The vehicle management platform is also provided with the same perception algorithm as that of the automatic driving vehicle, so that the obstacle detection result of the vehicle management platform is suitable for the automatic driving vehicle. The pre-set perception algorithm includes, but is not limited to: heuristic Ncut, a deep learning algorithm CNNSeg, etc. are suitable for the current mature perception algorithms, which are not listed here.
The obstacle information specifically includes the position of the obstacle, that is, the data point set constituting the obstacle, the shape of the obstacle, the movement condition of the obstacle, and the like.
Step S13: and displaying the obstacle information.
In the embodiment of the application, after the vehicle management platform detects the obstacle information in the point cloud data, the obstacle information is displayed on the display device of the platform so that an operator can visually judge the obstacle condition, and therefore remote management of the automatic driving vehicle is better achieved.
Further, the display device can only display the information of the obstacles, namely directly display the distance between the obstacles and the automatic driving vehicle and the like, so that an operator can conveniently and directly judge the vehicle condition; the display device can also display the whole point cloud data, and highlight the obstacle information in the point cloud data, such as changing the display color of a data point set forming the obstacle, or using a marking frame to display the specific position of the obstacle in the point cloud data, and displaying the distance between the obstacle and the automatic driving vehicle on the marking frame in real time, so that an operator can basically know the whole vehicle condition.
In the embodiment of the application, a vehicle management platform acquires point cloud data returned by an automatic driving vehicle; acquiring obstacle information in the point cloud data by adopting a preset perception algorithm which is the same as that of the automatic driving vehicle; and displaying the obstacle information. Through the mode, the data processing method enables the vehicle management platform to perform sensing processing on the returned point cloud data by deploying the same sensing algorithm as that of the automatic driving vehicle on the vehicle management platform, so that the visualization effect is achieved, and the accuracy of judgment on the point cloud data by workers is improved.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating a data processing method according to another embodiment of the present disclosure.
Specifically, as shown in fig. 2, the data processing method in the embodiment of the present application specifically includes the following steps:
step S21: and acquiring point cloud data returned by the automatic driving vehicle.
Step S22: and acquiring obstacle information in the point cloud data by adopting a preset perception algorithm which is the same as that of the automatic driving vehicle.
In the embodiment of the present application, steps S21 to S22 are the same as steps S11 to S12 in the embodiment described in fig. 1, and are not described again here.
Step S23: and acquiring a camera image returned by the automatic driving vehicle.
In the embodiment of the application, the vehicle management platform can acquire the camera image returned by the automatic driving vehicle besides the point cloud data returned by the automatic driving vehicle. Compared with point cloud data, the camera image can be directly displayed, data processing procedures such as perception processing and the like are not needed, and vehicle conditions can be visually displayed.
Further, since the vehicle management platform needs to connect a plurality of autonomous vehicles at the same time, for one autonomous vehicle, network resources between the autonomous vehicle and the vehicle management platform are limited. The automatic driving vehicle can acquire camera images in all directions of the automatic driving vehicle through the carried omnibearing camera, if the camera images in all directions are transmitted, a large amount of network resources are occupied, and a large burden is caused on the processing capacity of a vehicle management platform. Therefore, under a normal condition of the automatic driving vehicle, only the first camera image in the preset direction needs to be returned, wherein the preset direction can be preset in advance by an operator, and can be an important acquisition direction for the automatic driving vehicle, such as the front direction of the vehicle.
At this time, the vehicle management platform may also need to provide a camera image of the obstacle appearing direction back to the autonomous vehicle according to the obstacle information, so that the operator can make a judgment. Specifically, the point cloud data can embody the omnibearing vehicle condition of the automatic driving vehicle, and after the vehicle management platform detects the obstacle information, the vehicle management platform can acquire the direction in which the obstacle appears, namely the obstacle direction according to the obstacle information. Then, the vehicle management platform sends a control instruction to the autonomous vehicle, and the autonomous vehicle is instructed to supplement and return the second camera image in the obstacle direction so as to assist the operator in judging.
Step S24: and mapping the obstacle information to a camera image for display.
In the embodiment of the application, even if the obstacle information is identified, the problem that the vehicle condition cannot be visually displayed on the whole point cloud data cannot be solved. Therefore, the vehicle management platform can map the obstacle information onto the camera image for display. Specifically, the vehicle management platform may approve the device setting direction of the sensor and the device setting direction of the camera to obtain a camera image of the direction in which the obstacle is located, and then calibrate the position of the obstacle on the point cloud data and the position of the obstacle on the camera image, so that the mapping display may be completed.
Referring to fig. 3, fig. 3 is a schematic flowchart illustrating a data processing method according to another embodiment of the present disclosure.
Specifically, as shown in fig. 3, the data processing method in the embodiment of the present application specifically includes the following steps:
step S31: and acquiring point cloud data returned by the automatic driving vehicle.
In this embodiment, the vehicle management platform may further indicate point cloud data within a preset distance range returned by the autonomous vehicle.
Specifically, the vehicle management platform obtains the real-time driving speed of the autonomous vehicle from the autonomous vehicle, and then determines whether the real-time driving speed is greater than or equal to a preset driving speed, for example, the preset driving speed may be set to 60km/h, or other values of driving data. When the real-time driving speed is greater than or equal to the preset driving speed, the speed of the automatic driving vehicle is determined to be high, and in order to improve the safety of remote take-over of the vehicle management platform, the automatic driving vehicle needs to return point cloud data in a large range, namely the first preset distance range. For example, the first preset distance range may be set to a 50m range. When the real-time driving speed is lower than the preset driving speed, the speed of the automatic driving vehicle is confirmed to be slower, and in order to reduce the data volume returned by the automatic driving vehicle, the automatic driving vehicle can return point cloud data in a small range, namely, in a second preset distance range. For example, the second preset distance range may be set to a 30m range.
Step S32: and acquiring obstacle information in the point cloud data by adopting a preset perception algorithm which is the same as that of the automatic driving vehicle.
In the embodiment of the present application, steps S31 to S32 are the same as steps S11 to S12 in the embodiment described in fig. 1, and are not described again here.
Step S33: based on the obstacle information, a relative distance of the obstacle from the autonomous vehicle is identified.
In the embodiment of the application, the obstacle information is extracted from the point cloud data, the point cloud data can represent three-dimensional data of the position point, and the obstacle information can also represent three-dimensional data of the obstacle. Therefore, the vehicle management platform can calculate the relative distance between the obstacle and the automatic driving vehicle according to the obstacle information.
Step S34: and judging whether the relative distance is smaller than a first distance threshold value.
In the embodiment of the present application, the vehicle management platform determines whether the relative distance between the obstacle and the autonomous vehicle is smaller than a first distance threshold, and if so, determines that the current driving situation of the autonomous vehicle is at risk and needs to remind the rider of the autonomous vehicle, and then proceeds to step S35. The first distance threshold is a risk driving limit set by an empirical rule, and if the risk driving limit is exceeded, the automatic driving vehicle is considered to have a certain driving risk, and a passenger or an operator needs to be reminded to take over in advance.
Step S35: and prompting warning information.
In the embodiment of the application, when the vehicle management platform judges that the relative distance between the obstacle and the automatic driving vehicle is smaller than the first distance threshold, the automatic driving vehicle is considered to have a collision risk, an operator needs to be reminded of paying attention to the obstacle through warning information in time, and obstacle avoidance preparation is made in advance.
Step S36: and judging whether the relative distance is smaller than a second distance threshold value.
In the embodiment of the present application, the vehicle management platform determines whether the relative distance between the obstacle and the autonomous vehicle is smaller than the second distance threshold, and if so, determines that the current driving situation of the autonomous vehicle is dangerous, and needs to take over the autonomous vehicle in time to effectively prevent the occurrence of the danger, and then proceeds to step S37. Wherein the second distance threshold is smaller than the first distance threshold, the second distance threshold is a dangerous driving limit set by empirical rules, if the dangerous driving limit is exceeded, the autonomous vehicle is considered to have a certain driving risk, and the process goes to step S37.
In this embodiment, the second distance threshold may be determined after the first distance threshold, or may be determined directly, and a specific determination sequence is not limited herein.
Step S37: and carrying out emergency danger avoidance on the automatic driving vehicle based on the obstacle information.
In the embodiment of the application, when the vehicle management platform determines that the relative distance between the obstacle and the autonomous vehicle is smaller than the second distance threshold, it is considered that the autonomous vehicle is in a collision risk at the time, and an operator or the vehicle management platform needs to perform emergency risk avoidance control on the autonomous vehicle in time. Emergency hedge control includes, but is not limited to: turning in the opposite direction of the obstacle, emergency braking, reducing the speed of the vehicle, turning on the emergency light of the vehicle, etc.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
To implement the data processing method of the foregoing embodiment, the present application further provides a vehicle management platform, and specifically refer to fig. 4, where fig. 4 is a schematic structural diagram of an embodiment of the vehicle management platform provided in the present application.
As shown in fig. 4, the vehicle management platform 400 provided by the present application includes a data acquisition module 41, an obstacle sensing module 42, and an obstacle display module 43.
The data obtaining module 41 is configured to obtain point cloud data returned by the autonomous vehicle.
And the obstacle sensing module 42 is used for acquiring obstacle information in the point cloud data by adopting a preset sensing algorithm which is the same as that of the automatic driving vehicle.
And an obstacle display module 43, configured to display the obstacle information.
In order to implement the data processing method of the foregoing embodiment, the present application further provides another vehicle management platform, and specifically refer to fig. 5, where fig. 5 is a schematic structural diagram of another embodiment of the vehicle management platform provided in the present application.
The vehicle management platform 500 of the embodiment of the present application includes a memory 51 and a processor 52, wherein the memory 51 and the processor 52 are coupled.
The memory 51 is used for storing program data, and the processor 52 is used for executing the program data to realize the data processing method described in the above embodiments.
In the present embodiment, the processor 52 may also be referred to as a CPU (Central Processing Unit). Processor 52 may be an integrated circuit chip having signal processing capabilities. The processor 52 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor 52 may be any conventional processor or the like.
In order to implement the data processing method of the above embodiment, the present application further provides a computer-readable storage medium, as shown in fig. 6, the computer-readable storage medium 600 is used for storing program data 61, and when being executed by a processor, the program data 61 is used for implementing the data processing method of the above embodiment.
The present application also provides a computer program product, wherein the computer program product comprises a computer program operable to cause a computer to perform the data processing method according to the embodiments of the present application. The computer program product may be a software installation package.
The data processing method according to the above embodiments of the present application may be stored in a device, for example, a computer readable storage medium, when the data processing method is implemented in the form of a software functional unit and sold or used as an independent product. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. A data processing method applied to a vehicle management platform is characterized by comprising the following steps:
acquiring point cloud data returned by the automatic driving vehicle;
acquiring obstacle information in the point cloud data by adopting a preset perception algorithm which is the same as that of the automatic driving vehicle;
and displaying the obstacle information.
2. The data processing method of claim 1,
the data processing method further comprises the following steps:
acquiring a camera image returned by the automatic driving vehicle;
and mapping the obstacle information to the camera image for display.
3. The data processing method of claim 2,
the acquiring of the camera image returned by the automatic driving vehicle comprises:
acquiring a first camera image returned by the automatic driving vehicle, wherein the first camera image is acquired by the automatic driving vehicle according to a preset direction;
acquiring the obstacle direction of the obstacle based on the obstacle information, and generating a control instruction according to the obstacle direction;
and sending the control instruction to the automatic driving vehicle so as to enable the automatic driving vehicle to return a second camera image, wherein the second camera image is acquired by the automatic driving vehicle according to the control instruction and the obstacle direction.
4. The data processing method of claim 1,
after the obstacle information in the point cloud data is acquired by adopting a preset perception algorithm, the data processing method further comprises the following steps:
identifying a relative distance of an obstacle from the autonomous vehicle based on the obstacle information;
judging whether the relative distance is smaller than a first distance threshold value;
and if so, prompting warning information based on the obstacle information.
5. The data processing method of claim 4,
the identifying a relative distance of an obstacle from the autonomous vehicle based on the obstacle information, the data processing method further comprising:
judging whether the relative distance is smaller than a second distance threshold value, wherein the second distance threshold value is smaller than the first distance threshold value;
and if so, carrying out emergency danger avoidance on the automatic driving vehicle based on the obstacle information.
6. The data processing method of claim 1,
the point cloud data includes millimeter wave radar data, ultrasonic radar data, and/or laser radar data.
7. The data processing method of claim 1,
the method for acquiring the point cloud data returned by the automatic driving vehicle comprises the following steps:
acquiring point cloud data in a preset distance range returned by the automatic driving vehicle;
the acquiring of the point cloud data returned by the automatic driving vehicle within the preset distance range comprises the following steps:
acquiring a real-time driving speed of the autonomous vehicle;
judging whether the real-time driving speed is greater than or equal to a preset driving speed or not;
if yes, indicating the automatic driving vehicle to return point cloud data within a first preset distance range;
and if not, indicating the automatic driving vehicle to return point cloud data in a second preset distance range, wherein the second preset distance range is smaller than the first preset distance range.
8. The vehicle management platform is characterized by comprising a data acquisition module, an obstacle sensing module and an obstacle display module; wherein the content of the first and second substances,
the data acquisition module is used for acquiring point cloud data returned by the automatic driving vehicle;
the obstacle sensing module is used for acquiring obstacle information in the point cloud data by adopting a preset sensing algorithm which is the same as that of the automatic driving vehicle;
and the obstacle display module is used for displaying the obstacle information.
9. A vehicle management platform comprising a processor and a memory, the memory having stored therein program data, the processor being configured to execute the program data to implement the data processing method of any one of claims 1 to 7.
10. A computer-readable storage medium for storing program data, which when executed by a processor, is configured to implement the data processing method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111443647.8A CN114550476A (en) | 2021-11-30 | 2021-11-30 | Data processing method, vehicle management platform and computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111443647.8A CN114550476A (en) | 2021-11-30 | 2021-11-30 | Data processing method, vehicle management platform and computer readable storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114550476A true CN114550476A (en) | 2022-05-27 |
Family
ID=81669064
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111443647.8A Pending CN114550476A (en) | 2021-11-30 | 2021-11-30 | Data processing method, vehicle management platform and computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114550476A (en) |
Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480638A (en) * | 2017-08-16 | 2017-12-15 | 北京京东尚科信息技术有限公司 | Vehicle obstacle-avoidance method, controller, device and vehicle |
| CN109714422A (en) * | 2018-12-28 | 2019-05-03 | 深圳市易成自动驾驶技术有限公司 | Computing resource sharing method, system and readable storage medium storing program for executing based on automatic Pilot |
| CN109840448A (en) * | 2017-11-24 | 2019-06-04 | 百度在线网络技术（北京）有限公司 | Information output method and device for automatic driving vehicle |
| CN110362077A (en) * | 2019-07-03 | 2019-10-22 | 上海交通大学 | Automatic driving vehicle urgent danger prevention decision system, method and medium |
| US20190361454A1 (en) * | 2018-05-24 | 2019-11-28 | GM Global Technology Operations LLC | Control systems, control methods and controllers for an autonomous vehicle |
| CN110874925A (en) * | 2018-08-31 | 2020-03-10 | 百度在线网络技术（北京）有限公司 | Intelligent road side unit and control method thereof |
| CN111045425A (en) * | 2019-12-05 | 2020-04-21 | 中国北方车辆研究所 | Auxiliary teleoperation driving method for ground unmanned vehicle |
| CN111078243A (en) * | 2018-10-18 | 2020-04-28 | 现代自动车株式会社 | Software updating system, software updating method and software updating server |
| CN111199083A (en) * | 2018-10-31 | 2020-05-26 | 百度在线网络技术（北京）有限公司 | Method and device for reproducing accident data of unmanned vehicle |
| CN111665849A (en) * | 2020-06-29 | 2020-09-15 | 北京智行者科技有限公司 | Automatic driving system |
| CN112415904A (en) * | 2019-08-23 | 2021-02-26 | 郑州宇通客车股份有限公司 | Remote control method, device and system for automatic driving vehicle |
| CN112753000A (en) * | 2020-04-27 | 2021-05-04 | 深圳市大疆创新科技有限公司 | Obstacle avoidance method, movable platform, control device and storage medium |
| CN113366399A (en) * | 2019-12-30 | 2021-09-07 | 深圳元戎启行科技有限公司 | Vehicle control method and device based on remote takeover and computer equipment |
| CN113424121A (en) * | 2019-12-31 | 2021-09-21 | 深圳元戎启行科技有限公司 | Vehicle speed control method and device based on automatic driving and computer equipment |
-
2021
- 2021-11-30 CN CN202111443647.8A patent/CN114550476A/en active Pending
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107480638A (en) * | 2017-08-16 | 2017-12-15 | 北京京东尚科信息技术有限公司 | Vehicle obstacle-avoidance method, controller, device and vehicle |
| CN109840448A (en) * | 2017-11-24 | 2019-06-04 | 百度在线网络技术（北京）有限公司 | Information output method and device for automatic driving vehicle |
| US20190361454A1 (en) * | 2018-05-24 | 2019-11-28 | GM Global Technology Operations LLC | Control systems, control methods and controllers for an autonomous vehicle |
| CN110874925A (en) * | 2018-08-31 | 2020-03-10 | 百度在线网络技术（北京）有限公司 | Intelligent road side unit and control method thereof |
| CN111078243A (en) * | 2018-10-18 | 2020-04-28 | 现代自动车株式会社 | Software updating system, software updating method and software updating server |
| CN111199083A (en) * | 2018-10-31 | 2020-05-26 | 百度在线网络技术（北京）有限公司 | Method and device for reproducing accident data of unmanned vehicle |
| CN109714422A (en) * | 2018-12-28 | 2019-05-03 | 深圳市易成自动驾驶技术有限公司 | Computing resource sharing method, system and readable storage medium storing program for executing based on automatic Pilot |
| CN110362077A (en) * | 2019-07-03 | 2019-10-22 | 上海交通大学 | Automatic driving vehicle urgent danger prevention decision system, method and medium |
| CN112415904A (en) * | 2019-08-23 | 2021-02-26 | 郑州宇通客车股份有限公司 | Remote control method, device and system for automatic driving vehicle |
| CN111045425A (en) * | 2019-12-05 | 2020-04-21 | 中国北方车辆研究所 | Auxiliary teleoperation driving method for ground unmanned vehicle |
| CN113366399A (en) * | 2019-12-30 | 2021-09-07 | 深圳元戎启行科技有限公司 | Vehicle control method and device based on remote takeover and computer equipment |
| CN113424121A (en) * | 2019-12-31 | 2021-09-21 | 深圳元戎启行科技有限公司 | Vehicle speed control method and device based on automatic driving and computer equipment |
| CN112753000A (en) * | 2020-04-27 | 2021-05-04 | 深圳市大疆创新科技有限公司 | Obstacle avoidance method, movable platform, control device and storage medium |
| CN111665849A (en) * | 2020-06-29 | 2020-09-15 | 北京智行者科技有限公司 | Automatic driving system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6800899B2 (en) | Risk-based driver assistance for approaching intersections with limited visibility | |
| JP6833630B2 (en) | Object detector, object detection method and program | |
| CN109421738B (en) | Method and apparatus for monitoring autonomous vehicles | |
| JP6841843B2 (en) | Vehicle control systems, vehicle control methods, and vehicle control programs | |
| CN111976718B (en) | Automatic parking control method and system | |
| CN109841088B (en) | Vehicle driving assistance system and method | |
| EP3715204A1 (en) | Vehicle control device | |
| CN111383480B (en) | Method, apparatus, device and medium for hazard warning of vehicles | |
| EP4089659A1 (en) | Map updating method, apparatus and device | |
| US9120377B2 (en) | Method, system and computer readable medium embodying a computer program product for determining a vehicle operator's expectation of a state of an object | |
| Kim et al. | Probabilistic threat assessment with environment description and rule-based multi-traffic prediction for integrated risk management system | |
| US20190243360A1 (en) | Mode switch controller, mode switch control system, mode switch control method, and program | |
| CN113771867A (en) | Method and device for predicting driving state and terminal equipment | |
| CN111257882B (en) | Data fusion method and device, unmanned equipment and readable storage medium | |
| KR102601134B1 (en) | Method for estimating location of vehicle | |
| JP2022048339A (en) | Information processing device | |
| CN113085900A (en) | Method for calling vehicle to travel to user position | |
| WO2024022003A1 (en) | Vehicle avoidance method and apparatus, terminal device and storage medium | |
| CN113335311B (en) | Vehicle collision detection method and device, vehicle and storage medium | |
| US20230365133A1 (en) | Lane keeping based on lane position unawareness | |
| CN114550476A (en) | Data processing method, vehicle management platform and computer readable storage medium | |
| CN116853235A (en) | Collision early warning method, device, computer equipment and storage medium | |
| CN116137655A (en) | Intelligent vehicle system and control logic for surround view enhancement | |
| CN109835328B (en) | Intelligent network-connected automobile running control method and system | |
| CN114511834A (en) | Method and device for determining prompt information, electronic equipment and storage medium |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20220527 |