CN113156967A - Data acquisition method, equipment and system based on self-cognition mode - Google Patents
Data acquisition method, equipment and system based on self-cognition mode Download PDFInfo
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- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
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- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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Abstract
The invention provides a data acquisition method based on a self-cognition mode, which is applied to a vehicle terminal, wherein the vehicle terminal starts to run an intelligent driving main task and receives acquired environment information, and self-cognition service of the vehicle terminal runs in a background no-load mode; when the vehicle-mounted terminal runs an intelligent driving main task, analyzing the environmental information to output an analysis result, triggering a set service in a vehicle, and sending the analysis result to the self-cognition service; the self-cognition service receives the environmental information and the analysis result, monitors driving data in the vehicle driving process, and analyzes whether the driving data in the period is different from the analysis result; if yes, recording the analysis result and the environment information; and if no divergence exists, discarding the analysis result and the environment information. According to the intelligent driving task analysis method, the analysis result of the intelligent driving main task is judged through the self-cognition service, if the divergence is found, data acquisition is triggered, and long-tail data can be efficiently acquired.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to a data acquisition method, equipment and system based on a self-cognition mode.
Background
The intelligent driving technology is emerging, a large amount of data needs to be collected in the research and development of new technology, the prior art mainly focuses on data collection in the driving control and automatic driving categories, the scheme cannot be deployed and implemented aiming at most of vehicles which do not reach high intelligent driving capability at present, and data in other driving situations, such as voice input of users, are not collected in an effective mode.
Disclosure of Invention
In view of this, the invention provides a data acquisition method, device and system based on a self-cognition mode, which can effectively collect intelligent driving data.
In order to solve the technical problems, the invention adopts the following technical scheme:
the data acquisition method based on the self-cognition mode is applied to a vehicle terminal and comprises the following steps:
the vehicle-mounted terminal starts to run an intelligent driving main task and receives collected environmental information, and self-cognition service of the vehicle-mounted terminal runs in a background in a no-load mode;
when the vehicle-mounted terminal runs the intelligent driving main task, analyzing the environmental information to output an analysis result, triggering a set service in the vehicle, and sending the analysis result to a self-cognition service;
the self-cognition service receives environmental information, receives an analysis result, monitors driving data in the vehicle driving process, and analyzes whether the driving data in a monitoring period is different from the analysis result;
if the divergence exists, the vehicle-mounted terminal records the analysis result and the environment information;
and if no divergence exists, the vehicle-mounted terminal discards the analysis result and the environmental information.
Further, the environmental information includes one or more of video, voice, and the like.
Further, the driving data comprises one or more of vehicle signals, interactive behaviour, etc.
Further, when the intelligent driving main task is operated by the vehicle terminal, the environment information is analyzed to output an analysis result, a set service in the vehicle is triggered, and the analysis result is sent to the self-cognition service, including:
carrying out real-time analysis and calculation on environmental information by an algorithm in the intelligent driving main task;
the intelligent driving main task sends a signal that the algorithm starts to run to the self-cognition service;
and receiving the environment information after the self-cognition service receives the signal.
An embodiment of another aspect of the present invention provides a car terminal, including:
the main task execution module is used for acquiring and analyzing the collected environment information to output an analysis result, wherein the environment information comprises one or more of video, voice and the like;
and the self-cognition service execution module is used for receiving the environmental information and the analysis result, monitoring the driving data, judging whether the driving data and the analysis result are diverged or not, and recording the diverged analysis result and the environmental information.
Another embodiment of the present invention provides a data acquisition system, including: a sensor, a car terminal;
the sensor is used for acquiring environmental information and driving data in a driving environment;
the vehicle-mounted terminal is used for analyzing the environmental information and outputting an analysis result, and judging whether the driving data and the analysis result are diverged or not;
if the divergence exists, the on-board terminal records the analysis result and the environment information;
and if no divergence exists, the vehicle-mounted terminal discards the analysis result and the environmental information.
Further, the environmental information includes one or more of video, voice, and the like.
Further, the driving data comprises one or more of vehicle signals, interactive behaviour, etc.
Further, the vehicle terminal is also used for triggering the set service in the vehicle according to the analysis and calculation result.
Furthermore, the vehicle terminal is also used for storing the analysis calculation result and the environmental information which are diverged from the driving data.
The technical scheme of the invention at least has one of the following beneficial effects:
1. according to the method provided by the embodiment of the invention, the driving data is monitored through the self-cognition service to judge whether the analysis of the intelligent driving main task is correct or not, so that the data collection efficiency is improved;
2. the embodiment of the invention designs different strategies aiming at different tasks, collects the driving data under different task situations and can effectively expand the data source.
Drawings
Fig. 1 is an architecture diagram of a data acquisition method based on a self-cognition mode according to an embodiment of the present invention;
FIG. 2 is a flow chart of a data acquisition method based on a self-cognition mode according to an embodiment of the invention;
fig. 3 is a schematic structural diagram of a vehicle terminal according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of a data acquisition system based on a self-learning manner according to an embodiment of the present invention;
fig. 5 is a SoC block diagram of a data acquisition system based on a self-learning manner according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. 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 invention.
In the prior art, data collection in intelligent driving is mainly focused on data collection in the driving control and automatic driving categories, and aiming at the most of vehicles which do not reach high intelligent driving capability at present, the scheme can not be deployed and implemented, and the problem of long tail of data caused by the limitation of data collection is solved. The invention adopts the data acquisition method, the equipment and the system based on the self-cognition mode, and can collect the driving data under different scenes, thereby improving the data collection efficiency.
Referring to fig. 1, fig. 1 shows a system architecture scenario diagram, where the scenario diagram includes a car terminal 110 and a sensor 140, and the car terminal includes a master task execution module 120 and a self-learning service module 130. Wherein the sensor 140 may include a camera, a microphone, etc. The sensor 140 may collect environmental information, which may be video, voice, etc., and driving data of the driver, which may include behavior of the driver, such as rotating a steering wheel, turning a steering light, etc. The main task performing module 120 of the car terminal 110 acquires and analyzes the collected environment information to output an analysis result, for example, after the sensor 140 finds a red light ahead, the main task performing module 120 outputs an analysis result that the car should be parked ahead. The self-learning service execution module 130 receives the environmental information and the analysis result, monitors the driving data, and determines whether the driving data is different from the analysis result, for example, the main task execution module 120 outputs the analysis result of parking. However, if the self-learning service execution module 130 analyzes that the user does not stop according to the driving data, it is determined that the driving data is different from the analysis result. If the user stops the vehicle and the output result is just the same as that of the stopped vehicle, no analysis is considered. The split-time car machine terminal 110 records the analysis result and the environmental information collected by the sensor. According to the method, the self-cognition service execution module 130 is used for carrying out subsequent supervision and verification on the condition analyzed by the main task execution module, so that a divergent result is collected, data collection is facilitated, data can be efficiently collected, and the long-tail problem is solved.
Referring to fig. 2, fig. 2 is a flowchart of a data acquisition method based on a self-cognition mode according to an embodiment of the present invention.
As shown in fig. 1, according to a flow chart of a data acquisition method based on a self-cognition mode, the flow chart includes S210-S240, and several steps are described in detail below:
and S210, the vehicle terminal runs the intelligent driving main task and receives the environmental information.
Specifically, the vehicle-mounted terminal collects environmental information and sends the environmental information to the intelligent driving main task for processing during the driving process of the vehicle, and more specifically, the environmental information includes one or more of videos, audios and the like collected during the driving process of the vehicle, for example, image data of a front traffic light.
And S220, the self-cognition service runs in a no-load mode at the background of the vehicle terminal.
Specifically, at this time, the self-cognition service runs in a background in an idle mode, and waits for the vehicle terminal to transmit collected environment information.
And S230, analyzing the environmental information and outputting an analysis result by the intelligent driving main task.
Specifically, the algorithm in the intelligent driving main task analyzes and calculates the environmental information in real time, and sends a signal for starting the operation of the algorithm to the self-cognition service. More specifically, the algorithm in the intelligent driving main task analyzes and judges the environmental information to simulate the next processing of the driver, for example, after receiving the image data of the traffic light in front, the state of the traffic light at that time is analyzed, if the intelligent driving main task analyzes that the traffic light is the red light at that time, the judgment that the driver should stop in front is made, otherwise, the judgment that the driver will continue to pass in front is made.
Furthermore, the intelligent driving main task can trigger specific services in the vehicle, and by taking the above contents as examples, the intelligent driving main task can trigger the parking reminding service of the vehicle, so that the driver can be assisted to judge, and the driving experience is improved.
Further, the main task is driven intelligently and the analysis result is output to the self-cognition service.
And S240, receiving the environmental information and the analysis result and monitoring the driving data by the self-cognition service.
Specifically, the self-awareness service may continuously monitor the driving data of the vehicle after receiving the analysis result of the intelligent driving master task, for example, one or more of a control signal, an interactive behavior, and the like of the vehicle may be monitored, and more specifically, as exemplified in the above description, after receiving the analysis result of the vehicle that is determined based on the traffic light condition and sent by the intelligent driving master task and should be parked in front of the vehicle, the self-awareness service may start to continuously monitor the driving condition of the vehicle and monitor whether the vehicle has a brake signal.
And S250, analyzing whether the driving data in the monitoring period is different from the analysis result or not by the self-cognition service.
If yes, the self-cognition service executes S260 and records the analysis result and the environment information.
If there is no divergence, the self-learning service executes S270, discarding the analysis result and the environmental information.
The foregoing is a specific flow of a data acquisition method based on a self-cognition mode, and the following description is given by way of example, when a traffic light is encountered in front of a vehicle, a vehicle-mounted terminal detects an image of the traffic light in front, and sends the image data to an intelligent driving main task respectively for a self-cognition service, the intelligent driving main task performs analysis according to the traffic light image data collected by the vehicle, determines that the traffic light is the red light and should be parked in front at the time, and sends an analysis result to the self-cognition service, and simultaneously prompts the driver to park in front at the vehicle-mounted terminal.
The self-cognition service can continuously monitor the driving behavior within a certain time after receiving the analysis result sent by the intelligent driving main task, if the driving behavior is found to be the same as the analysis result, and the vehicle stops in front of the vehicle for waiting, the self-cognition service does not need to record data and discards the analysis result and the environmental information, otherwise, the self-cognition service can record the analysis result and the environmental information. The driving data are monitored through the self-cognition service, the analysis results of the intelligent driving main task are compared, and data collection is triggered if the intelligent driving main task is divergent, so that the efficiency of long-tail data collection can be effectively improved.
The above is a description of an example of a data acquisition method based on a self-cognition mode, and in actual use, the invention can trigger different services for different driving situations and collect long tail data generated under different driving situations. For example, the situation may be a preceding vehicle start situation, which corresponds to a preceding vehicle start reminding service, or the situation may be a driver state monitoring situation, which corresponds to a driver fatigue distraction reminding service. The present invention collects data from a variety of driving scenarios, thereby making the collected data more extensive and comprehensive.
Another embodiment of the present invention provides a car terminal, and fig. 3 shows a schematic structural diagram of the car terminal, including: a main task execution module 310, a self-learning service execution module 320;
the main task execution module is used for acquiring and analyzing the collected environment information to output an analysis result, wherein the environment information comprises one or more of video, voice and the like;
and the self-cognition service execution module is used for monitoring the driving data, judging whether the driving data is diverged from the analysis result or not, and recording the diverged analysis result and the environment information.
Specifically, the main task execution module collects different environment information through a plurality of services, for example, the environment information may be collected through an in-vehicle vision service, an out-vehicle vision service, or a voice service, and the in-vehicle vision service may capture pupil data, heartbeat data, and the like of the driver to determine whether the driver is tired of driving, taking the in-vehicle vision service as an example.
The self-cognition service execution module has self-cognition tasks corresponding to different services in the main task execution module, for example, a front vehicle started self-cognition task, a DMS self-cognition task in a vehicle, and the like, where the front vehicle started self-cognition task is taken as an example, the front vehicle started self-cognition task monitors whether a vehicle starts a following vehicle after the main task execution module outputs an analysis result that the front vehicle has started so as to determine whether the analysis result is divergent from environmental information, and if the analysis result is divergent, data collection is triggered.
In another embodiment of the present invention, a data acquisition system is provided, and fig. 4 shows a schematic structural diagram of the data acquisition system, including: sensor 410, car machine terminal 420.
The sensor 410 may include a camera, a microphone, and other devices for collecting environmental information and driving data in the driving environment;
the in-vehicle terminal 420 is configured to analyze the environmental information and output an analysis result, and determine whether there is a divergence between the driving data and the analysis result;
if the divergence exists, the on-board terminal records the analysis result and the environment information;
and if no divergence exists, the vehicle-mounted terminal discards the analysis result and the environmental information.
Furthermore, the sensors comprise one or more sensors, so that driving data under various scenes can be collected, and data collection sources can be effectively expanded.
Further, the environmental information includes one or more of video, voice, and the like. For example, it may be the driver's heart rate, the front vehicle condition, etc.
Further, the driving data comprises one or more of vehicle signals, interactive behaviour, etc. For example, it may be a vehicle start or stop signal, a driver voice input, or the like.
Further, the vehicle terminal is also used for triggering specific services in the vehicle according to the analysis and calculation result.
Furthermore, the vehicle terminal is also used for storing the analysis calculation result and the environmental information which are diverged from the driving data.
It should be noted that, for the specific functional functions of the method for executing the above embodiment by each device in the data acquisition system based on the self-cognition mode, reference may be made to the description of the above embodiment, and details are not described here again.
Referring now to fig. 5, shown is a block diagram of a SoC (System on Chip) 1300 in accordance with an embodiment of the present application. In fig. 5, similar components have the same reference numerals. In addition, the dashed box is an optional feature of more advanced socs. In fig. 5, SoC1300 includes: an interconnect unit 1350 coupled to the application processor 1310; a system agent unit 1380; a bus controller unit 1390; an integrated memory controller unit 1340; a set or one or more coprocessors 1320 which may include integrated graphics logic, an image processor, an audio processor, and a video processor; a Static Random Access Memory (SRAM) unit 1330; a Direct Memory Access (DMA) unit 1360. In one embodiment, the coprocessor 1320 includes a special-purpose processor, such as, for example, a network or communication processor, compression engine, GPGPU, a high-throughput MIC processor, embedded processor, or the like.
Included in Static Random Access Memory (SRAM) unit 1330 may be one or more computer-readable media for storing data and/or instructions. A computer-readable storage medium may have stored therein instructions, in particular, temporary and permanent copies of the instructions. The instructions may include: when executed by at least one unit in the processor, the Soc1300 may execute the data acquisition method according to the foregoing embodiment, which specifically refers to the method shown in fig. 2 in the foregoing embodiment, and details thereof are not repeated herein.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. The utility model provides a data acquisition method based on self-cognition mode, is applied to car machine terminal, its characterized in that includes:
the vehicle-mounted terminal starts to run an intelligent driving main task and receives collected environmental information, and self-cognition service of the vehicle-mounted terminal runs in a background in a no-load mode;
when the vehicle-mounted terminal runs the intelligent driving main task, analyzing the environmental information to output an analysis result, triggering a set service in a vehicle, and sending the analysis result to the self-cognition service;
the self-cognition service receives the environmental information, receives the analysis result, monitors driving data in the vehicle driving process, and analyzes whether the driving data in the monitoring period is different from the analysis result;
if the divergence exists, the vehicle-mounted terminal records the analysis result and the environment information;
and if no divergence exists, the vehicle-mounted terminal discards the analysis result and the environmental information.
2. The self-cognition based data collection method according to claim 1, wherein the environment information comprises one or more of video, voice and the like.
3. The self-cognition based data collection method according to claim 1 wherein the driving data includes one or more of vehicle signals, interactive behavior, etc.
4. The data acquisition method based on the self-cognition mode according to claim 1, wherein when the vehicle terminal runs an intelligent driving main task, the vehicle terminal analyzes the environmental information to output an analysis result, triggers a set service in a vehicle, and sends the analysis result to the self-cognition service, and the method comprises the following steps:
the algorithm in the intelligent driving main task analyzes and calculates the environmental information in real time;
the intelligent driving main task sends a signal that an algorithm starts to run to the self-cognition service;
and the self-cognition service receives the environment information after receiving the signal.
5. The utility model provides a car machine terminal which characterized in that includes:
the main task execution module is used for acquiring and analyzing the collected environment information to output an analysis result, wherein the environment information comprises one or more of video, voice and the like;
and the self-cognition service execution module is used for receiving the environmental information and the analysis result, monitoring driving data, judging whether the driving data is diverged from the analysis result, and recording the diverged analysis result and the environmental information.
6. A data acquisition system, comprising: a sensor, a car terminal;
the sensor is used for acquiring environmental information and driving data in a driving environment;
the vehicle-mounted terminal is used for analyzing the environmental information and outputting an analysis result, and judging whether the driving data and the analysis result have divergence or not;
if the divergence exists, the vehicle-mounted terminal records the analysis result and the environment information;
and if no divergence exists, the vehicle-mounted terminal discards the analysis result and the environmental information.
7. The self-cognition based data acquisition system according to claim 6 wherein the environmental information includes one or more of video, voice, etc.
8. A data acquisition system according to claim 6, wherein the driving data comprises one or more of vehicle signals, interactive behaviour etc.
9. The data acquisition system of claim 6, wherein the vehicle-mounted terminal is further configured to: and triggering set services in the vehicle according to the analysis and calculation result.
10. The self-cognition based data acquisition system according to claim 6, wherein the vehicle-mounted terminal is further configured to: and saving the analysis calculation result and the environmental information which are divergent from the driving data.
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