CN113886634B - Lane line offline data visualization method and device - Google Patents
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Abstract
The scheme relates to a lane line offline data visualization method and device, wherein the method comprises the following steps: acquiring and analyzing lane line offline data packets of each lane line sensor, and respectively storing the lane line offline data of each sensor into a first data container; carrying out time synchronization on the lane line offline data of each sensor stored in each first data container, and then transferring the data into a second data container; selecting a target time period and a lane line coefficient to be target, and extracting target lane line offline data of all sensors in the target time period from a second data container; forming a first graph of each target lane line curve coefficient of each sensor over time over a target time period; and calculating the mean value, the variance and/or the graph expected to change with time in the target time period of each target lane line curve coefficient of each sensor based on the first graph of the change with time in the target time period of each target lane line curve coefficient of each sensor, and then carrying out output display.
Description
Technical Field
The invention belongs to the technical field of automatic driving control application, and particularly relates to an automatic driving vehicle sensing fusion lane line data analysis method.
Background
In recent years, the cost of the laser radar is gradually lowered, a high-precision map basically covers national expressways, and the chip calculation power of cameras and radars is improved, so that the automatic driving technology is promoted to be rapidly improved due to the upgrading and large-scale popularization of the sensor technologies, and the automatic driving commercial mass production of more than L3 level becomes possible.
However, with the increase in the number of autopilot sensors and the increase in sensor computing power, a large amount of data, especially laser radar and cameras, is generated, and it is estimated that 1TB of data is generated every 100KM road test. The data generated by the automatic driving road test comprises information of vehicles, laser radar point clouds, camera image data, map data and the like, and how to utilize the data gold mine for improving the performance and the system robustness of an automatic driving system becomes a new problem.
The lane keeping function is the most basic and important part of the automatic driving function, and mainly aims to ensure that a vehicle runs on a structured road in the middle, and the function directly influences the safety of the whole automatic driving system, namely, the transverse deviation control can cause traffic accidents and comfortableness, and the shaking in a lane can influence the experience of passengers.
The quality of the output of the fused lane line directly influences the transverse control, and if the lane line outputs a navigation angle (the relative position of the lane line on the road surface and the vehicle) and the lane line outputs curvature fluctuation, the transverse control shake can be caused, and even serious traffic accidents are caused when the vehicle collides with the lane guardrail and the adjacent vehicle. Therefore, how to improve the robustness of the lane line output directly affects the effectiveness of lateral (steering wheel) control and the stability of the automatic driving system.
At present, the lane line mainly depends on a front view camera and a surrounding view camera to acquire images, the images are subjected to edge enhancement, binarization image processing and extraction, the lane line is finally fitted through hough change, and the lane line is characterized by a plurality of curve equations (wherein, the curve variance correlation coefficient of the lane line), so that the visual method provided by the patent can intuitively analyze and judge the false detection and omission detection problem of the lane line.
However, the existing lane line offline data analysis method has the following obvious defects:
1. only a single signal line graph is displayed in a static global mode (whole offline data), and signal changes at all time points cannot be displayed dynamically according to a time axis;
2. the numerical statistics module is not added, so that a developer is not beneficial to finding potential rules of data.
The data-based analysis must completely and correctly reflect the general view of the objective situation, and the direction of subsequent development must be made through processing, making, analyzing and researching a large amount of abundant statistical data and data under the guidance of the principle that the actual situation is. The process of data processing is a complex process, and errors can be generated from data collection to data screening and data analysis in the link, so that erroneous data needs to be screened in each link, and particularly the data processing stage can be a good process of cleaning the data.
In summary, the visualized dynamic display of the lane line data generated by the automatic driving is helpful for the developer to find the potential problems in the automatic driving system from the road test data, so as to improve the system safety, and meanwhile, the numerical statistics module can also provide the sensor lane line multidimensional data for the developer for optimizing the function development and improving the system robustness.
Disclosure of Invention
The invention aims to provide an automatic driving-based lane line data analysis method and a visualization device for developers, which are used for solving the problems of quick and visual analysis and positioning of the developers from mass data, improving the efficiency and reducing the problem processing time.
The invention adopts the technical scheme that:
the invention provides a lane line offline data visualization method, which comprises the following steps:
acquiring and analyzing lane line offline data packets of the lane line sensors to obtain lane line offline data of the lane line sensors, and respectively storing the lane line offline data of the sensors into a first data container;
performing time synchronization on the lane line offline data of each sensor stored in each first data container, and transferring the lane line offline data of each sensor stored in each first data container after time synchronization into the same second data container after adding corresponding sensor flag bits respectively;
selecting a target time period and a target lane line coefficient to be displayed, and extracting target lane line offline data of all sensors in the target time period from the second data container;
respectively establishing a Cartesian coordinate system with a horizontal axis being time and a vertical axis being a target lane line curve coefficient for each sensor, and forming a first curve graph of each target lane line curve coefficient of each sensor changing with time in the target time period;
calculating a second graph of the mean value of the target lane-line curve coefficients of each sensor over time over the target period based on a first graph of the target lane-line curve coefficients of each sensor over time over the target period, a third graph of the variance of the target lane-line curve coefficients of each sensor over time over the target period, and/or a fourth graph of the target lane-line curve coefficients of each sensor expected to change over time over the target period;
and outputting and displaying a second curve graph of the mean value of the target lane line curve coefficients of each sensor over time in the target time period, a third curve graph of the variance of the target lane line curve coefficients of each sensor over time in the target time period, and/or a fourth curve graph of the target lane line curve coefficients of each sensor expected to change over time in the target time period.
The invention also provides a lane line offline data visualization device, which comprises:
the off-line data analysis module is used for acquiring and analyzing the off-line data packets of the lane lines of the lane line sensors to obtain off-line data of the lane lines of the lane line sensors, and then respectively storing the off-line data of the lane lines of the sensors into a first data container;
the data transfer module is used for carrying out time synchronization on the lane line offline data of each sensor stored in each first data container, and then transferring the lane line offline data of each sensor stored in each first data container after the time synchronization into the same second data container after the corresponding sensor flag bit is added;
the time flow diagram module is used for selecting a target time period and target lane line coefficients to be displayed, and extracting target lane line offline data of all sensors in the target time period from the second data container; respectively establishing a Cartesian coordinate system with a horizontal axis being time and a vertical axis being a target lane line curve coefficient for each sensor, and forming a first curve graph of each target lane line curve coefficient of each sensor changing with time in the target time period;
a numerical analysis module for calculating a second graph of the mean value of the target lane-line curve coefficients of each sensor over time over the target time period based on a first graph of the target lane-line curve coefficients of each sensor over time over the target time period, a third graph of the variance of the target lane-line curve coefficients of each sensor over time over the target time period, and/or a fourth graph of the target lane-line curve coefficients of each sensor expected to change over time over the target time period;
and the visualization module is used for outputting and displaying a second curve graph of the mean value of the target lane line curve coefficients of each sensor over time in the target time period, a third curve graph of the variance of the target lane line curve coefficients of each sensor over time in the target time period and/or a fourth curve graph of the target lane line curve coefficients of each sensor expected to change over time in the target time period.
The invention has the beneficial effects that:
1. by introducing a time flow concept, the signal change can be dynamically displayed along with the time change;
2. the false detection and missing detection problem of the lane line can be intuitively analyzed and judged through the provided visualization method;
3. through the addition data statistical analysis, a developer can find out the potential rules of the sensor lane line data at multiple angles.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a block diagram of the structure of the device of the present invention.
Detailed Description
Referring to fig. 1 and 2, the invention provides an automatic driving lane line offline data analysis visualization device, which comprises the following modules:
and the offline data analysis module is used for: the method is used for loading lane line offline data packets of lane line sensors (such as a front camera and a round-the-eye camera) of an automatic driving vehicle, analyzing and storing the lane line offline data of the sensors obtained after analysis into an independent data container respectively.
In this embodiment, the lane line offline data analysis module specifically includes 3 components, which are respectively: a data loading component, a data parsing component and a data storage component. The data loading component firstly loads the lane line offline data packet to be processed; and then, a data analysis component is called to analyze the lane line offline data packets to obtain lane line offline data of each sensor, and the data analysis component mainly receives lane line offline data streams of one period according to different protocol rules (TPC/UDP/CAN) and analyzes original sensor signals. And finally, respectively storing the analyzed lane line offline data of each lane line sensor into a first data container by utilizing the data storage component. For example, if there are n lane line sensors, lane line offline data of the n analyzed lane line sensors are respectively stored in the first data sensor a 1 To A n Is a kind of medium.
And the data transfer module is used for: and (3) carrying out time synchronization on the automatic driving offline data of each lane line sensor stored in each data container, and then uniformly transferring the data into the same second data container. Meanwhile, an interface is reserved in the data transfer module, so that data cleaning is conveniently carried out on the automatic driving offline data transferred to the second data container.
The data transfer module comprises a time synchronization component and a data container component, and the logical relationship is as follows: in order to avoid the problem caused by different sending periods of different sensors, a time synchronization component is called to perform time synchronization processing on the lane line offline data in each first data container and load timeThe data container component adds corresponding sensor zone bits to each data after time synchronization, and then adds all the first data containers A after the sensor zone bits 1 All lane line offline data in An is transferred to the second data container B. In order to adapt to the requirements of different analysts, the data transfer module is separately provided with an interface, and if the data in the second data container B needs to be cleaned, the analyst only needs to load the screening rule by using the interface to finally obtain the required data.
A time flow graph module: and extracting target lane line offline data of all sensors in the target time period from the second data container according to the target time period and the target lane line coefficient to be displayed, which are preselected by a developer. And respectively establishing a Cartesian coordinate system with the horizontal axis being time and the vertical axis being a target lane line curve coefficient for each sensor, and forming a first curve graph of each target lane line curve coefficient of each sensor changing with time in the target time period.
Specifically, the time flow graph module: mainly comprises 2 components, a time window component and a drawing component. The logic relationship is as follows: the developer preselects a specific target time period, and all lane off-line data streams containing the target time period are serially flowed into the time window component. The time window component is called to enable the lane line offline data flow to establish a first graph taking x as time and y as a corresponding target lane line parameter value under a Cartesian coordinate system, dynamically display the corresponding target lane line value at each moment, and enable an analyst to observe the change of signals intuitively.
The drawing component then leaves a call interface for subsequent visualizations.
And a numerical analysis module: and according to the target lane line coefficient selected by the developer, selecting a corresponding statistical method, a mean value, an expected value and a variance based on the developer, processing the selected statistical method, the mean value, the expected value and the variance, and transmitting the processed statistical method, the mean value, the expected value and the variance into a visualization module.
Specifically, the numerical analysis module is mainly composed of all statistical algorithm components, and an interface is reserved to facilitate the new statistical method of an analyst. The module mainly comprises 2 components, a statistical algorithm component (a mean component, a variance component and a desired component) and a drawing component. The statistical algorithm component is used for calculating the mean value, the expected value and the variance of the target lane line curve coefficients of each sensor based on a first curve graph of the target lane line curve coefficients of each sensor over time in the target time period, and then utilizing the drawing component to draw and form a second curve graph of the mean value of the target lane line curve coefficients of each sensor over time in the target time period, a third curve graph of the variance of the target lane line curve coefficients of each sensor over time in the target time period, and/or a fourth curve graph of the expected value of the target lane line curve coefficients of each sensor over time in the target time period.
The visualization module has the following functions: 1. providing a file loading interface and starting each module thread; 2. and displaying the results processed by the time flow graph dumping component and the numerical analysis component on an interface through the data volume. 3. The original video image is displayed.
The visualization module mainly comprises 2 components, a thread component and a user interface component. The thread components serve the 4 modules respectively, and four different threads are started respectively: an offline data analysis and data transfer thread, a time flow diagram thread, a numerical analysis thread and an original video thread. The user interface component is used for providing controls for the threads respectively to trigger the signal slot function to open the threads, and two terminals for displaying drawings are arranged in addition.
The above-described apparatus in the present embodiment will now be described with reference to examples:
the automatic driving data packet recorded by the CAN protocol in the embodiment comprises Lane line information of the front-view camera and the round-the-view camera, and is replaced by FC_Lane and SC_Lane respectively.
1. And a visualization module: and importing the lane line offline data packet to be analyzed in the data loading window by an analyst, and calling the offline data analysis thread, the data transfer thread, the time flow diagram thread and the numerical analysis thread to finish initialization. And (5) receiving output results of the following steps 4 and 5 by using an interface for visual display by a user according to the sensor types.
2. And the offline data analysis module is used for: and (2) importing data to a data loading component according to the data path provided in the step (1), and analyzing SC_Lane and FC_Lane signals in 40ms (period) according to the communication matrix by an analyzing component and storing the signals into a first data container A1 and a first data container A2 respectively.
3. Data cleaning and transferring: firstly, synchronizing data in the second data containers A1 and A2 according to the current time of the system (the time synchronization is to ensure that all the received sensors ensure the same time), and then respectively adding the SC/FC sensor flag bits to lane line off-line data in the front-view camera A1 and the around-view camera A2 and storing the lane line off-line data in the second data container B.
4. A time flow graph module: extracting three cubic curve coefficients of FC\SC_Lane_C0\C2\C3 in a second data container B, wherein the three signal input quantities are respectively drawn in a visual component by a Cartesian coordinate system Y value and a time stamp as an X axis, and the signal change can be dynamically displayed along with time change;
5. and a numerical analysis module: and extracting FC\SC_Lane_C0\C2\C3 coefficients in the second data container B, respectively carrying out statistic (mean value and variance) iterative computation, and displaying the statistic value in real time according to the time flow in a visualization module.
By the method, the lane line offline data can be visually analyzed, so that developers can be helped to quickly and intuitively locate the problem from the massive lane line offline data, the processing effect is improved, and the problem processing time is shortened.
Claims (2)
1. A lane line offline data visualization method, comprising:
acquiring and analyzing lane line offline data packets of the lane line sensors to obtain lane line offline data of the lane line sensors, and respectively storing the lane line offline data of the sensors into a first data container;
performing time synchronization on the lane line offline data of each sensor stored in each first data container, and transferring the lane line offline data of each sensor stored in each first data container after time synchronization into the same second data container after adding corresponding sensor flag bits respectively;
selecting a target time period and a target lane line coefficient to be displayed, and extracting target lane line offline data of all sensors in the target time period from the second data container;
respectively establishing a Cartesian coordinate system with a horizontal axis being time and a vertical axis being a target lane line curve coefficient for each sensor, and forming a first curve graph of each target lane line curve coefficient of each sensor changing with time in the target time period;
calculating a second graph of the mean value of the target lane-line curve coefficients of each sensor over time over the target period based on a first graph of the target lane-line curve coefficients of each sensor over time over the target period, a third graph of the variance of the target lane-line curve coefficients of each sensor over time over the target period, and/or a fourth graph of the target lane-line curve coefficients of each sensor expected to change over time over the target period;
and outputting and displaying a second curve graph of the mean value of the target lane line curve coefficients of each sensor over time in the target time period, a third curve graph of the variance of the target lane line curve coefficients of each sensor over time in the target time period, and/or a fourth curve graph of the target lane line curve coefficients of each sensor expected to change over time in the target time period.
2. A lane line offline data visualization device, the device comprising:
the off-line data analysis module is used for acquiring and analyzing the off-line data packets of the lane lines of the lane line sensors to obtain off-line data of the lane lines of the lane line sensors, and then respectively storing the off-line data of the lane lines of the sensors into a first data container;
the data transfer module is used for carrying out time synchronization on the lane line offline data of each sensor stored in each first data container, and then transferring the lane line offline data of each sensor stored in each first data container after the time synchronization into the same second data container after the corresponding sensor flag bit is added;
the time flow diagram module is used for selecting a target time period and target lane line coefficients to be displayed, and extracting target lane line offline data of all sensors in the target time period from the second data container; respectively establishing a Cartesian coordinate system with a horizontal axis being time and a vertical axis being a target lane line curve coefficient for each sensor, and forming a first curve graph of each target lane line curve coefficient of each sensor changing with time in the target time period;
a numerical analysis module for calculating a second graph of the mean value of the target lane-line curve coefficients of each sensor over time over the target time period based on a first graph of the target lane-line curve coefficients of each sensor over time over the target time period, a third graph of the variance of the target lane-line curve coefficients of each sensor over time over the target time period, and/or a fourth graph of the target lane-line curve coefficients of each sensor expected to change over time over the target time period;
and the visualization module is used for outputting and displaying a second curve graph of the mean value of the target lane line curve coefficients of each sensor over time in the target time period, a third curve graph of the variance of the target lane line curve coefficients of each sensor over time in the target time period and/or a fourth curve graph of the target lane line curve coefficients of each sensor expected to change over time in the target time period.
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