CN112816954A - Road side perception system evaluation method and system based on truth value - Google Patents
Road side perception system evaluation method and system based on truth value Download PDFInfo
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Abstract
The application discloses a road side perception system evaluation method based on truth values, which comprises the following steps of: establishing a true value sensing equipment group, and synchronously carrying out roadside sensing data acquisition with sensing equipment of a roadside sensing system RSS to be tested in a selected test time interval; processing the original data returned by the true value sensing equipment group to finish target type identification and target track identification and finish sensing data annotation; generating a truth value based on the annotated data, wherein the truth value data comprises the target type, position, speed, acceleration and track of the traffic participant; and comparing the structured sensing data output by the RSS to be tested with the true value data in the selected testing time interval, and outputting a statistical evaluation result of the sensing performance. The application also provides a truth value system for evaluating the road side perception system.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a method and a device for evaluating a road side perception system based on a truth value.
Background
The Roadside Sensing System (RSS) is an important means for supporting internet automatic driving, improving traffic operation efficiency and relieving congestion. The RSS system provides information such as beyond-the-horizon perception, blind area early warning, driving intention and the like for the automatic driving automobile, and is one of important technical means for making up the limitation of the automatic driving perception of the single automobile. The existing RSS has different composition forms, including configuration schemes of a laser radar and a camera, a millimeter wave radar and a camera, and the like, various schemes are superior and inferior, a systematic evaluating method facing the RSS is lacking, especially, a test specification on the aspects of structural perception data type, precision, quality and the like of RSS output is not established, so that a vehicle end cannot directly adopt information on a road side, the RSS can only be used as a redundant information source in the current automatic driving development stage, the vehicle end is provided with fusion reference in a perception level, quantitative evaluation on the RSS is an elbow-control cooperative decision and control, and one of key factors for realizing complete automatic driving is realized.
Disclosure of Invention
The application provides a method and a system for evaluating a road side perception system based on a truth value, solves the problem that the conventional vehicle and road cooperation road side perception system lacks a systematic evaluation method, and particularly solves the problem that the quality of road side perception messages cannot be evaluated quantitatively.
On one hand, the embodiment of the application provides a method for evaluating a road side perception system based on a truth value, which comprises the following steps:
establishing a true value sensing equipment group, and synchronously carrying out roadside sensing data acquisition with sensing equipment of a roadside sensing system RSS to be tested in a selected test time interval;
processing the original data returned by the true value sensing equipment group to finish target type identification and target track identification and finish sensing data annotation;
generating a truth value based on the annotated data, wherein the truth value data comprises the target type, position, speed, acceleration and track of the traffic participant;
and comparing the structured sensing data output by the RSS to be tested with the true value data in the selected testing time interval, and outputting a statistical evaluation result of the sensing performance.
Preferably, the truth value perception device group comprises a high-beam laser radar, a high-definition camera and a millimeter-wave radar.
Preferably, the processing of the original data returned by the true value sensing device group is performed to complete target type recognition and target trajectory recognition, and further includes:
and (3) finishing the identification of the target type and the tracking of the target track of the traffic participant on the basis of point cloud data returned by the high-speed laser radar, correcting the target type by using data collected by a camera, and correcting the target track by using millimeter wave radar data.
Preferably, the processing the original data returned by the truth sensing device group further includes:
and (4) carrying out data cleaning on the original data returned by the true value sensing equipment group, and carrying out time alignment on the data from different sensing equipment.
In any one of the method embodiments of the present application, preferably, the comparing the structured sensing data of the RSS to be tested output with the truth data further includes calculating:
the target identification accuracy is the number of targets or events in the target or event number/true value data correctly detected by the RSS under test.
In any one of the method embodiments of the present application, preferably, the comparing the structured sensing data of the RSS to be tested output with the truth data further includes calculating:
the target missing detection rate is 1-the number of targets or events in the target or event/true value data detected by the RSS under test.
In any one of the method embodiments of the present application, preferably, the comparing the structured sensing data of the RSS to be tested output with the truth data further includes calculating:
the distance between the discrete-time sequence of state parameters in the structured awareness data and the discrete-time sequence of state parameters in the truth data of the RSS output.
The state parameters include at least one of: target size, position, speed, heading angle, acceleration, trajectory.
On the other hand, the application also provides a truth value system for evaluating the roadside sensing system, which realizes the method of any one embodiment of the application, wherein the truth value system comprises a truth value sensing equipment group and a server;
the true value sensing equipment group at least comprises a high linear speed laser radar, a high-definition camera and a millimeter wave radar;
the server comprises a data acquisition module, an intelligent processing module, a true value storage module and an RSS evaluation module;
the data acquisition module is used for fusing image, video and point cloud data output by the true value sensing equipment group;
the intelligent processing module is used for processing the original data returned by the true value sensing equipment group, completing target type identification and target track identification and completing sensing data annotation;
the truth value storage module is used for backing up truth value data;
and the RSS evaluation module is used for comparing the structured sensing data output by the RSS to be tested with the true value data and outputting the statistical evaluation result of the sensing performance.
Preferably, the sensing equipment group and the RSS to be tested multiplex bar frame resource deployment.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
the scheme of the invention provides feasible support for function and performance evaluation of the road side sensing system, provides test basis for product selection of the road side sensing system, promotes technical evolution and product iteration upgrading in the field of vehicle and road cooperation, and plays a role in improving industrial normative.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a diagram of a RSS architecture;
FIG. 2 is a diagram of the overall architecture of the truth system;
FIG. 3 is a method for testing a roadside sensing system based on RS;
fig. 4 is a sample space representation of detection accuracy calculations.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some 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 invention provides a method for evaluating a roadside sensing system based on a truth value system, which comprises the steps of firstly providing a composition architecture and a deployment principle of the truth value system, further detailing test logic and steps of the roadside sensing system on the basis of the truth value system, and finally providing an evaluation index system facing the roadside sensing system.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a technical architecture diagram of RSS.
The RSS is basically composed of a roadside sensing device and a roadside computing unit, as shown in fig. 1, the roadside sensing device includes, but is not limited to, a camera, a laser radar, a millimeter wave radar and other devices, and can acquire original sensing data of an image, a video, a point cloud and the like of a current covered traffic environment in real time, the roadside computing unit includes, but is not limited to, an edge computing server, an industrial personal computer and other computing devices, and the acquisition of full-scale information such as state information of traffic participants, road condition information, traffic events and the like in the traffic environment is realized by performing real-time fusion computing on the original sensing data acquired by the roadside sensing device, and then sensing information is issued to local/global traffic participants through a roadside unit RSU and a central subsystem.
No matter what kind of RSS framework is formed, the final output structured sensing data format is defined by the 'application layer and application data interaction standard for vehicle communication system of cooperative intelligent transportation system' T/CSAE 53-2017 standard, but the sensing data quality is closely related to the composition of RSS, for example, RSS configured with a laser radar is more accurate in acquisition of speed, acceleration, track tracking and other information of participants, RSS configured with a camera is stronger in identification capability of target types, and the like, a set of data reference system with complete equipment configuration and excellent sensing performance is established, and the data reference system has important significance for finely describing traffic environment and quantitatively evaluating the system performance of RSS.
Fig. 2 is a diagram of the overall architecture of the real-valued system.
The invention provides a truth System (RS) for RSS test evaluation, and a block diagram of the System is shown in FIG. 2. The RS system comprises a sensing equipment group consisting of high-performance sensors and an offline true value system server meeting the requirement of big data processing, wherein the sensing equipment group comprises a high-line beam laser radar, a high-definition camera and a millimeter wave radar; the offline truth-value system server has professional storage, processing and analysis capabilities of PB-level data, and is provided with four functional modules, namely a data acquisition module, an intelligent processing module, a truth-value storage module and an RSS evaluation module, wherein the data acquisition module is mainly used for realizing fusion and aggregation of data such as images, videos and point clouds, the intelligent processing module is mainly used for completing links such as original data association and automatic annotation, generating long-time sequence environment truth values, realizing storage tray dropping and redundant backup of the PB-level data through the truth-value storage module, and the RSS evaluation module is used for outputting statistical analysis results through set evaluation dimensions and an index system.
The RSS to be detected is generally deployed in key traffic monitoring areas such as urban intersections, expressway ramp import/export ports and bridges and tunnels, and the rack resources such as signal lamp racks, expressway racks and roadside lamp poles are multiplexed, the sensing equipment group of the RS can be deployed with the RSS to be detected multiplexing rack resources, and in view of the fact that an offline true value system server generates a true value by offline calculation on collected environment information, the RS can be flexibly deployed at positions such as roadside and central computer rooms according to actual conditions, and data return from the sensing equipment group to the server is achieved through a wired/wireless network.
FIG. 3 is a method for testing the road side sensing system based on the RS.
The invention provides a method for testing and evaluating a road side sensing system based on a true value system RS, wherein the evaluation comprises two parts of road side original sensing data acquisition and server side off-line processing, and the specific testing steps are shown in figure 3.
Acquiring original sensing data of a road end:
the method comprises the following steps: multiplexing RSS pole frame resources to be tested, and deploying RS sensing equipment groups;
step two: carrying out sensor global calibration on the RS sensing equipment group, and setting a true value acquisition area according to the sensed area of the RSS to be measured;
step three: and selecting a test time interval, synchronously starting roadside sensing data acquisition by the RSS and RS sensing equipment group to be tested, and transmitting the data back to the server side through a wired/wireless network.
Server side off-line processing:
the method comprises the following steps: carrying out data cleaning on original sensing data returned by the RS sensing equipment group, ensuring data consistency and finishing time alignment of data such as laser radar point cloud, millimeter wave radar point cloud, images, videos and the like;
step two: and (3) automatically labeling the data returned by the RS sensing equipment group, namely, on the basis of point cloud data returned by the high-line-beam laser radar, and completing the identification and detection of the target type of the traffic participant and the off-line track tracking of multiple targets based on algorithms such as machine learning, deep learning and the like. The method comprises the steps of collecting data by fusing a camera, carrying out secondary correction on the type of a target, fusing millimeter wave radar data, and carrying out secondary correction on target track data (including data such as speed, acceleration and position). Inputting the automatically labeled data into a correction module (allowing manual labeling to intervene in correction) to finish labeling of various sensing data;
step three: generating static and dynamic truth values based on the labeled data, wherein the static and dynamic truth values comprise truth values such as target types, positions, speeds, accelerations, tracks and the like of traffic participants, and finishing truth value storage and RS establishment;
step four: and extracting a true value in the test time interval, finishing time alignment with structured sensing data output by the RSS to be tested, setting evaluation dimensionality and outputting a statistical evaluation result of the sensing performance.
The invention also provides a perception data quality evaluation algorithm based on the multi-dimensional index system, which is applied to the design and development of the RSS evaluation module. The design of the multidimensional index system is based on a roadside message body defined by a communication system application layer and application data interaction standard for a cooperative intelligent transportation system T/CSAE 53-2017 and a perception data category output by current mainstream RSS, and specific evaluation indexes comprise target identification accuracy, target omission ratio and detection precision (target size, position, speed, course angle, acceleration and track), wherein the target identification accuracy and the target omission ratio are indexes for measuring the identification performance of the RSS to traffic participants, and the calculation method can be expressed by a formula as follows:
fig. 4 is a sample space diagram of detection accuracy calculation. The detection precision is an index for measuring the status tracking capability of the RSS to any traffic participant, the statistical evaluation sample space is the target status data output by the RSS to be detected and the target status true value stored by the RS (the finished time alignment), and the two groups of data can be represented as a broken line structure shown in fig. 4:
wherein,
1,2, …, n represents an off-line time sequence, points forming a track can represent any state parameter (target size, position, speed, course angle, acceleration, track), and then the distance between the state information output by the RSS to be detected and the RS state truth value is obtained through track similarity analysis, and the detection precision of each state parameter is represented by distance measurement. The track similarity measurement method can be roughly divided into three categories of distance based on points, distance based on shapes and distance based on segmentation, and the measurement mode of the track distance can be flexibly selected by comprehensively considering factors such as track length, noise sensitivity, calculation complexity and the like.
According to the road side perception system evaluating method based on the truth value system, disclosed by the invention, based on the high-performance truth value system synchronously deployed with the RSS to be tested, the refined data acquisition of a real complex traffic environment is realized, objective truth value data is generated through offline post-processing, and finally, the test evaluation of the perception performance of the RSS to be tested can be realized by utilizing the truth value data.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A road side perception system evaluation method based on truth is characterized by comprising the following steps:
establishing a true value sensing equipment group, and synchronously carrying out roadside sensing data acquisition with sensing equipment of a roadside sensing system RSS to be tested in a selected test time interval;
processing the original data returned by the true value sensing equipment group to finish target type identification and target track identification and finish sensing data annotation;
generating a truth value based on the annotated data, wherein the truth value data comprises the target type, position, speed, acceleration and track of the traffic participant;
and comparing the structured sensing data output by the RSS to be tested with the true value data in the selected testing time interval, and outputting a statistical evaluation result of the sensing performance.
2. The method of claim 1,
the truth value perception equipment group comprises a high-beam laser radar, a high-definition camera and a millimeter wave radar.
3. The method of claim 1,
the processing of the original data returned by the true value sensing device group to complete the target type recognition and the target track recognition further comprises:
and (3) finishing the identification of the target type and the tracking of the target track of the traffic participant on the basis of point cloud data returned by the high-speed laser radar, correcting the target type by using data collected by a camera, and correcting the target track by using millimeter wave radar data.
4. The method of claim 1,
the processing of the original data returned by the true sensing device group further includes:
and (4) carrying out data cleaning on the original data returned by the true value sensing equipment group, and carrying out time alignment on the data from different sensing equipment.
5. The method according to any one of claims 1 to 4,
the comparing the structured sensing data output by the RSS to be tested with the true value data further comprises the following steps:
the target identification accuracy is the number of targets or events in the target or event number/true value data correctly detected by the RSS under test.
6. The method according to any one of claims 1 to 4,
the comparing the structured sensing data output by the RSS to be tested with the true value data further comprises the following steps:
the target missing detection rate is 1-the number of targets or events in the target or event/true value data detected by the RSS under test.
7. The method according to any one of claims 1 to 4,
the comparing the structured sensing data output by the RSS to be tested with the true value data further comprises the following steps:
the distance between the discrete-time sequence of state parameters in the structured awareness data and the discrete-time sequence of state parameters in the truth data of the RSS output.
8. The method of claim 7, wherein the step of removing comprises removing a portion of the substrate from the substrate
The state parameters include at least one of: target size, position, speed, heading angle, acceleration, trajectory.
9. A truth value system for evaluating a roadside perception system, which realizes the method of any one of claims 1 to 8, and is characterized by comprising a truth value perception equipment group and a server;
the true value sensing equipment group at least comprises a high linear speed laser radar, a high-definition camera and a millimeter wave radar;
the server comprises a data acquisition module, an intelligent processing module, a true value storage module and an RSS evaluation module;
the data acquisition module is used for fusing image, video and point cloud data output by the true value sensing equipment group;
the intelligent processing module is used for processing the original data returned by the true value sensing equipment group, completing target type identification and target track identification and completing sensing data annotation;
the truth value storage module is used for backing up truth value data;
and the RSS evaluation module is used for comparing the structured sensing data output by the RSS to be tested with the true value data and outputting the statistical evaluation result of the sensing performance.
10. A truth system for roadside perception system evaluation according to claim 9, wherein,
and multiplexing the pole frame resource deployment of the sensing equipment group and the RSS to be detected.
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