CN112816954B - Road side perception system evaluation method and system based on true value - Google Patents
Road side perception system evaluation method and system based on true value Download PDFInfo
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Abstract
The application discloses a road side perception system evaluation method based on true values, which comprises the following steps: establishing a true value sensing equipment group, and synchronously acquiring road side sensing data with sensing equipment of a road side 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 recognition and target track recognition and finish sensing data labeling; generating true values based on the marked data, wherein the true value data comprises the target type, position, speed, acceleration and track of the traffic participant; and in the selected test time interval, comparing the structured perception data output by the RSS to be tested with the truth data, and outputting a statistical evaluation result of the perception performance. The application also provides a truth value system for road side perception system evaluation.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a road side perception system evaluation method and device based on true values.
Background
The road side perception system (Roadside Sensing System, RSS) is an important means for supporting network automatic driving, improving traffic running efficiency and relieving congestion. The RSS system provides information such as beyond visual range 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 bicycle. The current RSS has different composition forms, including configuration schemes such as a laser radar, a camera, a millimeter wave radar, a camera and the like, various schemes are better and worse, a systematic evaluation method for the whole RSS is lacked, and especially, test specifications on the aspects of the type, the precision, the quality and the like of structural perception data output by the RSS are not established, so that a vehicle end cannot directly collect road side information, the RSS in the current automatic driving development stage can only be used as a redundant information source, the vehicle end is fused and referenced in a perception layer, and the quantitative evaluation of the RSS cannot be realized, so that the method is one of key factors for making elbow collaborative decisions and controlling and realizing full automatic driving.
Disclosure of Invention
The application provides a road side perception system evaluation method and system based on a true value, which solve the problem that the current vehicle-road collaborative road side perception system lacks a systematic evaluation method, and particularly solve the problem that the quality of road side perception information cannot be quantitatively evaluated.
On one hand, the embodiment of the application provides a road side perception system evaluation method based on true values, which comprises the following steps:
establishing a true value sensing equipment group, and synchronously acquiring road side sensing data with sensing equipment of a road side 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 recognition and target track recognition and finish sensing data labeling;
generating true values based on the marked data, wherein the true value data comprises the target type, position, speed, acceleration and track of the traffic participant;
and in the selected test time interval, comparing the structured perception data output by the RSS to be tested with the truth data, and outputting a statistical evaluation result of the perception performance.
Preferably, the truth-value sensing equipment group comprises a high-beam laser radar, a high-definition camera and a millimeter wave radar.
Preferably, the processing the raw data returned by the true value sensing device group to complete the target type identification and the target track identification further includes:
and (3) completing recognition of the target type and tracking of the target track of the traffic participant based on the point cloud data returned by the high-linear-speed laser radar, correcting the target type by using the collected data of the camera, and correcting the target track by using the millimeter wave radar data.
Preferably, the processing the raw data returned by the true value sensing device group further includes:
and (3) 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 perceptual data of the RSS output under test with the truth data further includes calculating:
target recognition accuracy = target or event number in the target or event number/truth value data correctly detected by the RSS to be detected.
In any one of the method embodiments of the present application, preferably, the comparing the structured perceptual data of the RSS output under test with the truth data further includes calculating:
target miss rate=1-target or event number in the target or event number/truth value data detected by the RSS to be detected.
In any one of the method embodiments of the present application, preferably, the comparing the structured perceptual data of the RSS output under test with the truth data further includes calculating:
the distance between the discrete time sequence of state parameters in the structured perceptual data and the discrete time sequence of state parameters in the truth data of the RSS output.
The status parameter includes at least one of: target size, position, speed, heading angle, acceleration, trajectory.
On the other hand, the application also provides a truth system for evaluating the road side perception system, and the truth system comprises a truth perception equipment group and a server, so as to realize the method according to any one embodiment of the application;
the truth 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 the 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 to finish target type identification and target track identification and finish sensing data marking;
the truth value storage module is used for backing up truth value data;
the RSS evaluation module is used for comparing the structured perception data output by the RSS to be tested with the truth data and outputting a statistical evaluation result of the perception performance.
Preferably, the sensing equipment group and the RSS multiplexing rod frame resource to be tested are deployed.
The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect:
the technical development and the iterative upgrading of the products in the vehicle-road cooperation field are promoted, and the effect of improving the industry standardization is achieved.
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 embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:
FIG. 1 is a diagram of a technical architecture of RSS;
FIG. 2 is a diagram of the overall architecture of the truth system;
FIG. 3 is a schematic diagram of a method for testing a road side perception system based on RS;
fig. 4 is a sample space representation of detection accuracy calculations.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments of the present application and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The invention provides a road side perception system evaluation method based on a truth system, which firstly provides a composition framework and a deployment principle of the truth system, further details test logic and steps of the road side perception system on the basis of the truth system, and finally provides an evaluation index system oriented to the road side perception system.
The following describes in detail the technical solutions provided by the embodiments of the present application with reference to the accompanying drawings.
Fig. 1 is a diagram of an RSS architecture.
The basic constitution of the RSS is a road side sensing device and a road side computing unit, as shown in fig. 1, the road side sensing device comprises but not limited to a camera, a laser radar, a millimeter wave radar and other devices, can acquire original sensing data such as images, videos, point clouds and the like of a currently covered traffic environment in real time, the road side computing unit comprises but not limited to computing devices such as an edge computing server and an industrial personal computer, and the like, and the acquisition of full information such as traffic participant state information, road condition information, traffic events and the like in the traffic environment is realized through the real-time fusion computation of the original sensing data acquired by the road side sensing device, so that sensing information is issued to local/global traffic participants through a road side unit RSU and a central subsystem.
Regardless of the composition of the RSS architecture, the final output structured perception data format is defined by the T/CSAE 53-2017 specification of the communication system application layer and the application data interaction standard for the cooperative intelligent transportation system vehicle, but the perception data quality is closely related to the composition of the RSS, for example, the RSS configuring the laser radar is more accurate in acquiring information such as speed, acceleration, track tracking and the like of a participant, the RSS configuring the camera is more powerful in identifying capability on a target type and the like, and a set of data reference system with complete equipment configuration and superior perception performance is established, so that the method has important significance for finely describing the traffic environment and quantitatively evaluating the system performance of the RSS.
Fig. 2 is a diagram of the overall architecture of the truth system.
The invention provides a truth System (RS) oriented to RSS test evaluation, and a System composition block diagram is shown in fig. 2. The RS system comprises a sensing equipment group formed by high-performance sensors and an off-line truth value system server meeting the large data processing requirement, wherein the sensing equipment group comprises a high-beam laser radar, a high-definition camera and a millimeter wave radar; the offline truth system server has specialized storage, processing and analysis capabilities of PB-level data, and is provided with four functional modules, namely a bearing data acquisition module, an intelligent processing module, a truth storage module and an RSS evaluation module, wherein the data acquisition module mainly realizes fusion and convergence of image, video, point cloud and other data, the intelligent processing module mainly completes links such as original data association and automatic labeling, generates environment truth values of a long-time sequence, realizes storage and landing and redundancy backup of PB-level data through the truth storage module, and the RSS evaluation module outputs statistical analysis results through set evaluation dimensions and index systems.
The RSS to be tested is generally deployed in heavy-point traffic monitoring areas such as urban intersections, expressway ramp in/out ports, bridge tunnels and the like, pole frame resources such as signal lamp portals, expressway portals, roadside lamp posts and the like are multiplexed, the sensing equipment group of the RS can be deployed with the RSS multiplexing pole frame resources to be tested, in view of the fact that an offline truth system server generates a truth value by offline calculation on acquired environmental information, the RSS to be tested can be flexibly deployed at the positions such as roadsides, central machine rooms and the like according to actual conditions, and data return from the sensing equipment group to the server is realized through a wired/wireless network.
Fig. 3 is a diagram of a RS-based road side perception system testing method.
The invention provides a method for testing and evaluating a road side perception system based on a truth system RS, wherein the testing and grading is that the road side original perception data acquisition and the server side offline processing are carried out, and the specific testing steps are shown in fig. 3.
Collecting original perception data at a road end:
step one: multiplexing RSS rod rack resources to be detected, and deploying an RS sensing equipment group;
step two: performing sensor global calibration on the RS sensing equipment group, and setting a truth value acquisition area according to the RSS sensing area to be detected;
step three: and selecting a test time interval, synchronously starting road side sensing data acquisition by the RSS and RS sensing equipment group to be tested, and transmitting the data back to the server through a wired/wireless network.
And (3) offline processing at a server side:
step one: data cleaning is carried out on the original sensing data returned by the RS sensing equipment group, data consistency is ensured, and time alignment of data such as laser radar point cloud, millimeter wave Lei Dadian cloud, images and videos is completed;
step two: and (3) automatically labeling returned data of the RS sensing equipment group, namely based on point cloud data returned by the high-beam laser radar, completing recognition and detection of the target type of the traffic participant and off-line track tracking of multiple targets based on algorithms such as machine learning, deep learning and the like. And (3) acquiring data by fusing the camera, performing secondary correction on the type of the target, fusing millimeter wave radar data, and performing secondary correction on target track data (including data such as speed, acceleration and position). Inputting the automatically marked data into a correction module (allowing manual marking to intervene in correction) to finish various perception data marks;
step three: generating static and dynamic truth values based on marked 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 a test time interval, completing time alignment with the RSS output structured perception data to be tested, setting an evaluation dimension, and outputting a statistical evaluation result of the perception performance.
The invention also provides a perception data quality evaluation algorithm based on the multidimensional 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 road side message body defined by a communication system application layer and application data interaction standard for a cooperative intelligent transportation system vehicle (T/CSAE 53-2017) and a perception data category of current mainstream RSS output, and specific evaluation indexes comprise target identification accuracy, target omission factor and detection accuracy (target size, position, speed, course angle, acceleration and track), wherein the target identification accuracy and the target omission factor are indexes for measuring the identification performance of the RSS on traffic participants, and the calculation method can be expressed by formulas:
fig. 4 is a schematic diagram of a sample space for detection accuracy calculation. The detection precision is an index for measuring the state tracking capability of the RSS on any traffic participant, the sample space for statistical evaluation is the target state data output by the RSS to be detected and the target state true value (the time alignment is completed) stored by the RS, and the two groups of data can be represented as a broken line structure presented in fig. 4:
wherein,1,2, …, n represent an offline time sequence, points forming a track can represent any state parameter (target size, position, speed, course angle, acceleration and track), and further the distance between the state information output by the RSS to be detected and the true value of the RS state 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 a distance based on points, a distance based on shapes and a distance based on segmentation, and the measurement mode of the track distance can be selected flexibly by comprehensively considering factors such as track length, noise sensitivity, calculation complexity and the like.
According to the road side perception system evaluation method based on the truth system, the evaluation method is based on the high-performance truth system which is synchronously deployed with the RSS to be tested, so that the precise data acquisition of the real complex traffic environment is realized, objective truth data is generated through offline post-processing, and finally, the test evaluation of the RSS perception performance to be tested can be realized by utilizing the truth 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 foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc. which are within the spirit and principles of the present application are intended to be included within the scope of the claims of the present application.
Claims (10)
1. The road side perception system evaluation method based on the true value is characterized by comprising the following steps of:
establishing a true value sensing equipment group, and synchronously acquiring road side sensing data with sensing equipment of a road side 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 recognition and target track recognition and finish sensing data labeling;
generating true values based on the marked data, wherein the true value data comprises the target type, position, speed, acceleration and track of the traffic participant;
and in the selected test time interval, comparing the structured perception data output by the RSS to be tested with the truth data, and outputting a statistical evaluation result of the perception performance.
2. The method of claim 1, wherein,
the truth value sensing equipment group comprises a high-beam laser radar, a high-definition camera and a millimeter wave radar.
3. The method of claim 1, wherein,
the processing of the original data returned by the true value sensing equipment group to complete target type recognition and target track recognition further comprises the following steps:
and (3) completing recognition of the target type and tracking of the target track of the traffic participant based on the point cloud data returned by the high-linear-speed laser radar, correcting the target type by using the collected data of the camera, and correcting the target track by using the millimeter wave radar data.
4. The method of claim 1, wherein,
the processing the original data returned by the true value sensing equipment group further comprises the following steps:
and (3) 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 claim 1 to 4, wherein,
the comparing the structured perceptual data of the RSS output to be tested with the truth data further comprises, calculating:
target recognition accuracy = target or event number in the target or event number/truth value data correctly detected by the RSS to be detected.
6. The method according to any one of claim 1 to 4, wherein,
the comparing the structured perceptual data of the RSS output to be tested with the truth data further comprises, calculating:
target miss rate=1-target or event number in the target or event number/truth value data detected by the RSS to be detected.
7. The method according to any one of claim 1 to 4, wherein,
the comparing the structured perceptual data of the RSS output to be tested with the truth data further comprises, calculating:
the distance between the discrete time sequence of state parameters in the structured perceptual 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 status parameter includes at least one of: target size, position, speed, heading angle, acceleration, trajectory.
9. A truth value system for evaluating a road side perception system, which is used for realizing 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 truth 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 the 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 to finish target type identification and target track identification and finish sensing data marking;
the truth value storage module is used for backing up truth value data;
the RSS evaluation module is used for comparing the structured perception data output by the RSS to be tested with the truth data and outputting a statistical evaluation result of the perception performance.
10. The truth system for roadside perception system evaluation according to claim 9,
and the sensing equipment group and the RSS multiplexing rod frame resource to be tested are deployed.
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