CN110596731A - Active obstacle detection system and method for metro vehicle - Google Patents
Active obstacle detection system and method for metro vehicle Download PDFInfo
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- CN110596731A CN110596731A CN201910867435.9A CN201910867435A CN110596731A CN 110596731 A CN110596731 A CN 110596731A CN 201910867435 A CN201910867435 A CN 201910867435A CN 110596731 A CN110596731 A CN 110596731A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/93—Radar or analogous systems specially adapted for specific applications for anti-collision purposes
- G01S13/931—Radar or analogous systems specially adapted for specific applications for anti-collision purposes of land vehicles
- G01S2013/9328—Rail vehicles
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Abstract
The invention discloses a subway vehicle active obstacle detection system, which comprises a laser radar detection device, a millimeter wave radar detection device and a data fusion and control platform, wherein the laser radar detection device is used for detecting a plurality of active obstacles; wherein: the laser radar detection device transmits laser beams to the obstacles, and compares signals reflected by the obstacles with the transmitted signals to detect the position, speed, distance and angle of the obstacles; the millimeter wave radar detection device transmits millimeter waves to the obstacle, and detects the shape, position, speed, distance and angle of the obstacle according to the echo characteristics of the obstacle; the data fusion and control platform comprises a processor, the processor receives information from the laser radar detection device and the millimeter wave radar detection device, performs data fusion calculation processing, and outputs signals to the subway vehicle control and management system. The invention also discloses a subway vehicle active obstacle detection method. The invention realizes the all-weather and all-working-condition active obstacle high-precision detection and improves the running safety of the subway vehicle.
Description
Technical Field
The invention relates to the technical field of railway vehicle safety, in particular to a subway vehicle active obstacle detection system.
Background
At present, vehicles are the most important carrier of a subway passenger transport system, the safety of subway vehicles is the central importance of urban rail transit work, and foreign matter invasion and repair tool omission accidents caused by natural and human factors are frequently caused in a subway line operation area in recent years. Meanwhile, in the rail transit construction of many cities at home and abroad, the full-automatic unmanned technology is widely applied, the running efficiency is improved, the acceleration and deceleration performance of the vehicle is improved, and the comfort level is improved. The observation of a driver is lacked, and the high-reliability obstacle detection is the technical problem which needs to be solved urgently to ensure the driving safety of the subway train.
The obstacle detection technology applied to the existing subway train is mostly in contact passive detection or a detection mode of a single sensor. Passive barrier detection belongs to collision barrier detection, can not extract the early warning, can not avoid the barrier striking. And a single sensor is used for detection, all-weather detection under all working conditions cannot be realized, the detection distance is limited, and the detection recognition rate is not high.
Disclosure of Invention
The invention provides a subway vehicle active obstacle detection system capable of early warning for solving the technical problems in the prior art.
The technical scheme adopted by the invention for solving the technical problems in the prior art is as follows: an active obstacle detection system for a metro vehicle comprises a laser radar detection device, a millimeter wave radar detection device and a data fusion and control platform; wherein:
the laser radar detection device transmits laser beams to the obstacles, and compares signals reflected by the obstacles with the transmitted signals to acquire position, speed, distance and angle information of the obstacles;
the millimeter wave radar detection device transmits millimeter waves to the obstacle, and acquires the shape, position, speed, distance and angle information of the obstacle according to the echo characteristics of the obstacle;
the data fusion and control platform comprises a processor, the processor receives information from the laser radar detection device and the millimeter wave radar detection device, performs data fusion calculation processing, and outputs signals to the subway vehicle control and management system.
Further, the millimeter wave radar detection device comprises a long-distance millimeter wave radar and a short-distance millimeter wave radar; the long-distance millimeter wave radar is used for detecting obstacles between 30 meters and 200 meters; the short-range millimeter wave radar is used for detecting obstacles between 0 and 30 meters.
The invention also provides a method for detecting the active obstacle of the metro vehicle, which adopts a laser radar detection device to emit laser beams to the obstacle, and compares signals reflected by the obstacle with the emitted signals to acquire the position, speed, distance and angle information of the obstacle; the method comprises the steps that millimeter waves are transmitted to an obstacle by a millimeter wave radar detection device, and the shape, position, speed, distance and angle information of the obstacle is obtained according to the echo characteristics of the obstacle; and a data fusion and control platform provided with a processor is adopted, the processor inputs the information of the obstacles acquired by the laser radar detection device and the millimeter wave radar detection device, data fusion calculation is carried out, the obstacles are identified, and signals are sent to a subway vehicle control and management system.
Furthermore, the millimeter wave radar detection device adopts two millimeter wave radars, namely a long-distance millimeter wave radar and a short-distance millimeter wave radar; detecting obstacles between 30 meters and 200 meters by using the long-distance millimeter wave radar; and detecting the obstacle between 0 and 30 meters by using the short-distance millimeter wave radar.
Further, the processor performs data fusion processing by adopting a multivariate sensor fusion algorithm.
Further, the processor identifies the obstacle using a point cloud sensing algorithm.
The invention has the advantages and positive effects that: the invention adopts a laser radar detection device, a millimeter wave radar detection device and a data fusion and control platform; and detecting by adopting a multi-sensor fusion algorithm and a laser radar detection device and a millimeter wave radar detection device to obtain obstacle information, identifying the obstacle by adopting a radar point cloud sensing algorithm, realizing active detection and early warning on the obstacle, and providing reliable obstacle information for a train control and management system.
Compared with the prior art, the invention can obtain the following technical effects:
(1) the method has the advantages that all-weather and all-working-condition active obstacle high-precision detection of the subway train in a line area is realized, the running safety of the subway train is improved, and a full-automatic driving technology system of the subway train is perfected;
(2) by applying various radar detection devices and adopting a multi-source sensor data fusion algorithm, the precision and the reliability of the obstacle detection are improved.
Drawings
FIG. 1 is a schematic structural view of the present invention;
fig. 2 is a block diagram of the working principle of the present invention.
In the figure: 1. a laser radar detection device; 2. a subway train; 3. millimeter wave radar detection device.
Detailed Description
For further understanding of the contents, features and effects of the present invention, the following embodiments are enumerated in conjunction with the accompanying drawings, and the following detailed description is given:
referring to fig. 1 to 2, an active obstacle detection system for a metro vehicle includes a laser radar detection device 1, a millimeter wave radar detection device 3, and a data fusion and control platform; wherein:
the laser radar detection device 1 emits laser beams to the obstacles, and compares signals reflected by the obstacles with emitted signals to acquire position, speed, distance and angle information of the obstacles;
the millimeter wave radar detection device 3 transmits millimeter waves to the obstacle, and acquires the shape, position, speed, distance and angle information of the obstacle according to the echo characteristics of the obstacle;
the data fusion and control platform comprises a processor, the processor receives information from the laser radar detection device 1 and the millimeter wave radar detection device 3 and performs data fusion calculation processing, and the processor outputs signals to the subway vehicle control and management system.
The millimeter wave radar operates in the millimeter wave band. Usually, the millimeter wave refers to the frequency band of 30 to 300GHz (the wavelength is 1 to 10 mm). The wavelength of the millimeter wave is between the centimeter wave and the light wave, so the millimeter wave has the advantages of microwave guidance and photoelectric guidance. Compared with the centimeter wave seeker, the millimeter wave seeker has the characteristics of small volume, light weight and high spatial resolution. Compared with optical probes such as infrared, laser and television, the millimeter wave probe has strong capability of penetrating fog, smoke and dust and has the characteristics of all weather (except heavy rainy days) all day long. In addition, the anti-interference capability of the millimeter wave guide head is also superior to that of other microwave guide heads. It has the following characteristics:
(1) small antenna aperture, narrow beam: high tracking and guiding precision; the low elevation angle tracking is easy to carry out, and the ground multipath and clutter interference are resisted; the method has high transverse resolution on near-empty targets; high angular resolution is provided for region imaging and target monitoring; high anti-interference performance of narrow beams; high antenna gain; small targets, including power lines, poles, and projectiles, etc., are easily detected.
(2) Large bandwidth: the method has high information rate, and is easy to adopt narrow pulse or broadband frequency modulation signals to obtain the detailed structural characteristics of the target; the device has wide spectrum spreading capability, reduces multipath and clutter and enhances the anti-interference capability; the radar or millimeter wave recognizer of adjacent frequency works, so that mutual interference is easy to overcome; high distance resolution and easy obtaining of accurate target tracking and identification capability.
(3) High doppler frequency: good detection and identification capabilities of slow targets and vibrating targets; the target characteristic identification is easy to be carried out by utilizing the target Doppler frequency characteristic; penetration characteristics to dry atmospheric pollution provide good detection capability under dust, smoke and dry snow conditions.
Laser radar range finding is accurate, and resolution ratio is high, and the characteristic identification degree is high, but the adaptability of bad weather such as haze, dust is poor. The millimeter wave radar is accurate in distance measurement and speed measurement, strong in environmental adaptability, but poor in characteristic identification, and the coverage area is fan-shaped and has a blind area. Therefore, only by fusing the information of the two sensors and making up for the deficiencies, reliable and reliable detection and early warning information can be provided for decision control.
The data fusion and control platform comprises a processor, the processor receives information from the laser radar detection device 1 and the millimeter wave radar detection device 3, performs data fusion and calculation processing, and outputs a signal to an upper platform, namely a subway vehicle control and management system (TCMS).
The TCMS is an English abbreviation of a Train Control and Management System, and mainly has the functions of realizing subway Train characteristic Control, logic Control, fault monitoring and self-diagnosis, and transmitting information to a display screen on a driver console or a Control center for drivers or driving scheduling so as to visually reflect the real-time state of a subway Train.
The data fusion and control platform is in communication with a train control and management system. The processor of the data fusion and control platform receives information from the laser radar detection device 1 and the millimeter wave radar detection device 3, and performs data fusion and calculation processing; and judging and identifying the found obstacles, outputting identification information, sending the identification information to a TCMS train control and management system, and further processing the identification information by the train control and management system.
Laser radar detection device 1 can adopt laser radar among the prior art such as TOF laser radar, and TOF laser radar benefits from the characteristics of TOF ultrashort time light pulse at present, can all reach same drawing and range finding effect indoor outer, uses Silan company' S RPLIDAR S1 laser range finding sensor as an example, and it can realize effective work under 60klx light, does not receive the interference of outdoor highlight.
The millimeter-wave radar detection device 3 can adopt the millimeter-wave radar in the prior art, such as the continuous ARS 408 and the continuous ASR 308; and various millimeter-wave radars such as Delphi ESR type.
The processor of the data fusion and control platform can adopt a computer processor in the prior art, and can carry out model selection according to the processed data quantity, the operation speed requirement and the like.
The laser radar detection device 1, the millimeter wave radar detection device 3 and the data fusion and control platform can be connected and communicated through a wireless network or a wired network. The data fusion and control platform and the upper platform subway vehicle control and management system (TCMS) can communicate through a wired or wireless network.
Further, the millimeter wave radar detection means 3 may include a long-distance millimeter wave radar and a short-distance millimeter wave radar; the long-distance millimeter wave radar is used for detecting obstacles between 30 meters and 200 meters; the short-range millimeter wave radar is used for detecting obstacles between 0 and 30 meters. Long Range Radar (LRR) supports multiple functions, can easily handle obstacle detection at distances of 30 to 200 meters, and Short Range Radar (SRR) can detect obstacles at distances below 30 meters. Two millimeter wave radars are adopted, so that the information of the barrier can be acquired in two stages, and the detection is more accurate. The short-range radar (SRR) in the millimeter wave radar detection device 3 may employ a 24GHz band, and the long-range radar (LRR) in the millimeter wave radar detection device 3 may employ a 77GHz band (76-81 GHz). The short-range radar (SRR) and the long-range radar (LRR) such as USRR, SRR, MRR, LRR and the like can be built and realized by using an Enzhipu radar MCU and a 77GHz front-end technology RFCMOS and BiCMOS.
The laser radar detection device 1 can be arranged at the upper part of the subway train 2; the millimeter wave radar detection device 3 can be arranged at the lower part of the subway train 2; the data fusion and control platform can be installed in the subway train 2.
The invention also provides an embodiment of a method for detecting the active obstacle of the metro vehicle, wherein the method adopts the laser radar detection device 1 to emit laser beams to the obstacle, and compares signals reflected by the obstacle with the emitted signals to obtain the position, speed, distance and angle information of the obstacle; the millimeter wave radar detection device 3 is adopted to emit millimeter waves to the obstacle, and the shape, position, speed, distance and angle information of the obstacle is obtained according to the echo characteristics of the obstacle; and a data fusion and control platform provided with a processor is adopted, the information of the obstacles acquired by the laser radar detection device 1 and the millimeter wave radar detection device 3 is input through the processor, data fusion calculation is carried out through the processor, the obstacles are identified through the processor, and signals are sent to a subway vehicle control and management system through the processor.
The data fusion and control platform is in communication with a train control and management system. The processor of the data fusion and control platform receives information from the laser radar detection device 1 and the millimeter wave radar detection device 3, and performs data fusion and calculation processing; and judging and identifying the found obstacles, outputting identification information, sending the identification information to a TCMS train control and management system, and further processing the identification information by the train control and management system.
Further, the millimeter wave radar detection device 3 may adopt two millimeter wave radars, namely a long-distance millimeter wave radar and a short-distance millimeter wave radar; the long-distance millimeter wave radar can be used for detecting obstacles between 30 and 200 meters; obstacles between 0 and 30 meters may be detected using the short-range millimeter wave radar. Long Range Radar (LRR) supports multiple functions, can easily handle obstacle detection at distances of 30 to 200 meters, and Short Range Radar (SRR) can detect obstacles at distances below 30 meters. Two millimeter wave radars are adopted, so that the information of the barrier can be acquired in two stages, and the detection is more accurate. The short-range radar (SRR) in the millimeter wave radar detection device 3 may employ a 24GHz band, and the long-range radar (LRR) in the millimeter wave radar detection device 3 may employ a 77GHz band (76-81 GHz).
The obtained information of the obstacles is processed through the processor, a training sample set can be constructed on the basis of a deep learning algorithm on the basis of the possibly-appearing obstacles such as rail vehicles, pedestrians, various tools and instruments which may be omitted on a line, common invasion foreign matters and the like, and the processing results of the obstacle information obtained by the laser radar detection device 1 and the millimeter wave radar detection device 3 are compared. The method can enable the laser radar to firstly identify the obstacles in the scanning process, determine the positions of the obstacles in the space and then classify the obstacles according to the training sample set.
Further, the processor may perform data fusion processing using a multivariate sensor fusion algorithm. The redundancy and fault tolerance of the system can be obviously improved by the multi-sensor fusion, so that the rapidity and the correctness of decision making are ensured. The performance of each sensor has advantages and disadvantages, and the sensor can play unique advantages in different application scenes. The basic principle of the multi-sensor fusion algorithm is just like the comprehensive processing information of human brain, the resources of a plurality of sensors are fully utilized, and the redundant or complementary information of the sensors in space or time is combined according to a certain criterion through reasonable domination and use of the sensors and the observation information thereof, so as to obtain the consistency explanation or description of the measured object. The multivariate sensor fusion algorithm integrates and comprehensively analyzes data and information acquired by a plurality of sensors so as to describe the external environment more accurately and reliably, thereby improving the correctness of system decision. The multi-sensor fusion algorithm can rapidly process data and filter useless and wrong information, so that the system can make a decision timely and correctly. The multivariate sensor fusion algorithm can adopt one or a combination of a Bayesian rule method, a Kalman filtering method, a D-S evidence theory method, a fuzzy set theory method and an artificial neural network method.
Further, the processor may identify an obstacle using a point cloud sensing algorithm. The point cloud perception algorithm is characterized in that 3D scanning is carried out on a line where a subway vehicle runs through a multi-line laser radar, modeling is carried out on the surrounding environment, and a training sample set is constructed on the possible obstacles such as rail vehicles, pedestrians, various tools and instruments which are possibly omitted on the line, common invasion foreign matters and the like on the basis of a deep learning algorithm. The point cloud perception algorithm can adopt an apollo perception algorithm in the prior art and the like.
Acquiring point cloud data by using a laser radar detection device 1, a millimeter wave radar detection device 3 and the like; the point cloud data is a digital representation of three-dimensional data, which in practical implementations may also contain additional source data for each point. The point cloud data can be filtered by adopting a filter, the data volume of processing is reduced, and common filters include:
voxel grid filter: and setting the three-dimensional size of each grid.
A through filter: and according to the priori knowledge, creating point cloud data cut by filtering, and setting a filtering axis and a filtering interval.
An outlier removal filter: the outliers are removed. The filtering algorithm of the outlier removal filter is: for each point within the point cloud, the distances of all neighboring points are calculated, and then the average distance is calculated. By assuming a gaussian distribution, if the distances of all points are outside of ± standard deviation of the mean of the gaussian distribution, it is considered as an outlier and removed from the point cloud.
The multivariate sensor fusion algorithm, the point cloud perception algorithm, the neural network (CNN) algorithm and other algorithms can all adopt related algorithms in the prior art.
The working principle of the invention is as follows: the invention aims to provide an active obstacle detection device for a metro vehicle, which can utilize a millimeter wave and laser dual-radar detection device when the metro vehicle runs, make up for the deficiency through data fusion, realize active high-precision sensing and early warning on all-weather and all-working-condition obstacles in a line area, and protect the metro vehicle from running in full operation and full-automatic driving.
The above-mentioned embodiments are only for illustrating the technical ideas and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and to carry out the same, and the present invention should not be limited to the embodiments, i.e. the equivalent changes or modifications made in the spirit of the present invention are still within the scope of the present invention.
Claims (6)
1. An active obstacle detection system for a metro vehicle is characterized by comprising a laser radar detection device, a millimeter wave radar detection device and a data fusion and control platform; wherein:
the laser radar detection device transmits laser beams to the obstacles, and compares signals reflected by the obstacles with the transmitted signals to acquire position, speed, distance and angle information of the obstacles;
the millimeter wave radar detection device transmits millimeter waves to the obstacle, and acquires the shape, position, speed, distance and angle information of the obstacle according to the echo characteristics of the obstacle;
the data fusion and control platform comprises a processor, the processor receives information from the laser radar detection device and the millimeter wave radar detection device, performs data fusion calculation processing, and outputs signals to the subway vehicle control and management system.
2. A metro vehicle active obstacle detection system according to claim 1, wherein the millimeter wave radar detection means comprises a long distance millimeter wave radar and a short distance millimeter wave radar; the long-distance millimeter wave radar is used for detecting obstacles between 30 meters and 200 meters; the short-range millimeter wave radar is used for detecting obstacles between 0 and 30 meters.
3. A subway vehicle active obstacle detection method is characterized in that a laser radar detection device is adopted to emit laser beams to an obstacle, and signals reflected by the obstacle are compared with the emitted signals to obtain the position, speed, distance and angle information of the obstacle; the method comprises the steps that millimeter waves are transmitted to an obstacle by a millimeter wave radar detection device, and the shape, position, speed, distance and angle information of the obstacle is obtained according to the echo characteristics of the obstacle; and a data fusion and control platform provided with a processor is adopted, the processor inputs the information of the obstacles acquired by the laser radar detection device and the millimeter wave radar detection device, data fusion calculation is carried out, the obstacles are identified, and signals are sent to a subway vehicle control and management system.
4. A method for detecting active obstacles of metro vehicles according to claim 3, wherein the millimeter wave radar detection means employs two millimeter wave radars, a long-distance millimeter wave radar and a short-distance millimeter wave radar; detecting obstacles between 30 meters and 200 meters by using the long-distance millimeter wave radar; and detecting the obstacle between 0 and 30 meters by using the short-distance millimeter wave radar.
5. A method for detecting active obstacles of metro vehicles according to claim 3 or 4, characterized in that the processor adopts a multivariate sensor fusion algorithm for data fusion processing.
6. A method for detecting active obstacles of metro vehicles according to claim 3 or 4, wherein the processor adopts a point cloud sensing algorithm to identify obstacles.
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| CN113442915A (en) * | 2021-08-17 | 2021-09-28 | 北京理工大学深圳汽车研究院(电动车辆国家工程实验室深圳研究院) | Automatic obstacle avoidance antenna |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111273268A (en) * | 2020-01-19 | 2020-06-12 | 北京百度网讯科技有限公司 | Obstacle type identification method and device and electronic equipment |
| CN111273268B (en) * | 2020-01-19 | 2022-07-19 | 北京百度网讯科技有限公司 | Automatic driving obstacle type identification method and device and electronic equipment |
| CN111413694A (en) * | 2020-04-27 | 2020-07-14 | 江苏协和电子股份有限公司 | Double-pulse radar |
| CN112053565A (en) * | 2020-11-09 | 2020-12-08 | 江苏量动信息科技有限公司 | Laser radar identification system |
| CN112347999A (en) * | 2021-01-07 | 2021-02-09 | 深圳市速腾聚创科技有限公司 | Obstacle recognition model training method, obstacle recognition method, device and system |
| CN113511236A (en) * | 2021-08-11 | 2021-10-19 | 上海无线电设备研究所 | High-precision sensing equipment and sensing method for motion state of rail transit train |
| CN113511236B (en) * | 2021-08-11 | 2023-02-28 | 上海无线电设备研究所 | High-precision sensing equipment and sensing method for motion state of rail transit train |
| CN113442915A (en) * | 2021-08-17 | 2021-09-28 | 北京理工大学深圳汽车研究院(电动车辆国家工程实验室深圳研究院) | Automatic obstacle avoidance antenna |
| CN113442915B (en) * | 2021-08-17 | 2022-07-15 | 北京理工大学深圳汽车研究院(电动车辆国家工程实验室深圳研究院) | Automatic obstacle avoidance antenna |
| CN117590358A (en) * | 2024-01-15 | 2024-02-23 | 吉林瑞电科技有限公司 | Obstacle detection equipment with flange type sealing cover structure |
| CN117590358B (en) * | 2024-01-15 | 2024-04-05 | 吉林瑞电科技有限公司 | Obstacle detection equipment with flange type sealing cover structure |
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Application publication date: 20191220 |