CN110412564A - A kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion - Google Patents
A kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion Download PDFInfo
- Publication number
- CN110412564A CN110412564A CN201910689859.0A CN201910689859A CN110412564A CN 110412564 A CN110412564 A CN 110412564A CN 201910689859 A CN201910689859 A CN 201910689859A CN 110412564 A CN110412564 A CN 110412564A
- Authority
- CN
- China
- Prior art keywords
- sensor
- railway carriage
- identification
- data
- radar
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C11/00—Photogrammetry or videogrammetry, e.g. stereogrammetry; Photographic surveying
-
- 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
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/06—Systems determining position data of a target
- G01S13/08—Systems for measuring distance only
-
- 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
- G01S13/86—Combinations of radar systems with non-radar systems, e.g. sonar, direction finder
- G01S13/867—Combination of radar systems with cameras
-
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
Abstract
The identification of train railway carriage and distance measuring method that the invention discloses a kind of based on Multi-sensor Fusion, comprising the following steps: S1 dual sensor is acquired and handles to target area data, obtains respective normal data；S2 builds data fusion platform according to respective normal data, and obtains effective target region；S3 is identified and is detected to target according to effective target region；S4 extracts the range information of target railway carriage in effective target region, by obtain whether target railway carriage information and corresponding range information real-time delivery to front end.The present invention can automatically measure train tailstock portion and provide the distance parameter to distance between trailer skin, and in real time for trainman, convenient for locomotive in real time, accurately adjust train speed, be finally reached it is reliable, safely mount train to be hung.
Description
Technical field
The invention belongs to traffic control system technical fields, and in particular to a kind of train railway carriage based on Multi-sensor Fusion
Identification and distance measuring method.
Background technique
Currently, goods train goods yard mounting railway carriage using manual method realization, this method by human eye estimate in the way of
Train is measured with to the distance between trailer skin, error is larger, and trainman is inaccurate using distance parameter adjustment speed,
Influence the work quality of railway carriage mounting operation.In addition, due to blocking and influenced with factors such as light by season, weather, so that people
It will receive further influence when work judge distance, so that operating efficiency further decreases.
The driver of train observes by the naked eye after receiving order with pilot to be cooperateed with after resolution according to relative program
It completes, railway carriage to be mounted is distributed on different turnouts, and engine driver is responsible for driving into train to be used in combination to the turnout of trailer skin
Naked eyes judge distance to control speed and complete mounting, and this mounting mode is widely used in national most of freight rail.But people
Work industry has as a drawback that.First, scheduling strategy is complicated, and traffic efficiency is low.Second, manual work controllability is low, easily produces
Raw erroneous judgement and carelessness, and work accident is caused (when speed too fast (being greater than 5km/h), railway carriage collision to occur, railway carriage ontology is caused to become
Shape；When speed excessively slow (being less than 5km/h), reasoning is insufficient, and railway carriage is caused not mount).Third is influenced seriously by weather, such as
Time at midnight, the manual dispatching ability for being illuminated by the light condition influence will be greatly reduced.
Currently, the advantages of mainstream distance measuring sensor technology such as infrared distance measurement is cheap, course of work safety, but anti-dry
Immunity is poor, and distance is nearly (effective distance is usually within ten meters), and directionality is poor；The advantages of laser radar range is accurate, disadvantage
It is that the course of work needs to pay attention to human-body safety, and the difficulty made is larger, higher cost, and optical system needs to keep dry
Only, it otherwise will affect measurement；Remote (effective distance is usually within the 100 meters) real-time of millimetre-wave radar ranging distance is good and false-alarm
Rate is high, and visual sensor recognition capability is strong, the disadvantage is that ranging strategy is complicated and precision is low.Due to the influence of environmental factor, such as
Low-visibility conditions, the working site (goods at cold environment caused by seasonal variations (subzero ten degree or so), night rainy day in greasy weather etc.
Factory) non-targeted object causes the complex environment for blocking or interfering, although researcher is in sensor research field by improving system
Hardware performance of uniting and optimization algorithm, or propose new system schema, and greatly improve the performance of single-sensor, however above-mentioned
The accurate sensing capability of single-sensor is still to be improved in complicated traffic operating condition, and researcher gradually begins to focus on thus
Recognition methods combined of multi-sensor information, and the rapid development of the progress of software and hardware technology and mathematical algorithm in recent years, are more
Sensor fusion provides technical and theoretic support.Front can be detected automatically by various algorithms and mathematical model
The distance of target.This controllability is high, high degree of automation control model gradually becomes research hotspot both domestic and external and technology
Tackling key problem field.Therefore to meet course of work needs, the sensor selection includes millimetre-wave radar and monocular RGB visual sensor
The sensor of fusion has complementary advantages, can reduce single dependence and improve performance to reach target.
Summary of the invention
The identification of train railway carriage and distance measuring method that the invention discloses a kind of based on Multi-sensor Fusion, this method is using milli
The mode that metre wave radar is merged with visual sensor, automatically measures train tailstock portion and to distance between trailer skin, and real
When for trainman provide the distance parameter, convenient for locomotive in real time, accurately adjust train speed, be finally reached reliable, safety
Ground mounts train to be hung.
The invention is realized by the following technical scheme: a kind of identification of train railway carriage and ranging side based on Multi-sensor Fusion
Method the described method comprises the following steps:
S1 dual sensor is acquired and handles to target area data, obtains respective normal data；
S2 builds data fusion platform according to respective normal data, and obtains effective target region；
S3 is identified and is detected to target according to effective target region；
S4 extracts the range information of target railway carriage in effective target region, by obtain whether the information of target railway carriage and
Corresponding range information real-time delivery is to remote-terminal controller.
Further, include: in step S1
S11 first sensor acquires front region image data in real time, and exports original image data, meanwhile, second passes
Sensor acquires front region radar data in real time, and exports radar initial data；
S12 is standardized described image initial data and the radar initial data, respectively obtains image mark
Quasi- data and radar normal data execute step S2.
Further, include: in step S12
S121 carries out distortion correction to original image data；
S122 carries out coded treatment to the original image data after distortion correction；
S123 carries out processes pixel to the original image data after carrying out coded treatment, then generates graphics standard number
According to execution step S2.
Further, in step S12 further include:
S124 carries out threshold filter to radar initial data；
S125 carries out Kalman estimator to by the radar initial data of threshold filter, then generates radar criterion numeral
According to execution step S2.
Further, comprising: first sensor installs demarcating module, second sensor installs demarcating module, combined calibrating
Module and time synchronization module, wherein
The first sensor installs demarcating module, for the first sensor to be installed and demarcated；
The second sensor installs demarcating module, for the second sensor to be installed and demarcated；
The combined calibrating module, for carrying out combined calibrating to the first sensor and second sensor；
The time synchronization module, for carrying out time synchronization to the first sensor and second sensor,
Include: in step S2
S21 is installed and is demarcated to first sensor；
S22 is installed and is demarcated to second sensor；
S23 carries out combined calibrating and time synchronization to first sensor and second sensor, obtains effective target region.
Further, the first sensor is camera sensor.
Further, the second sensor is radar sensor.
Further, the radar sensor is millimetre-wave radar.
Further, include: in step S3
S31 training obtains deep neural network object detector；
Front region rail is detected by deep neural network object detector in effective target region by S32, and reduction obtains
Effective measuring area containing target railway carriage.
Further, in step S4, specifically, step S1-S3 is packaged and is integrated, it is obtained in image recognition
In effective measuring area extract complete target railway carriage range information, by obtain whether the information of target railway carriage with it is corresponding away from
From information real-time delivery to remote-terminal controller.
The beneficial effects of the present invention are:
First, by merging for millimeter wave thunder sensor and imaging sensor, single-sensor can be made up by, which greatly improving, exists
Obtain road environment perception information amount defect not abundant enough；
Second, under inclement weather conditions, artificial judgment will receive the severe jamming of the weather conditions such as light low temperature, and millimeter
Wave radar is more powerful in adaptive capacity to environment, and the potentiality of significant increase vehicle environment sensing capability are to adapt to round-the-clock work
Make, including winter (temperature is lower than 30 DEG C of situations), summer, daytime, night, fine day, greasy weather, rainy day, haze sky etc..Compared to only
It manually operates, greatly improves the reliability of environment sensing capability, accuracy and adaptability in the course of work；
Third can scientifically and accurately calculate the distance of target railway carriage rather than operator only by using sensor
By rule of thumb with the fuzzy Judgment that visually observes, to eliminate the inefficient unstable defect of manual control significantly；
Detailed description of the invention
Fig. 1 is a kind of method flow of train railway carriage identification and distance measuring method based on Multi-sensor Fusion of the invention
Figure；
Fig. 2 is that a kind of module of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion of the invention executes
Figure.
Specific embodiment
Technical solution in the embodiment of the present invention that following will be combined with the drawings in the embodiments of the present invention carries out clear, complete
Ground description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on this
Embodiment in invention, every other reality obtained by those of ordinary skill in the art without making creative efforts
Example is applied, shall fall within the protection scope of the present invention.
Shown in referring to Fig.1, the invention is realized by the following technical scheme: the present invention provides one kind to be melted based on multisensor
One embodiment of train the railway carriage identification and distance measuring method of conjunction, the described method comprises the following steps:
S1 dual sensor is acquired and handles to target area data, obtains respective normal data；
S2 builds data fusion platform according to respective normal data, and obtains effective target region；
S3 is identified and is detected to target according to effective target region；
S4 extracts the range information of target railway carriage in effective target region, by obtain whether the information of target railway carriage and
Corresponding range information real-time delivery is to remote-terminal controller.
Shown in referring to Fig.1, in the preferred embodiment of this part, include: in step S1
S11 first sensor acquires front region image data in real time, and exports original image data, meanwhile, second passes
Sensor acquires front region radar data in real time, and exports radar initial data；
S12 is standardized described image initial data and the radar initial data, respectively obtains image mark
Quasi- data and radar normal data execute step S2.
Specifically, radar is millimetre-wave radar, the special Radar Technology of shortwave electromagnetic wave is used, uses a kind of frequency
The special millimetre-wave radar technology for modulating continuous wave (FMCW), by the FMCW signal of capture reflection, radar system acquires in real time
The data such as distance, angle and the relative velocity of target lead object.The video camera (camera sensor) chooses 1024 × 728
Resolution ratio RGB color camera, to acquire color image in real time.Step S12 is the pretreatment carried out to acquired image, right
Radar data is filtered and corrects.
Shown in referring to Fig.1, in the preferred embodiment of this part, include: in step S12
S121 carries out distortion correction to original image data；
S122 carries out coded treatment to the original image data after distortion correction；
S123 carries out processes pixel to the original image data after carrying out coded treatment, then generates normal data, holds
Row step S2.
Shown in referring to Fig.1, in the preferred embodiment of this part, in step S12 further include:
S124 carries out threshold filter to radar initial data；
S125 carries out Kalman estimator to by the radar initial data of threshold filter, then generates radar criterion numeral
According to execution step S2.
Referring to Fig.1 shown in-Fig. 2, in the preferred embodiment of this part, comprising: first sensor installs demarcating module, second
Sensor installs demarcating module, combined calibrating module and time synchronization module, wherein
The first sensor installs demarcating module, for the first sensor to be installed and demarcated；
The second sensor installs demarcating module, for the second sensor to be installed and demarcated；
The combined calibrating module, for carrying out combined calibrating to the first sensor and second sensor；
The time synchronization module, for carrying out time synchronization to the first sensor and second sensor,
Include: in step S2
S21 is installed and is demarcated to first sensor；
S22 is installed and is demarcated to second sensor；
S23 carries out combined calibrating and time synchronization to first sensor and second sensor, obtains effective target region.
Referring to shown in Fig. 2, in the preferred embodiment of this part, the first sensor is camera sensor.
Referring to shown in Fig. 2, in the preferred embodiment of this part, the second sensor is radar sensor.
Referring to shown in Fig. 2, in the preferred embodiment of this part, the radar sensor is millimetre-wave radar.
Shown in referring to Fig.1, in the preferred embodiment of this part, include: in step S3
S31 training obtains deep neural network object detector；
Front region rail is detected by deep neural network object detector in effective target region by S32, and reduction obtains
Effective measuring area containing target railway carriage.
Shown in referring to Fig.1, in the preferred embodiment of this part, in step S4, specifically, being packaged to step S1-S3
With integration, the range information for completing target railway carriage is extracted in the obtained effective measuring area of image recognition, is by what is obtained
The information of no target railway carriage and corresponding range information real-time delivery to front end.
Specifically, above-mentioned steps are packaged and are integrated, it is obtained in image recognition using obtained radar information
The range information for completing target railway carriage is extracted in effective coverage.By obtain whether the information of target railway carriage with it is corresponding distance believe
Real-time delivery is ceased to front end, and the data obtained to assist staff to pass through complete railway carriage and mount work.Preferably, the milli
Metre wave radar uses ARS408-217-77GHz millimetre-wave radar.
The specific work process of the present embodiment:
When train mounts railway carriage, driver responsible train turns to the turnout to trailer skin by straight way and starts the method for the present invention
The relevant device being related to, color image sensor (camera sensor) are sent to processor after acquiring front region RGB image,
Deep neural network frame is built after image preprocessing, is carried out rail identification and is reduced radar monitoring range.Judgement front area
There are target railway carriage or visual angle barriers in domain, and target railway carriage, millimetre-wave radar sensor acquire the objects ahead tailstock if it exists
Distance, speed and angle information be sent to processor, later by information convergence platform to Radar Calibration, camera installation mark
Fixed, combined calibrating and time synchronization.The real-time range information of forward end output objects ahead railway carriage after finally handling；If it exists
Visual angle barrier, then return the information and calculate currently with the real-time range of front obstacle.To assist operator to control vehicle
Speed realizes the railway carriage mounting of rail traffic.
Claims (10)
1. it is a kind of based on Multi-sensor Fusion train railway carriage identification and distance measuring method, which is characterized in that the method includes with
Lower step:
S1 dual sensor is acquired and handles to target area data, obtains respective normal data；
S2 builds data fusion platform according to respective normal data, and obtains effective target region；
S3 is identified and is detected to target according to effective target region；
S4 extracts the range information of target railway carriage in effective target region, by obtain whether the information and correspondence of target railway carriage
Range information real-time delivery to remote-terminal controller.
2. a kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion according to claim 1, feature
It is, includes: in step S1
S11 first sensor acquires front region image data in real time, and exports original image data, meanwhile, second sensor
Acquisition front region radar data in real time, and export radar initial data；
S12 is standardized described image initial data and the radar initial data, respectively obtains graphics standard number
According to radar normal data, execute step S2.
3. a kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion according to claim 2, feature
It is, includes: in step S12
S121 carries out distortion correction to original image data；
S122 carries out coded treatment to the original image data after distortion correction；
S123 carries out processes pixel to the original image data after carrying out coded treatment, then generates graphics standard data, holds
Row step S2.
4. a kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion according to claim 2, feature
It is, in step S12 further include:
S124 carries out threshold filter to radar initial data；
S125 carries out Kalman estimator to by the radar initial data of threshold filter, then generates radar normal data,
Execute step S2.
5. a kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion according to claim 2, feature
It is, comprising: it is same that first sensor installs demarcating module, second sensor installation demarcating module, combined calibrating module and time
Walk module, wherein
The first sensor installs demarcating module, for the first sensor to be installed and demarcated；
The second sensor installs demarcating module, for the second sensor to be installed and demarcated；
The combined calibrating module, for carrying out combined calibrating to the first sensor and second sensor；
The time synchronization module, for carrying out time synchronization to the first sensor and second sensor,
Include: in step S2
S21 is installed and is demarcated to first sensor；
S22 is installed and is demarcated to second sensor；
S23 carries out combined calibrating and time synchronization to first sensor and second sensor, obtains effective target region.
6. a kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion according to claim 5, feature
It is, the first sensor is camera sensor.
7. a kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion according to claim 5, feature
It is, the second sensor is radar sensor.
8. a kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion according to claim 7, feature
It is, the radar sensor is millimetre-wave radar.
9. a kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion according to claim 1, feature
It is, includes: in step S3
S31 training obtains deep neural network object detector；
Front region rail is detected by deep neural network object detector in effective target region by S32, and reduction is contained
The effective measuring area of target railway carriage.
10. a kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion according to claim 1, feature
It is, in step S4, specifically, step S1-S3 is packaged and is integrated, in the obtained effective measuring area of image recognition
It is middle to extract the range information for completing target railway carriage, by obtain whether the information of target railway carriage is passed with corresponding range information in real time
It is handed to remote-terminal controller.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910689859.0A CN110412564A (en) | 2019-07-29 | 2019-07-29 | A kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910689859.0A CN110412564A (en) | 2019-07-29 | 2019-07-29 | A kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110412564A true CN110412564A (en) | 2019-11-05 |
Family
ID=68363779
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910689859.0A Pending CN110412564A (en) | 2019-07-29 | 2019-07-29 | A kind of identification of train railway carriage and distance measuring method based on Multi-sensor Fusion |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110412564A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112526997A (en) * | 2020-12-07 | 2021-03-19 | 中国第一汽车股份有限公司 | Automatic driving perception system and method and vehicle |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0509313D0 (en) * | 2004-06-17 | 2005-06-15 | Daimler Chrysler Ag | Method for hitching a trailer to a motor vehicle |
| US20080150786A1 (en) * | 1997-10-22 | 2008-06-26 | Intelligent Technologies International, Inc. | Combined Imaging and Distance Monitoring for Vehicular Applications |
| EP2484542A1 (en) * | 2011-02-08 | 2012-08-08 | Jost-Werke GmbH | System for automatic adjusting of a gap between a motorcar and a trailer coupled to it |
| US20130033597A1 (en) * | 2011-08-03 | 2013-02-07 | Samsung Electro-Mechanics Co., Ltd. | Camera system and method for recognizing distance using the same |
| CN202807928U (en) * | 2012-10-12 | 2013-03-20 | 中国神华能源股份有限公司 | Auxiliary arm of locating vehicle and locating vehicle |
| EP2581892A1 (en) * | 2011-10-13 | 2013-04-17 | Robert Bosch Gmbh | Distance measuring system and method for measuring distance, in particular between a vehicle and its surroundings |
| GB201307525D0 (en) * | 2013-04-26 | 2013-06-12 | Jaguar Land Rover Ltd | Vehicle hitch assistance system |
| US20140012465A1 (en) * | 2012-07-05 | 2014-01-09 | Uusi, Llc | Vehicle trailer connect system |
| CN205500347U (en) * | 2016-04-07 | 2016-08-24 | 莱芜钢铁集团有限公司 | Empty wagon machine of shunting of tipper and tipper |
| CN206203491U (en) * | 2016-08-31 | 2017-05-31 | 大唐环境产业集团股份有限公司 | A kind of novel car dumper transfer platform is to rail detection system |
| CN107609522A (en) * | 2017-09-19 | 2018-01-19 | 东华大学 | A kind of information fusion vehicle detecting system based on laser radar and machine vision |
| CN108536154A (en) * | 2018-05-14 | 2018-09-14 | 重庆师范大学 | Low speed automatic Pilot intelligent wheel chair construction method based on bioelectrical signals control |
| CN108596081A (en) * | 2018-04-23 | 2018-09-28 | 吉林大学 | A kind of traffic detection method merged based on radar and video camera |
| WO2018183546A1 (en) * | 2017-03-28 | 2018-10-04 | Sri International | Identification system for subject or activity identification using range and velocity data |
| CN108828606A (en) * | 2018-03-22 | 2018-11-16 | 中国科学院西安光学精密机械研究所 | One kind being based on laser radar and binocular Visible Light Camera union measuring method |
| WO2018218486A1 (en) * | 2017-05-31 | 2018-12-06 | Beijing Didi Infinity Technology And Development Co., Ltd. | Devices and methods for recognizing driving behavior based on movement data |
| CN108960183A (en) * | 2018-07-19 | 2018-12-07 | 北京航空航天大学 | A kind of bend target identification system and method based on Multi-sensor Fusion |
| CN109143241A (en) * | 2018-07-26 | 2019-01-04 | 清华大学苏州汽车研究院（吴江） | The fusion method and system of radar data and image data |
| CN109472831A (en) * | 2018-11-19 | 2019-03-15 | 东南大学 | Obstacle recognition range-measurement system and method towards road roller work progress |
| CN109471128A (en) * | 2018-08-30 | 2019-03-15 | 福瑞泰克智能系统有限公司 | A kind of positive sample production method and device |
| CA3028659A1 (en) * | 2017-12-11 | 2019-06-11 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for identifying and positioning objects around a vehicle |
-
2019
- 2019-07-29 CN CN201910689859.0A patent/CN110412564A/en active Pending
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20080150786A1 (en) * | 1997-10-22 | 2008-06-26 | Intelligent Technologies International, Inc. | Combined Imaging and Distance Monitoring for Vehicular Applications |
| GB0509313D0 (en) * | 2004-06-17 | 2005-06-15 | Daimler Chrysler Ag | Method for hitching a trailer to a motor vehicle |
| EP2484542A1 (en) * | 2011-02-08 | 2012-08-08 | Jost-Werke GmbH | System for automatic adjusting of a gap between a motorcar and a trailer coupled to it |
| US20130033597A1 (en) * | 2011-08-03 | 2013-02-07 | Samsung Electro-Mechanics Co., Ltd. | Camera system and method for recognizing distance using the same |
| EP2581892A1 (en) * | 2011-10-13 | 2013-04-17 | Robert Bosch Gmbh | Distance measuring system and method for measuring distance, in particular between a vehicle and its surroundings |
| US20140012465A1 (en) * | 2012-07-05 | 2014-01-09 | Uusi, Llc | Vehicle trailer connect system |
| CN202807928U (en) * | 2012-10-12 | 2013-03-20 | 中国神华能源股份有限公司 | Auxiliary arm of locating vehicle and locating vehicle |
| GB201307525D0 (en) * | 2013-04-26 | 2013-06-12 | Jaguar Land Rover Ltd | Vehicle hitch assistance system |
| CN205500347U (en) * | 2016-04-07 | 2016-08-24 | 莱芜钢铁集团有限公司 | Empty wagon machine of shunting of tipper and tipper |
| CN206203491U (en) * | 2016-08-31 | 2017-05-31 | 大唐环境产业集团股份有限公司 | A kind of novel car dumper transfer platform is to rail detection system |
| WO2018183546A1 (en) * | 2017-03-28 | 2018-10-04 | Sri International | Identification system for subject or activity identification using range and velocity data |
| WO2018218486A1 (en) * | 2017-05-31 | 2018-12-06 | Beijing Didi Infinity Technology And Development Co., Ltd. | Devices and methods for recognizing driving behavior based on movement data |
| CN107609522A (en) * | 2017-09-19 | 2018-01-19 | 东华大学 | A kind of information fusion vehicle detecting system based on laser radar and machine vision |
| CA3028659A1 (en) * | 2017-12-11 | 2019-06-11 | Beijing Didi Infinity Technology And Development Co., Ltd. | Systems and methods for identifying and positioning objects around a vehicle |
| CN108828606A (en) * | 2018-03-22 | 2018-11-16 | 中国科学院西安光学精密机械研究所 | One kind being based on laser radar and binocular Visible Light Camera union measuring method |
| CN108596081A (en) * | 2018-04-23 | 2018-09-28 | 吉林大学 | A kind of traffic detection method merged based on radar and video camera |
| CN108536154A (en) * | 2018-05-14 | 2018-09-14 | 重庆师范大学 | Low speed automatic Pilot intelligent wheel chair construction method based on bioelectrical signals control |
| CN108960183A (en) * | 2018-07-19 | 2018-12-07 | 北京航空航天大学 | A kind of bend target identification system and method based on Multi-sensor Fusion |
| CN109143241A (en) * | 2018-07-26 | 2019-01-04 | 清华大学苏州汽车研究院（吴江） | The fusion method and system of radar data and image data |
| CN109471128A (en) * | 2018-08-30 | 2019-03-15 | 福瑞泰克智能系统有限公司 | A kind of positive sample production method and device |
| CN109472831A (en) * | 2018-11-19 | 2019-03-15 | 东南大学 | Obstacle recognition range-measurement system and method towards road roller work progress |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112526997A (en) * | 2020-12-07 | 2021-03-19 | 中国第一汽车股份有限公司 | Automatic driving perception system and method and vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3885794A1 (en) | Track and road obstacle detecting method | |
| CN105711597B (en) | Front locally travels context aware systems and method | |
| Pomerleau | Visibility estimation from a moving vehicle using the RALPH vision system | |
| CN109552366B (en) | Intelligent detection and alarm system for locomotive-mounted railway obstacles and early warning method thereof | |
| CN109359409A (en) | A kind of vehicle passability detection system of view-based access control model and laser radar sensor | |
| CN109017780A (en) | A kind of Vehicular intelligent driving control method | |
| WO2021031469A1 (en) | Vehicle obstacle detection method and system | |
| CN104290753A (en) | Tracking and predicting device of motion state of front vehicle and predicating method thereof | |
| CN103057470B (en) | A kind of vehicle peccancy line ball pre-tip device and reminding method thereof | |
| CN105955259A (en) | Monocular vision AGV accurate positioning method and system based on multi-window real-time range finding | |
| CN109910955B (en) | Rail transit tunnel barrier detection system and method based on transponder information transmission | |
| CN209765730U (en) | vehicle type recognition system | |
| CN103909930A (en) | Method for auxiliary control of traveling along with vehicle ahead | |
| CN107380163A (en) | Automobile intelligent alarm forecasting system and its method based on magnetic navigation | |
| US20120163671A1 (en) | Context-aware method and apparatus based on fusion of data of image sensor and distance sensor | |
| CN110091875A (en) | Deep learning type intelligent driving context aware systems based on Internet of Things | |
| CN101941399A (en) | Vehicle lane change auxiliary device and auxiliary judgment method thereof | |
| CN113799852B (en) | Intelligent active obstacle identification protection method supporting dynamic mode switching | |
| CN111016903B (en) | System and method for guaranteeing driving safety of automatic driving vehicle in rainy days | |
| CN111506069B (en) | All-weather all-ground crane obstacle identification system and method | |
| CN115257784A (en) | Vehicle-road cooperative system based on 4D millimeter wave radar | |
| CN110806411A (en) | Unmanned aerial vehicle rail detecting system based on line structure light | |
| DE102015207026A1 (en) | Method and device for controlling a detection system for detecting an environment of a vehicle | |
| CN113420687A (en) | Method and device for acquiring travelable area and vehicle | |
| CN204124125U (en) | A kind of front vehicles state of kinematic motion follows the trail of prediction unit |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191105 |
|
| RJ01 | Rejection of invention patent application after publication |