CN111674394B - Automatic driving following keeping method capable of realizing microscopic regulation and control - Google Patents
Automatic driving following keeping method capable of realizing microscopic regulation and control Download PDFInfo
- Publication number
- CN111674394B CN111674394B CN202010518998.XA CN202010518998A CN111674394B CN 111674394 B CN111674394 B CN 111674394B CN 202010518998 A CN202010518998 A CN 202010518998A CN 111674394 B CN111674394 B CN 111674394B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- distance
- following
- regulation
- information
- 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.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000001514 detection method Methods 0.000 claims abstract description 30
- 238000004364 calculation method Methods 0.000 claims abstract description 20
- 239000002184 metal Substances 0.000 claims description 14
- 239000003550 marker Substances 0.000 claims description 12
- 238000004088 simulation Methods 0.000 claims description 10
- 238000005259 measurement Methods 0.000 claims description 7
- 230000008569 process Effects 0.000 claims description 7
- 238000009434 installation Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000004891 communication Methods 0.000 claims description 3
- 238000012937 correction Methods 0.000 claims description 3
- 239000000463 material Substances 0.000 claims description 2
- 239000003973 paint Substances 0.000 claims description 2
- 230000000737 periodic effect Effects 0.000 claims 1
- 238000003384 imaging method Methods 0.000 description 5
- 238000004422 calculation algorithm Methods 0.000 description 2
- 239000011248 coating agent Substances 0.000 description 2
- 238000000576 coating method Methods 0.000 description 2
- 230000001276 controlling effect Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 230000001680 brushing effect Effects 0.000 description 1
- 238000001599 direct drying Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 238000003825 pressing Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 230000003746 surface roughness Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/14—Adaptive cruise control
- B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
- B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/12—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to parameters of the vehicle itself, e.g. tyre models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
- B60W60/001—Planning or execution of driving tasks
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2300/00—Indexing codes relating to the type of vehicle
-
- B60W2420/408—
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Abstract
The invention discloses an automatic driving and following keeping method capable of realizing microscopic regulation, which comprises the following steps: step 1: inputting required parameter information; step 2: the range radar carries out panoramic range finding to obtain range finding information; step 3; the mark detection device detects and generates corresponding image information; and 4, step 4: calculating the distance from the mark detection device to the corresponding mark and correcting; and 5: the corrected distance is combined with the calculated expected distance to judge whether the track regulation is needed; and 6: establishing a calculation model of adjusting grades, calculating a real-time adjusting angle, and then executing a next steering step; and 7: and establishing a model to calculate the target speed required by the vehicle and control the vehicle speed. The invention controls the running track of the vehicle to run in one lane, keeps the following distance in a safe range all the time, has high regulation and control precision and obviously improves the traffic capacity in unit distance.
Description
Technical Field
The invention relates to an automatic driving, following and maintaining method capable of realizing microcosmic regulation and control.
Background
In the field of automatic driving, a vehicle automatically keeps a driving direction in a lane and automatically keeps a certain following distance with a front vehicle through an automatic following mode, so that the driving stability of the vehicle and the control of the distance between the vehicle can be effectively ensured, no matter a detection device or an image acquisition device on the vehicle is adopted in the prior art, environmental information is acquired to realize following control, or a road network monitoring unit for detecting and sending information is arranged on a roadside to provide data for the vehicle, the accuracy of controlling the following control is insufficient, the change of the relative position of the vehicle and the speed required by the following of the vehicle is easily influenced by severe environments, such as rain, snow, darkness and the like, the resolution and the reliability of the image acquisition device are linearly reduced, a lane line is shielded by snow in case, the information of the lane position cannot be passed by the general image detection mode, the error of the following control is very large, and particularly in the process of high-speed movement, even a dozen centimeters of error can cause serious consequences. In addition, in the prior art, the control on the speed and the steering angle is not fine enough, the limit of the controllable minimum steering angle of the vehicle and the like is not considered, and the control precision of automatic following is influenced.
Disclosure of Invention
The invention aims to provide an automatic driving following keeping method capable of realizing microcosmic regulation and control, and aims to solve the problems that the control precision of a following process of a target vehicle is low and the target vehicle is easily influenced by external severe environment factors in the prior art.
The automatic driving following keeping method capable of realizing micro regulation comprises the following steps:
step 1: inputting vehicle type information, lane information and installation parameter information of each mark detection device;
and 2, step: the range radar carries out panoramic range finding to obtain range finding information;
and 4, step 4: calculating the distance from the mark detection device to the corresponding mark according to the acquired and input information, and correcting the result according to the completed simulation experiment data;
and 5: setting an expected distance from the mark detection device to a corresponding mark, judging whether track regulation is needed or not according to the combination of the corrected distance obtained in the step 4 and the expected distance, and executing the step 6 if the track regulation is needed, or executing the step 7 if the track regulation is not needed;
and 6: establishing a calculation model of the adjustment grade, calculating the adjustment grade according to the distance obtained in the step 4 and the step 5 and the expected distance, further calculating the real-time adjustment angle of the vehicle, and then executing steering and the step 7;
and 7: and establishing a model related to the vehicle speed and the change of the following distance, calculating a target speed to be reached by the vehicle according to the model, and controlling the running speed of the vehicle.
Preferably, the distance measurement information is obtained by panoramic distance measurement around the vehicle, and D = { D = f ，D b ，D l ，D r And v is set as the current vehicle speed in the step 7 0 Establishing a historical track calculation cycle time table t = { t = { t } －k ，t -k+1 ，...，t 0 Where t is 0 Indicating the most recent distance measurement and obtaining the corresponding time
Setting standard front vehicle and following vehicle distance D f Then, the distance difference Δ S is calculated by the following equation: />
The average acceleration of the vehicle relative to the vehicle in front is then:
the target speed of the vehicle is:
preferably, the vehicle type information entered in step 1 includes a vehicle width W car Whether or not to carry cargo, the ultimate width W of the cargo lim (ii) a The mounting parameters of the mark detection means comprise a resolution σ; the mounting position is higher than the ground by h, the outward viewing angle theta is larger than the distance L between the mark detection devices on the two sides; the lane information includes a lane width list W road [level]＝{w1，w2，w3，...}。
Preferably, in step 4, the position of the central angle located at the view angle is defined as O, a coordinate system is established with O as an origin, coordinate axes point from the inside to the outside, that is, the inside is negative and the outside is positive, and the number of pixels α from the central position of the metal thin strip to O is recorded with pixels as a minimum unit scale, and then: d p = h × tan θ + (± α) × σ; according to big data simulation, a sample database is established, and a sample standard deviation is obtained according to the sample database:
where n is the number of samples involved in the calculation,
measured values obtained for corresponding samples, d p(i) The correction value is based on the theoretical calculation value obtained for the corresponding sample>
Preferably, the specific process of calculating and judging in step 5 is to calculate the expected distance and the difference between the expected distance and the corrected distance according to the following formula:
the judgment standard is as
Maintaining the driving track; when/is>
The vehicle is steered to adjust the trajectory until its relationship to the desired value is within the tolerance.
Preferably, the specific process of step 6 is:
setting the regulation angle range as +/-omega and the regulation grade as m, and establishing a historical track time table t = { t = (t) } －k ，t -k+1 ，...，t 0 Where t is 0 Representing the time of the most recently acquired image and obtaining the corresponding time
The calculation model of the adjustment levels is as follows:
here, the maximum adjustment level is set according to the minimum angle at which the vehicle can be precisely controlled, and the conditions are set as follows: setting maximum adjustmentWhen the level K =10, if | m | > 10, it is set to | m | =10; determining a real-time adjustment angle of the vehicle from this
Preferably, the marker is a metal thin strip laid on the lane marking line, and the surface of the metal thin strip is rough and coated with paint which is the same as the material of the marking line.
Preferably, the mark detection device is a synthetic aperture radar, and the radar frequency of the synthetic aperture radar is selected to be 50-100GHz, and the communication frequency band is avoided.
The invention has the following advantages: in the method, the marker is arranged on the lane marking line of the road to assist the vehicle in positioning calculation, so that the control precision is improved, the influence of severe environments such as too dark light or rain and snow weather on the system is avoided, and meanwhile, the algorithm related to the position of the marker is adopted to accurately position the position of the vehicle in the lane and accurately control the steering angle and the vehicle speed. The improvements improve the microcosmic automatic driving positioning precision to centimeter level, improve the vehicle lane change positioning calculation to centimeter level, facilitate shortening the following distance of the automatic driving vehicle in a following state, and provide a solution for meeting the requirement of an L4-L5 level automatic driving technology on the vehicle lane positioning capability.
Drawings
Figure 1 is a schematic view of the invention in use when the vehicle is travelling in a lane,
figure 2 is a comparison of the vehicle to lane marker deviation results in a simulation experiment of the present invention,
figure 3 is a comparison of the following distance results in a simulation experiment of the present invention,
in the figure: 1. lane marking, 2, marker, 3, vehicle, 4, mark detection device.
Detailed Description
The following detailed description of the embodiments of the present invention will be given in order to provide those skilled in the art with a more complete, accurate and thorough understanding of the inventive concept and technical solutions of the present invention.
As shown in fig. 1 to 3, the present invention provides an automatic driving following keeping method capable of realizing microscopic control, which requires a marker 2 to be disposed on a road marking, and a marker detection device 4 and a 360 ° panoramic ranging radar to be mounted on a vehicle 3. And assembling a 360-degree panoramic ranging radar around the target vehicle 3, and detecting the distance of the moving target in the surrounding environment of the target to obtain the distance between the target vehicle 3 and other surrounding vehicles 3.
The marker 2 can be a metal thin strip, the specification of the metal thin strip is controlled within 0.2X20.0X100.0mm, the surface is rough, the thickness of the thin strip is reduced as thin as possible, the surface roughness of the metal thin strip is maintained, the use is not influenced after oxidation, the backscattering capacity of a target in a frequency band is enhanced, and therefore the target form can be reproduced through an imaging algorithm. When marking lines are drawn for the lanes, for the virtual white lines, respectively laying a thin metal strip from the head to the tail of the marking lines and fixing, brushing a thin layer of coating on the surface, and blending the thin layer of coating into the marking lines while avoiding direct drying; and for the solid white line driving area, metal thin strips are laid in the solid white lines at equal intervals.
The mark detection device 4 may employ a synthetic aperture radar or an infrared finder. The synthetic aperture radar frequency is selected to be 50-100GHz, and a communication frequency band is avoided, the frequency band is adopted, the meaning of the frequency band is that the distance direction imaging resolution of the radar is directly related to the carrier bandwidth, and in order to meet the requirement of metal thin strip imaging, at least enough pixels are occupied by an imaging target in the distance direction so as to ensure the requirement of later-stage calculation; meanwhile, the frequency band has small attenuation under the rain and snow conditions, so that all-weather use conditions can be met, and the SAR imaging of the target can be realized by combining azimuth acquisition data.
The four corners around the target vehicle 3 are provided with mark detection devices 4, and care is taken to ensure that the detection range of the detection device covers an area with a radius of more than 0.5m by taking the installation position as the center. According to different road grade lane width, simultaneously according to the car width difference of different motorcycle types, the actual field of vision radius adjusts as required, and its basic principle is under the condition of 3 one side line pressing of vehicle, and the opposite side still can detect corresponding lane marking.
According to the above-described device and the marker 2, the present automatic driving control method includes the steps of:
step 1: inputting vehicle type information, lane information and installation parameter information of each mark detection device; the vehicle type information includes vehicle width W car Whether or not to carry cargo, the ultimate width W of the cargo lim (ii) a The mounting parameters of the mark detection device 4 include a resolution σ; the height h between the installation position and the ground, the outward viewing angle theta and the distance L between the mark detection devices 4 on the two sides; the lane information includes a lane width list W road [level]＝{w1，w2，w3，...}。
Step 2: carrying out panoramic ranging by a ranging radar to obtain ranging information; the distance measurement information is obtained by panoramic distance measurement around the vehicle 3, and D = { D = f ，D b ，D l ，D r }。
And 4, step 4: and calculating the distance from the mark detection device 4 to the corresponding mark 2 according to the acquired and input information, and correcting the result according to the completed simulation experiment data. The details are as follows.
Setting the central angle position at the view angle as O, establishing a coordinate system by taking the O as an origin, enabling coordinate axes to point to the outside from the inner side, namely the inner side is a negative outer side and is a positive side, and recording the pixel number alpha from the central position of the metal thin strip to the O by taking a pixel as the minimum unit scale, wherein the method comprises the following steps: d p ＝h×tanθ+(±α)×σ；
According to big data simulation, a sample database is established, and a sample standard deviation is obtained according to the sample database:
where n is the number of samples involved in the calculation,
obtained for corresponding samplesMeasured value, d p(i) The correction value is based on the theoretical calculation value obtained for the corresponding sample>
And 5: and setting an expected distance from the mark detection device 4 to the corresponding mark 2, judging whether track regulation is needed or not according to the combination of the corrected distance obtained in the step 4 and the expected distance, and executing a step 6 if the track regulation is needed, or executing a step 7 if the track regulation is not needed. The details are as follows.
The specific process of calculation and judgment is to calculate the expected distance and the difference between the expected distance and the corrected distance according to the following formula:
the judgment standard is as
Maintaining the driving track; when/is>
The vehicle 3 turns to adjust the trajectory until its relationship with the desired value is within the allowable error.
Step 6: and (5) establishing a calculation model of the adjustment grade, calculating the adjustment grade according to the distance obtained in the step (4) and the step (5) and the expected distance, further calculating the real-time adjustment angle of the vehicle (3), and then executing steering and the step (7). The specific procedure for calculating the adjustment level and the steering angle is as follows.
Setting the regulation angle range as +/-omega and the regulation grade as m, and establishing a historical track time table t = { t = (t) } －k ，t -k+1 ，...，t 0 H, where t 0 Representing the time of the most recently acquired image and obtaining the corresponding time
The calculation model for the adjustment levels is as follows: />
Here, the maximum adjustment level is set according to the minimum angle at which the vehicle 3 can be precisely controlled, and the conditions are set as follows: setting the maximum adjustment level K =10, and if | m | > 10, setting | m | =10; from this, a real-time adjustment angle of the vehicle 3 is determined
And 7: a model relating the speed of the vehicle 3 and the change in the following distance is established, and a target speed to be achieved by the vehicle 3 is calculated therefrom. The method comprises the following specific steps:
setting the current vehicle speed as v 0 Establishing a historical track calculation cycle time table t = { t = { t } －k ，t -k+1 ，...，t 0 Where t is 0 Indicating the most recent distance measurement and obtaining the corresponding time
Setting standard front vehicle following vehicle distance D f Then, the distance difference Δ S is calculated by the following equation:
the average acceleration of the vehicle 3 relative to the preceding vehicle 3 is then:
the target speed of the vehicle 3 is:
and finally, carrying out speed control on the vehicle 3 according to the calculated target speed, so that the vehicle can follow the front vehicle and keep a safe vehicle distance.
The basic parameters for performing the simulation experiment in the scheme are configured as follows.
Selecting a simulated road section: ninghui highway section, single lane width W road [1]＝3.75m；
Selecting a simulated vehicle type: maximum width W car ＝2.5m；
Installation parameters: the mounting height h =1.5m; exterior viewing angle θ =15 °; the distance L =2.4m between the left radar and the right radar;
standard front car following car distance D f ＝20m；
The speed of the simulated running is 80km/h at a constant speed, the speed interval [70, 90] km/h, and the number S of simulated running kilometers is = {5km,10km,15km,20km and 40km }.
The simulation results are shown in fig. 2 and 3, the abscissa of fig. 2 and 3 represents the road distance of the experimental road section, and the results show that under the condition that the width of the primary road is 3.75m, the maximum allowable unilateral deviation distance of the vehicle with the width of 2.5m is less than 60 cm, so that the vehicle can always run in a given lane, and after the trajectory of the deviated vehicle is actively regulated, the vehicle running trajectory can always be kept near the center distance and controlled to run in a single-lane, and the following distance is always kept in a safe range, so that the traffic capacity per unit distance is obviously improved.
While the invention has been described in connection with the drawings, it is to be understood that the invention is not limited to the precise arrangements and instrumentalities disclosed, but is intended to cover all modifications, equivalents and alternatives falling within the spirit and scope of the invention as defined by the appended claims.
Claims (3)
1. An automatic driving following keeping method capable of realizing microscopic regulation and control is characterized in that: comprises the following steps:
step 1: inputting vehicle type information, lane information and installation parameter information of each mark detection device;
step 2: the range radar carries out panoramic range finding to obtain range finding information;
and step 3: the mark detection device (4) detects a mark (2) arranged on the lane marking line (1) and generates corresponding image information;
and 4, step 4: calculating the distance from the mark detection device (4) to the corresponding mark (2) according to the acquired and input information, and correcting the result according to the completed simulation experiment data;
and 5: setting an expected distance from the mark detection device (4) to the corresponding mark (2), judging whether track regulation is needed or not according to the combination of the corrected distance obtained in the step (4) and the expected distance, and executing a step 6 if the track regulation is needed, otherwise executing a step 7;
step 6: establishing a calculation model of the adjustment grade, calculating the adjustment grade according to the distance obtained in the step 4 and the step 5 and the expected distance, further calculating the real-time adjustment angle of the vehicle (3), and then executing steering and the step 7;
and 7: establishing a model related to the speed and the following distance change of the vehicle (3), calculating a target speed to be reached by the vehicle (3) according to the model, and controlling the running speed of the vehicle (3);
the ranging information is obtained by panoramic ranging around the vehicle (3) to obtain D = { D = { (D) } f ，D b ，D l ，D r And v is set as the current vehicle speed in the step 7 0 Establishing historical track calculations periodic schedule t = { t = －k ，t -k+1 ，...，t 0 Where t is 0 Indicating the most recent distance measurement and obtaining the corresponding time
Setting standard front vehicle and following vehicle distance D f Then, the distance difference Δ S is calculated by the following equation:
the vehicle (3) is relatively forwardThe average acceleration of the vehicle (3) is:
the target speed of the vehicle (3) is:
the vehicle type information recorded in the step 1 comprises vehicle width W car Whether or not to carry cargo, the ultimate width W of the cargo lim (ii) a The mounting parameters of the mark detection devices (4) comprise resolution sigma, height h between a mounting position and the ground, an outward viewing angle theta and a distance L between the mark detection devices (4) on two sides; the lane information includes a lane width list W road [level]＝{w1，w2，w3，...}；
In the step 4, the central angle position of the view angle is determined as O, a coordinate system is established with O as an origin, coordinate axes point from the inside to the outside, that is, the inside is negative and the outside is positive, meanwhile, pixels are used as the minimum unit scale, the marker (2) is a metal thin strip, and the number of pixels α from the central position of the metal thin strip to O is recorded, so that: d is a radical of p ＝h×tanθ+(±α)×σ；
According to big data simulation, a sample database is established, and a sample standard deviation is obtained according to the sample database:
where n is the number of samples involved in the calculation,
measured values obtained for corresponding samples, d p(i) The correction value is based on the theoretical calculation value obtained for the corresponding sample>
The specific process of calculating and judging in step 5 is to calculate the difference between the expected distance and the corrected distance according to the following formula:
the judgment standard is as
Maintaining the driving track; when/is>
The vehicle (3) is steered to adjust the trajectory until its relationship with the desired value is within the tolerance;
the specific process of the step 6 is as follows:
setting the regulation angle range as +/-omega and the regulation grade as m, and establishing a historical track time table t = { t = (t) } -k ，t -k+1 ，...，t 0 H, where t 0 Representing the time of the most recently acquired image and obtaining the corresponding time
The calculation model for the adjustment levels is as follows:
the maximum adjustment level is set here according to the minimum angle at which the vehicle (3) can be precisely controlled, the conditions being set as follows: setting the maximum adjustment level K =10, and if | m | > 10, setting | m | =10; from this, the real-time adjustment angle of the vehicle (3) is determined
2. The automatic driving-following-maintaining method capable of realizing micro-control according to claim 1, characterized in that: the marker (2) is a metal thin strip paved on the lane marking line (1), and the surface of the metal thin strip is rough and coated with paint which is the same as the marking line in material.
3. The automatic driving-following-maintaining method capable of achieving the microscopic control according to claim 2, characterized in that: the mark detection device (4) is a synthetic aperture radar, the radar frequency of the synthetic aperture radar is selected to be 50-100GHz, and the communication frequency band is avoided.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010518998.XA CN111674394B (en) | 2020-06-09 | 2020-06-09 | Automatic driving following keeping method capable of realizing microscopic regulation and control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010518998.XA CN111674394B (en) | 2020-06-09 | 2020-06-09 | Automatic driving following keeping method capable of realizing microscopic regulation and control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111674394A CN111674394A (en) | 2020-09-18 |
| CN111674394B true CN111674394B (en) | 2023-04-11 |
Family
ID=72435655
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010518998.XA Active CN111674394B (en) | 2020-06-09 | 2020-06-09 | Automatic driving following keeping method capable of realizing microscopic regulation and control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111674394B (en) |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050125121A1 (en) * | 2003-11-28 | 2005-06-09 | Denso Corporation | Vehicle driving assisting apparatus |
| CN102295004B (en) * | 2011-06-09 | 2013-07-03 | 中国人民解放军国防科学技术大学 | Lane departure warning method |
| JP6294905B2 (en) * | 2016-03-31 | 2018-03-14 | 株式会社Ｓｕｂａｒｕ | Display device |
| JP6589760B2 (en) * | 2016-07-07 | 2019-10-16 | 株式会社デンソー | Vehicle control device |
| CN109597317B (en) * | 2018-12-26 | 2022-03-18 | 广州小鹏汽车科技有限公司 | Self-learning-based vehicle automatic driving method and system and electronic equipment |
| CN110531661B (en) * | 2019-08-22 | 2021-06-22 | 浙江吉利汽车研究院有限公司 | Automatic following control method, device and equipment for vehicle |
| CN110750877B (en) * | 2019-09-27 | 2024-05-03 | 西安理工大学 | Vehicle following behavior prediction method under Apollo platform |
-
2020
- 2020-06-09 CN CN202010518998.XA patent/CN111674394B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN111674394A (en) | 2020-09-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP4756931B2 (en) | Digital lane mark creation device | |
| CN111619589B (en) | Automatic driving control method for complex environment | |
| CN109062209A (en) | A kind of intelligently auxiliary Ride Control System and its control method | |
| US5964822A (en) | Automatic sensor azimuth alignment | |
| EP0446902B1 (en) | Automatic travelling apparatus | |
| KR101729912B1 (en) | Method for estimating the roll angle in a travelling vehicle | |
| US20180067494A1 (en) | Automated-vehicle 3d road-model and lane-marking definition system | |
| CN110208842A (en) | Vehicle high-precision locating method under a kind of car networking environment | |
| CN107783535A (en) | Controller of vehicle | |
| CN105676253A (en) | Longitudinal positioning system and method based on city road marking map in automatic driving | |
| CN107615201A (en) | Self-position estimation unit and self-position method of estimation | |
| CN114999228B (en) | Anti-collision method for automatic driving vehicle in severe weather | |
| CN111044073B (en) | High-precision AGV position sensing method based on binocular laser | |
| CN106114357A (en) | Device and method for preventing scratching during turning of vehicle | |
| CN111137298B (en) | Vehicle automatic driving method, device, system and storage medium | |
| CN111176298B (en) | Unmanned vehicle track recording and tracking method | |
| CN114173307A (en) | Roadside perception fusion system based on vehicle-road cooperation and optimization method | |
| CN112526968B (en) | Method for building automatic driving virtual test platform for mapping real world road conditions | |
| CN112147651B (en) | Asynchronous multi-vehicle cooperative target state robust estimation method | |
| CN112937606B (en) | Anti-collision path planning and control method and system for tracking automatic driving vehicle | |
| CN107024687A (en) | A kind of offline method for quickly realizing POS/ laser radar process alignment error calibrations | |
| CN111674394B (en) | Automatic driving following keeping method capable of realizing microscopic regulation and control | |
| CN111650939B (en) | Trajectory control method for automatic driving | |
| JPH09269726A (en) | Preparation of high-precision road map | |
| CN114995491A (en) | Wading driving method and system based on unmanned aerial vehicle |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP01 | Change in the name or title of a patent holder |
Address after: Nanjing Institute of technology, No.1, Yangshan North Road, Qixia District, Nanjing City, Jiangsu Province Patentee after: Nanjing Vocational University of Industry Technology Address before: Nanjing Institute of technology, No.1, Yangshan North Road, Qixia District, Nanjing City, Jiangsu Province Patentee before: NANJING INSTITUTE OF INDUSTRY TECHNOLOGY |
|
| CP01 | Change in the name or title of a patent holder | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240104 Address after: 230000 floor 1, building 2, phase I, e-commerce Park, Jinggang Road, Shushan Economic Development Zone, Hefei City, Anhui Province Patentee after: Dragon totem Technology (Hefei) Co.,Ltd. Address before: Nanjing Institute of technology, No.1, Yangshan North Road, Qixia District, Nanjing City, Jiangsu Province Patentee before: Nanjing Vocational University of Industry Technology |
|
| TR01 | Transfer of patent right |