CN113246968B - Automatic driving automobile accurate parking control method and device - Google Patents
Automatic driving automobile accurate parking control method and device Download PDFInfo
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- CN113246968B CN113246968B CN202110660540.2A CN202110660540A CN113246968B CN 113246968 B CN113246968 B CN 113246968B CN 202110660540 A CN202110660540 A CN 202110660540A CN 113246968 B CN113246968 B CN 113246968B
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- 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
- B60W30/06—Automatic manoeuvring for parking
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- 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
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- 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
- B60W60/0025—Planning or execution of driving tasks specially adapted for specific operations
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- 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
- B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
Abstract
The invention discloses a control method and a device for accurate parking of an automatic driving automobile, wherein the method comprises the following steps: during the parking of the automatic driving automobile, the automatic driving automobile respectively acquires a distance value, an angular velocity value and an acceleration value of the current time; inputting the distance value, the angular velocity value and the acceleration value into a trained Gongworth model by the automatic driving automobile to obtain a track curve of the current time; and the automatic driving automobile respectively calculates the distance value and the angle of the curve to be moved by utilizing the track curve of the current time so as to accurately stop according to the distance value and the angle of the curve.
Description
Technical Field
The invention relates to the technical field of computer software, in particular to a method and a device for controlling accurate parking of an automatic driving automobile.
Background
Along with the increasing improvement of the life quality of people, the automobile becomes an indispensable important component in the life of people when going out, and the automobile brings convenience to people and saves time. However, in real life, people feel tired due to busy work, night stay or long-time driving, and at the moment, traffic accidents are easy to happen when driving automobiles. Even some people who do not pay attention to traffic regulations are drunk to drive, and call receiving and calling, nerves receiving and the like in driving cause serious life threats to other people and the people themselves. Just because these drivers who do not comply with the road traffic regulations put a great strain on our roads and traffic environments, the efficiency of people's trips, especially peak trips, is greatly reduced. To reduce the traffic accident rate and the road load. In 1970, developed countries such as the united states began to apply artificial intelligence to automobiles, and autodrive automobiles were developed, and with the development of big data, autodrive automobiles were pushed to a new height.
In the research of the automatic driving automobile, the research on the efficient parallel parking of the automatic driving automobile is always a difficult point, and particularly, the research focus and the difficult point of current research students are the automatic parking which is fast and avoids obstacles. Currently, there are some existing parking control systems for an autonomous vehicle, for example: the method for controlling the automatic parking of the vehicle based on ultrasonic ranging data processing and a sine function, which is proposed by Paromtchik, the multi-level driver-assisted parking control system based on path planning and a human-computer interface, which is proposed by M.Wada, and the vehicle parking control and the like are realized by using fuzzy logic. In the aspect of data storage, the methods do not need to consume a large amount of space to store and process data, so that the storage cost is high. However, a large amount of nonlinear equations are needed to be calculated, the complexity of calculation time is extremely high, the reaction of controlling the automobile is slow, the implementation in practice is difficult, and the stable automatic obstacle avoidance and parking of the automatic driving automobile are difficult to realize.
The existing automatic parking control system of the automatic driving automobile mainly has a method for realizing control by using fuzzy logic, and the method utilizes a fuzzy logic model and combines information received by various sensors to fuse data of the sensors and put the data into the model for operation, thereby controlling the parking of the automatic driving automobile.
The fuzzy logic control method used by the existing automatic driving automobile is very suitable for a language description scene of a complex system, and meanwhile, the fuzzy logic control method can be used for making and translating human experiences expressed by languages so as to realize a proper automatic control strategy. However, the method has the defects that when the method is used, the algorithm time complexity is high, and the stability of the algorithm is strong, so that in practical application tests, the automatic driving automobile tested by the inventor has the situations of slow response, blockage or collision with other automobiles in a parking space.
Disclosure of Invention
The technical problem solved by the scheme provided by the embodiment of the invention is how to plan the optimal obstacle avoidance parking track under the condition of ensuring the shortest parking time of the automatic driving automobile, and the generated candidate track is subjected to collision detection.
The accurate parking control method for the automatic driving automobile, provided by the embodiment of the invention, comprises the following steps:
during the parking of the automatic driving automobile, the automatic driving automobile respectively acquires a distance value, an angular velocity value and an acceleration value of the current time;
inputting the distance value, the angular velocity value and the acceleration value into a trained Gongworth model by the automatic driving automobile to obtain a track curve of the current time;
and the automatic driving automobile respectively calculates the distance value and the angle of the curve to be moved by utilizing the track curve of the current time so as to accurately stop according to the distance value and the angle of the curve.
According to the embodiment of the invention, the automatic driving automobile accurate parking control device comprises:
the acquisition module is used for respectively acquiring a distance value, an angular velocity value and an acceleration value of the current time during the parking of the automatic driving automobile;
the input module is used for inputting the distance value, the angular velocity value and the acceleration value into a trained Gongoni model to obtain a track curve of the current time;
and the calculation and parking module is used for respectively calculating a curve distance value and an angle to be moved by utilizing the track curve of the current time so as to carry out accurate parking according to the curve distance value and the angle.
According to the scheme provided by the embodiment of the invention, the automatic driving automobile can quickly park in the parallel position, and meanwhile, the automatic driving automobile can be prevented from colliding or scratching with surrounding obstacles or other automobiles.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:
fig. 1 is a flowchart of an automatic automobile precise parking control method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an automatic vehicle precision parking control device according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, and it should be understood that the preferred embodiments described below are only for the purpose of illustrating and explaining the present invention, and are not to be construed as limiting the present invention.
Fig. 1 is a flowchart of an automatic automobile precision parking control method according to an embodiment of the present invention, as shown in fig. 1, including:
step S101: during the parking of the automatic driving automobile, the automatic driving automobile respectively acquires a distance value, an angular velocity value and an acceleration value of the current time;
step S102: inputting the distance value, the angular velocity value and the acceleration value into a trained Gongworth model by the automatic driving automobile to obtain a track curve of the current time;
step S103: and the automatic driving automobile respectively calculates the distance value and the angle of the curve to be moved by utilizing the track curve of the current time so as to accurately stop according to the distance value and the angle of the curve.
Wherein the distance value comprises any one or a combination of: a first distance value of a front left wheel from a front obstacle or a front stop line; a second distance value of the front right wheel from a front obstacle or a front stop line; a third distance value of the rear left wheel from a rear obstacle or a rear stop line; a fourth distance value of the rear right wheel from a rear obstacle or rear stop line. The automatic driving automobile respectively acquiring the distance value, the angular velocity value and the acceleration value of the current time comprises the following steps: measuring a distance value of the current time through a camera of the automatic driving automobile and a laser distance scanner; acquiring an angular speed value of the current time through a single-axis gyroscope sensor of the automatic driving automobile; and measuring the acceleration value of the current time through the three-dimensional acceleration sensor of the automatic driving automobile.
Specifically, the trained Googloux model includes: the automatic driving automobile respectively obtains the driving speed of the automatic driving automobile, the driving distance of the middle point of the rear wheel and the total length value of the available parking space; and the automatic driving automobile trains the initial Gangbort model by utilizing the running speed of the automatic driving automobile, the running distance of the middle point of the rear wheel and the total length value of the available parking space to obtain the trained Gangbort model.
Further, the calculating, by the autonomous vehicle, a curve distance value to be moved using the trajectory curve of the current time includes: the automatic driving automobile calculates an arc length value of the track curve according to the track curve of the current time; and the automatic driving automobile calculates the distance of the curve to be moved according to the arc length value of the track curve.
Further, the step of calculating the angles to be moved by the autonomous vehicle using the trajectory curve of the current time includes: the automatic driving automobile constructs an average total length equation of available parking spaces of the automatic driving automobile at the current time according to the distance value of the current time and the arc length value of the track curve; inputting an average total length equation and time of the available parking space into the initial Golay model by the automatic driving automobile to obtain a plurality of different Golay model values; and the automatic driving automobile calculates the angle to be moved according to the different Golay model values.
Further, the automatic driving automobile parking accurately according to the curve distance value and the angle comprises the following steps: the automatic driving automobile carries out mobile parking processing according to the curve distance value and the angle to obtain a mobile parking result; the automatic driving automobile judges whether the mobile parking result reaches accurate parking; and if the mobile parking result does not reach the accurate parking result, recalculating a new curve distance value and a new angle by the automatic driving automobile until the accurate parking is achieved.
Fig. 2 is a schematic diagram of an automatic precise parking control device for a vehicle according to an embodiment of the present invention, as shown in fig. 2, including: an obtaining module 201, configured to obtain a distance value, an angular velocity value, and an acceleration value at a current time during parking of an autonomous vehicle, respectively; the input module 202 is configured to input the distance value, the angular velocity value, and the acceleration value into a trained okouz model to obtain a trajectory curve of the current time; and the calculating and parking module 203 is configured to calculate a distance value and an angle of a curve to be moved respectively by using the trajectory curve of the current time, so as to perform accurate parking according to the distance value and the angle of the curve.
Wherein the distance value comprises any one or a combination of: a first distance value of a front left wheel from a front obstacle or a front stop line; a second distance value of the front right wheel from a front obstacle or a front stop line; a third distance value of the rear left wheel from a rear obstacle or a rear stop line; a fourth distance value of the rear right wheel from a rear obstacle or rear stop line.
Further, the obtaining module 201 includes: measuring a distance value of the current time through a camera and a laser distance scanner; acquiring an angular speed value of current time through a single-axis gyroscope sensor; and measuring the acceleration value of the current time through the three-dimensional acceleration sensor.
In order to enable an automatic driving automobile to park more quickly and accurately, the embodiment of the invention utilizes an optimization scheme to determine track parameters in real time in the parking path planning stage of the automatic driving automobile so as to generate candidate parking paths, and mainly comprises the following steps:
the method comprises the following steps that firstly, the maximum rotation angle of an automatic driving automobile is assumed to be 180 degrees, the model of a mainboard controller is AMD _ Geode _ LX800, and a VGA interface integrated camera based on a CCD chip set, a single-axis gyroscope sensor, a wheel shaft encoder, a near distance sensor, a three-dimensional acceleration sensor and a laser distance scanner are mainly used as sensors.
Step two, reading the driving speed of the automatic driving automobile measured by the sensor at the moment as V, and simultaneously reading the scale data of the encoder to obtain that the driving distances of the rear wheel 1 and the rear wheel 2 of the automatic driving automobile at the moment are respectively L1And L2The midpoint of the two rear wheels is denoted by m, and the midpoint m of the rear wheels can be calculated2Has a running distance of
。
And step three, taking the key m at the moment as a reference point of the current vehicle position in the input system. Formula for introducing Gompertz model
Where Y is a curve of a parking trajectory predicted by Gompertz at a certain time, and K represents a width of the trajectory, i.e., a distance between two parallel lines when the vehicle is parallel for the first time and when the parking of the vehicle is completed, for defining
E represents a natural constant, a represents the distance from the end of the vehicle to be parallel to the road line when the vehicle is parked in parallel, b represents the total length of the currently available parking space, and t represents a time variable for the vehicle to travel.
Step four, bringing the total length value of the available parking space acquired by the sensor into a formula of a Gompertz model
In this method, a preliminary action curve L is calculated.
Step five, let t trend to infinity, and bring the value of t into the Gompertz model formula of step three
In (3), the following equation can be derived:
step six, the value obtained by the equation set forth in step four can be used to calculate Y = K, and therefore the value of K can be used as the value of K
I.e. the width of the whole parking lot
Step seven, the corresponding value received by the sensor is brought into
In the method, the track curve Y of the current time can be calculatedt，
Wherein the corresponding values received by the sensors include: the angular velocity received by the single-axis gyroscope sensor, the distance between the short-distance sensor and the obstacle, the distance between the camera and the laser distance scanner and the distance between the camera and the stop line are measured, and the acceleration value of the automobile at the moment is measured by the three-dimensional acceleration sensor. The track width K value and the available parking length b are obtained through the distance measured by the camera and the laser distance scanner, and the translation distance of the automobile during parking is calculated through the acceleration and the angular speed of the automobile, so that the value a can be obtained.
Step eight, the arc length formula of the reuse function
The curve Y can be preliminarily estimatedtThe arc length value S, S also initially represents the curve distance of the movement required for the next step of the car.
Step nine, after calculating the curve distance value of the movement required by the next automobile, calculating the angle of the next automobile required to be steered
. Connecting the middle point m of the rear wheel2And the midpoint m of the front wheel1A median line M is generated, which according to common knowledge can be derived to be almost parallel to the edge of the body.
And step ten, reading the current time by using a sensor, and automatically driving the distance between the front left wheel and the front right wheel of the automobile and the front marking or the rear marking, and the distance between the rear left wheel and the rear right wheel of the automobile and the rear marking or the rear automobile.
Step eleven: the distance between the front left wheel and the front marking line or the vehicle is recorded as x1The distance between the front right wheel and the front marking line or the vehicle is recorded as x2The distance between the rear left wheel and the rear marking line or the vehicle is recorded as x3And the distance between the rear right wheel and the rear marking line or the vehicle is recorded as x4。
Step twelve: based on the distance value and the arc length value S read in the previous step, a one-dimensional cubic equation can be constructed to represent the average total length of the available parking space at the current time
Wherein
Respectively, are positive integer constants.
Step thirteen: calculating the average total length b of the current available parking spaces1Multiplication by time t yields the equation:
fourteen steps: substituting bXt derived from step thirteen into the formula of the Gompertz model of step three
In (1), the equation can be derived:
step fifteen: 1 to x1Are brought into separately
In (1) - (x)2Are brought into separately
In (1) - (x)3Are brought into separately
In (1) - (x)4Are brought into separately
In, each increment is 1; note the book
Max is max;
sixthly, the steps are as follows: repeating the thirteen and fourteen steps to calculate max different Y values, which are respectively marked as
；
Seventeen steps: calculate out
Is the average value of
Eighteen steps: the sum of the average values obtained from the previous step
Can calculate the angle of the parking steering of the automatic driving automobile
(ii) a Wherein the content of the first and second substances,
eighteen, computer transfer angle
The values and the distance value S of the curve track to the controller can control the movement required by the next step of the calculator.
And nineteen steps, repeating the fourth step to the eighteenth step until the automatic driving automobile stops accurately.
According to the scheme provided by the embodiment of the invention, the following beneficial effects are achieved:
1) the maximum steering angle which can be realized physically is considered, and the steering condition of the automatic driving automobile in actual life is greatly adapted.
2) When the candidate track is generated, collision prediction detection is performed under the track environment, and the parking safety of the automatic driving automobile is greatly guaranteed.
3) And (3) parameterizing the running track into an arc length format by utilizing a cubic interpolation method, and performing simulation calculation for multiple times according to data to select the optimal running track.
4) And excessive complex operation is not needed, so that the control time is further saved.
Although the present invention has been described in detail hereinabove, the present invention is not limited thereto, and various modifications can be made by those skilled in the art in light of the principle of the present invention. Thus, modifications made in accordance with the principles of the present invention should be understood to fall within the scope of the present invention.
Claims (9)
1. An automatic automobile accurate parking control method is characterized by comprising the following steps:
during the parking of the automatic driving automobile, the automatic driving automobile respectively acquires a distance value, an angular velocity value and an acceleration value of the current time;
inputting the distance value, the angular velocity value and the acceleration value into a trained Gongworth model by the automatic driving automobile to obtain a track curve of the current time, wherein the trained Gongworth model comprises the following components: the automatic driving automobile respectively obtains the driving speed of the automatic driving automobile, the driving distance of the middle point of the rear wheel and the total length value of the available parking space; the automatic driving automobile trains an initial Gangbort model by using the running speed of the automatic driving automobile, the running distance of the middle point of the rear wheel and the total length value of the available parking space to obtain a trained Gangbort model;
and the automatic driving automobile respectively calculates the distance value and the angle of the curve to be moved by utilizing the track curve of the current time so as to accurately stop according to the distance value and the angle of the curve.
2. The precision parking control method of an autonomous automobile according to claim 1, wherein the distance value includes any one or a combination of:
a first distance value of a front left wheel from a front obstacle or a front stop line;
a second distance value of the front right wheel from a front obstacle or a front stop line;
a third distance value of the rear left wheel from a rear obstacle or a rear stop line;
a fourth distance value of the rear right wheel from a rear obstacle or rear stop line.
3. The precision parking control method of an autonomous vehicle as claimed in claim 2, wherein the acquiring the distance value, the angular velocity value and the acceleration value of the current time by the autonomous vehicle comprises:
measuring a distance value of the current time through a camera of the automatic driving automobile and a laser distance scanner;
acquiring an angular speed value of the current time through a single-axis gyroscope sensor of the automatic driving automobile;
and measuring the acceleration value of the current time through the three-dimensional acceleration sensor of the automatic driving automobile.
4. The precision parking control method of an autonomous automobile according to claim 1, wherein the autonomous automobile uses the trajectory curve of the current time and the calculating of the curve distance value to be moved comprises:
the automatic driving automobile calculates an arc length value of the track curve according to the track curve of the current time;
and the automatic driving automobile calculates the distance of the curve to be moved according to the arc length value of the track curve.
5. The precision parking control method of an autonomous automobile according to claim 4, wherein the autonomous automobile respectively calculating the angles to be moved using the trajectory curve of the current time includes:
the automatic driving automobile constructs an average total length equation of available parking spaces of the automatic driving automobile at the current time according to the distance value of the current time and the arc length value of the track curve;
inputting an average total length equation and time of the available parking space into the initial Golay model by the automatic driving automobile to obtain a plurality of different Golay model values;
and the automatic driving automobile calculates the angle to be moved according to the different Golay model values.
6. The precision parking control method for the automatic driving automobile according to claim 5, wherein the precision parking of the automatic driving automobile according to the curve distance value and the angle comprises the following steps:
the automatic driving automobile carries out mobile parking processing according to the curve distance value and the angle to obtain a mobile parking result;
the automatic driving automobile judges whether the mobile parking result reaches accurate parking;
and if the mobile parking result does not reach the accurate parking result, recalculating a new curve distance value and a new angle by the automatic driving automobile until the accurate parking is achieved.
7. The utility model provides an automatic accurate parking control device of driving car which characterized in that includes:
the acquisition module is used for respectively acquiring a distance value, an angular velocity value and an acceleration value of the current time during the parking of the automatic driving automobile;
an input module, configured to input the distance value, the angular velocity value, and the acceleration value into a trained okouz model to obtain a trajectory curve of a current time, where the trained okouz model includes: the automatic driving automobile respectively obtains the driving speed of the automatic driving automobile, the driving distance of the middle point of the rear wheel and the total length value of the available parking space; the automatic driving automobile trains an initial Gangbort model by using the running speed of the automatic driving automobile, the running distance of the middle point of the rear wheel and the total length value of the available parking space to obtain a trained Gangbort model;
and the calculation and parking module is used for respectively calculating a curve distance value and an angle to be moved by utilizing the track curve of the current time so as to carry out accurate parking according to the curve distance value and the angle.
8. The precision parking control apparatus of an autonomous vehicle of claim 7, wherein the distance value comprises any one or a combination of:
a first distance value of a front left wheel from a front obstacle or a front stop line;
a second distance value of the front right wheel from a front obstacle or a front stop line;
a third distance value of the rear left wheel from a rear obstacle or a rear stop line;
a fourth distance value of the rear right wheel from a rear obstacle or rear stop line.
9. The autonomous driving precision parking control apparatus of claim 8, wherein the obtaining module comprises:
measuring a distance value of the current time through a camera and a laser distance scanner;
acquiring an angular speed value of current time through a single-axis gyroscope sensor;
and measuring the acceleration value of the current time through the three-dimensional acceleration sensor.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3124995A1 (en) * | 2015-07-31 | 2017-02-01 | Aisin Seiki Kabushiki Kaisha | Parking assistance device |
| CN106585627A (en) * | 2016-11-07 | 2017-04-26 | 纵目科技（上海）股份有限公司 | Parking auxiliary system and automobile |
| CN106874551A (en) * | 2017-01-11 | 2017-06-20 | 成都信息工程大学 | A kind of Parallel parking method for being based on three rank arctan function models |
| CN107735290A (en) * | 2015-06-19 | 2018-02-23 | 日产自动车株式会社 | Parking aid and parking assistance method |
| CN108725585A (en) * | 2017-04-14 | 2018-11-02 | 上海汽车集团股份有限公司 | The Trajectory Tracking Control method and device of vehicle autonomous parking |
| KR20190041253A (en) * | 2017-10-12 | 2019-04-22 | 엘지전자 주식회사 | Autonomous vehicle and method for controlling the same |
| CN111907516A (en) * | 2019-05-09 | 2020-11-10 | 广州汽车集团股份有限公司 | Full-automatic parking method and system |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE102006027114A1 (en) * | 2006-06-12 | 2007-12-13 | Robert Bosch Gmbh | Control device and method for driver assistance |
| US9151646B2 (en) * | 2011-12-21 | 2015-10-06 | Deka Products Limited Partnership | System, method, and apparatus for monitoring, regulating, or controlling fluid flow |
| KR101739175B1 (en) * | 2013-04-26 | 2017-05-23 | 요코가와 덴키 가부시키가이샤 | Control system and control method |
| CN107206908B (en) * | 2015-01-28 | 2018-09-04 | 日产自动车株式会社 | Parking aid |
| EP3611472B1 (en) * | 2016-06-27 | 2021-11-24 | Mobileye Vision Technologies Ltd. | Controlling host vehicle based on detected parked vehicle characteristics |
| KR101915166B1 (en) * | 2016-12-30 | 2018-11-06 | 현대자동차주식회사 | Automatically parking system and automatically parking method |
| US10703367B2 (en) * | 2017-05-31 | 2020-07-07 | Nio Usa, Inc. | Utilization of smoothing functions for acceleration and deceleration profile generation |
| US11694356B2 (en) * | 2019-11-15 | 2023-07-04 | Argo AI, LLC | Methods and systems for joint pose and shape estimation of objects from sensor data |
-
2021
- 2021-06-15 CN CN202110660540.2A patent/CN113246968B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107735290A (en) * | 2015-06-19 | 2018-02-23 | 日产自动车株式会社 | Parking aid and parking assistance method |
| EP3124995A1 (en) * | 2015-07-31 | 2017-02-01 | Aisin Seiki Kabushiki Kaisha | Parking assistance device |
| CN106585627A (en) * | 2016-11-07 | 2017-04-26 | 纵目科技（上海）股份有限公司 | Parking auxiliary system and automobile |
| CN106874551A (en) * | 2017-01-11 | 2017-06-20 | 成都信息工程大学 | A kind of Parallel parking method for being based on three rank arctan function models |
| CN108725585A (en) * | 2017-04-14 | 2018-11-02 | 上海汽车集团股份有限公司 | The Trajectory Tracking Control method and device of vehicle autonomous parking |
| KR20190041253A (en) * | 2017-10-12 | 2019-04-22 | 엘지전자 주식회사 | Autonomous vehicle and method for controlling the same |
| CN111907516A (en) * | 2019-05-09 | 2020-11-10 | 广州汽车集团股份有限公司 | Full-automatic parking method and system |
Non-Patent Citations (3)
| Title |
|---|
| Application of Sigmoidal Gompertz Curves in Reverse Parallel Parking for Autonomous Vehicles;Aneesh Chand等;《International Journal of Advanced Robotic Systems》;20150930;第12卷(第9期);第1350~1356页 * |
| 基于三阶反正切函数模型的平行泊车轨迹规划;彭莉斯等;《测控技术》;20180718(第07期);第152~156页 * |
| 基于伪谱法的自主泊车路径规划方法;叶林铨等;《计算机工程》;20170228(第02期);第39~43、48页 * |
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