KR101525897B1 - auto-docking control method of bus, and thereof control system - Google Patents
auto-docking control method of bus, and thereof control system Download PDFInfo
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- KR101525897B1 KR101525897B1 KR1020140012488A KR20140012488A KR101525897B1 KR 101525897 B1 KR101525897 B1 KR 101525897B1 KR 1020140012488 A KR1020140012488 A KR 1020140012488A KR 20140012488 A KR20140012488 A KR 20140012488A KR 101525897 B1 KR101525897 B1 KR 101525897B1
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- 238000003032 molecular docking Methods 0.000 title claims abstract description 64
- 238000000034 method Methods 0.000 title claims abstract description 48
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- 238000013528 artificial neural network Methods 0.000 claims abstract description 9
- 238000004891 communication Methods 0.000 claims description 8
- 238000004904 shortening Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 19
- 238000004364 calculation method Methods 0.000 description 9
- 238000003062 neural network model Methods 0.000 description 8
- 230000002902 bimodal effect Effects 0.000 description 7
- 230000006698 induction Effects 0.000 description 7
- 230000006870 function Effects 0.000 description 6
- 230000001939 inductive effect Effects 0.000 description 3
- 230000008569 process Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 230000002708 enhancing effect Effects 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
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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
- B60W10/00—Conjoint control of vehicle sub-units of different type or different function
- B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
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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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
- B62D—MOTOR VEHICLES; TRAILERS
- B62D6/00—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
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- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Steering Control In Accordance With Driving Conditions (AREA)
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Abstract
The present invention relates to a method of controlling an auto docking of a bus and a control system using the method. More particularly, the present invention relates to an auto docking system comprising a camera, an image processing device, an electronic controller, an auto gearbox and a steering angle and torque sensor The method comprising the steps of: (a) inputting an auto-docking command by a switch when entering a predetermined distance from a stop; (b) calculating the stop data by identifying the position of the bus, the stop stop boundary line, and the stop entry direction of the image taken by the camera, by the image processing apparatus; (c) outputting a target steering angle by the electronic controller which has learned the calculated stop data by a neural network; (d) driving an actuator of the auto gear box through the output target steering angle; And (e) repeating the steps (b) and (f) until the bus enters a stop position.
The present invention provides an automobile docking control method and system in which a bus automatically closes a stop (within 50 mm) to a bus stop to increase the spread of low-floor buses and smooth boarding of passengers aboard the bus, , Shortening the boarding time, and preventing the motorcycle from colliding with the passengers when getting on and off.
Description
More particularly, the present invention relates to a method and apparatus for controlling an auto-docking of a bus for precise and close proximity auto-docking of an inductive bus stop such as a bimodal tram And a control system using the method.
The Bi-modality Tram is a kind of induction bus that is a CNG hybrid bend-reflex bus that is self-guided by self-induction. It can run on public roads like a bus and can be operated automatically in a special orbit like a subway. It is all public transportation that can run. The length is 18m, the number of wheels is 6, and all three axes can be controlled.
When the bimodal tram comes to a stop and stops, the distance between the bimodal tram and the station is far away, which makes it inconvenient for passengers to board. In order to facilitate wheelchair access, the station and bimodal tram should be as close as possible. However, it is not easy for a person to drive a bimodal tram to the station.
Accordingly, a technique for inducing and controlling steering to a conventional target track has been disclosed. The automatic guiding apparatus disclosed in Japanese Patent Application Laid-Open No. Hei 02-308313 guides the user to move the vehicle to a target position set by the user on the setting screen. Such automatic guidance estimates the current position of the vehicle, (Orbit) from the target position to the target position in real time, and controlling the steering angle based on the generated target track.
Japanese Patent Laid-Open Publication No. 10-264839 discloses an automatic inductive device that estimates a current position of a vehicle based on respective wheel speeds detected by wheel speed sensors and a yaw rate detected by a yaw rate sensor. And determines whether or not the estimated current position of the vehicle is different from the target track. If the estimated current position is different from the target track, the steering angle of the wheel is controlled in accordance with the amount of deviation to correct deviation from the target track.
In order to automatically guide the vehicle to the target position, it is required to monitor in real time the relationship between the target track and the current position of the vehicle during the movement of the vehicle. This means that estimating the current vehicle position plays an important role.
In general, the estimation of the current vehicle position is based on the controlled steering angle and the amount of movement (moving distance) of the vehicle detected by the yaw rate sensor and the wheel speed sensor, for example, as in the above-mentioned prior art. However, even when the steering angle is precisely controlled according to the target steering angle, there is a difference between the estimated vehicle position and the actual vehicle position. This difference can not be detected by the system because the steering angle is precisely controlled according to the target steering angle.
As a result of this, the difference can not be corrected and therefore the vehicle is led to the wrong place. Therefore, it is required to improve the accuracy of automatic guidance control. Also, the induction bus, such as the Bi-modality Tram, must be able to guide the stop to the stop as close to the stop as the large vehicle, so the precision of the steering control for auto docking with the stop dock There is a problem that it needs to be further increased.
SUMMARY OF THE INVENTION It is an object of the present invention to provide a control method and a control system for accurate and close auto docking of a stop of an induction bus such as a Bi-modality Tram.
According to a first aspect of the present invention, there is provided an automatic docking control method for a bus, which comprises a camera installed on a side surface of a bus, an image processing device, an electronic controller, an auto gearbox, The method comprising the steps of: (a) inputting an auto-docking command by a switch when entering a predetermined distance from a stop; (b) calculating the stop data by identifying the position of the bus, the stop stop boundary line, and the stop entry direction of the image taken by the camera, by the image processing apparatus; (c) outputting a target steering angle by the electronic controller which has learned the calculated stop data by a neural network; (d) driving an actuator of the auto gear box through the output target steering angle; And (e) repeating the steps (b) and (b) until the bus enters a stop position.
The step (b) includes calculating the position of the vehicle by GPS mounted on the bus, calculating the stop data for identifying the stop boundary line and the entry direction of the stop using the stop recognition algorithm and the automatic parking algorithm And the output of the steering angle is preferably a step of calculating the steering angle using a bicycle model as a vehicle dynamics model of the bus.
Preferably, the steering angle may be calculated using a bus departure distance indicating a distance between the bus side and the stop boundary line and a bus departure angle indicating a difference between the bus travel direction and the stop boundary direction , And the cycle of repeating the steps (b) to (d) may be 50 ms to 100 ms. If the stop is detected, an alarm alarm indicating whether or not the auto-docking is performed may be displayed.
According to a second aspect of the present invention, there is provided an automatic docking control system for a bus, comprising: a camera provided on a side or front of a bus for photographing a front of the bus using the above-described control method; An image processing device for calculating the stop data through the image photographed by the camera; An electronic controller for calculating a target steering angle through the stop data; A sensor for measuring a current steering angle and a torque of the bus; And an auto gear box for controlling the steering drive through a target steering angle calculated from the electronic controller and a current steering angle measured from the sensor.
It is preferable to further include a GPS device for measuring the position of the bus on the bus. Preferably, the communication between the camera, the image processing device, the sensor and the electronic controller uses CAN communication.
It is an object of the present invention to provide a control method and a control system for precise and close auto docking of a bus stop of an induction bus such as a Bi-modality Tram, Docking control method and system that the bus automatically stops close to stop (less than 50mm) to get to and from the bus stop to reduce the stopping work of the driver, improve the safety, shorten the boarding time, to provide.
FIG. 1 is a flowchart illustrating a method of controlling an auto-docking of a bus according to an embodiment of the present invention,
2 is a schematic diagram showing a configuration of an auto docking system of a bus according to an embodiment of the present invention,
3 is a diagram illustrating a flow of steering control in a method of controlling an auto-docking of a bus according to an embodiment of the present invention,
4 is a diagram illustrating a flow of state control in a method of controlling an auto-docking of a bus according to an embodiment of the present invention,
FIG. 5 is a schematic diagram showing a lane recognition algorithm used in a method of controlling an auto-docking of a bus according to an embodiment of the present invention,
FIG. 6 is a schematic diagram illustrating an automatic aftermath algorithm used in a method of controlling an auto-docking of a bus according to an embodiment of the present invention,
FIG. 7 is a diagram illustrating the modeling of the Ekman steer angle in the bicycle model applied in the embodiment of the present invention,
8 is a schematic diagram for calculating a distance difference in a steering control model using a neural network model in a method of controlling an autodocking of a bus according to an embodiment of the present invention,
9 is a schematic diagram for predicting the departure distance,
10 is a schematic diagram for calculating the departure angle,
11 is a schematic diagram illustrating a steering angle calculation model using a neural network model applied to a method of controlling an auto-docking of a bus according to an embodiment of the present invention.
BRIEF DESCRIPTION OF THE DRAWINGS The advantages and features of the present invention, and how to accomplish it, will be described with reference to the embodiments described in detail below with reference to the accompanying drawings. However, the present invention is not limited to the embodiments described herein but may be embodied in other forms. The embodiments are provided so that those skilled in the art can easily carry out the technical idea of the present invention to those skilled in the art.
In the drawings, embodiments of the present invention are not limited to the specific forms shown and are exaggerated for clarity. Also, the same reference numerals denote the same components throughout the specification.
The expression "and / or" is used herein to mean including at least one of the elements listed before and after. Also, singular forms include plural forms unless the context clearly dictates otherwise. Also, components, steps, operations and elements referred to in the specification as " comprises "or" comprising " refer to the presence or addition of one or more other components, steps, operations, elements, and / or devices.
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.
FIG. 1 is a flow chart of a method of controlling an auto docking of a bus according to an embodiment of the present invention, and FIG. 2 is a schematic diagram showing a configuration of a bus auto docking system according to an embodiment of the present invention. As shown in Figs. 1 and 2, a method of controlling an auto-docking of a bus according to an embodiment of the present invention includes a camera, an image processing device, an electronic controller, an auto gearbox, An automatic docking control method using a configured auto docking system, the method comprising: (a) inputting an auto docking command by a switch when entering a predetermined distance from a stop; (b) calculating the stop data by identifying the position of the bus, the stop stop boundary line, and the stop entry direction of the image taken by the camera, by the image processing apparatus; (c) outputting a target steering angle by the electronic controller which has learned the calculated stop data by a neural network; (d) driving an actuator of the auto gear box through the output target steering angle; And (e) repeating the steps (b) and (f) until the bus enters a stop position.
As described above, the present invention provides a control method and a control system for precise and close auto docking of a bus stop of an induction bus such as a bi-modality tram, (Less than 50mm) automatically to the bus stop for stopping the bus stop, reducing the stopping work of the driver, improving the safety, shortening the boarding time, and preventing the motorcycle from colliding with the passenger. I want to.
The auto docking control system for a bus according to the embodiment of the present invention includes a camera installed on a side or front of a bus for photographing the front of a bus as shown in Fig. An image processing device for calculating the stop data through the image photographed by the camera; An electronic controller for calculating a target steering angle through the stop data; A sensor for measuring a current steering angle and a torque of the bus; And an automatic gearbox for controlling the steering drive through a target steering angle calculated from the electronic controller and a current steering angle measured from the sensor.
Since it is not easy for a person to operate an induction bus such as a bimodal tram and make it close to a station, communication between the camera and the CAN So that the distance between the bus and the station is located between 5 cm and 10 cm. In the embodiment of the present invention, the uniaxial steering of the bimodal tram was controlled using the NI CAN, and a red solid line was displayed on the stationary curb at the stop boundary line in order to recognize the stationary position with the image.
The CAN (Controller Area Network) is a standard communication standard designed for communication between microcontrollers and devices without a host computer in the vehicle. CAN communication is a message-based protocol and recently it is often used in industrial automation equipment and medical equipment as well as in vehicles have.
The camera captures an image of the front of the vehicle, and performs a function of an image sensor for generating stop data for close docking with a stop. In the embodiment of the present invention, the image sensor of the image is a Flea 3 USB 3.0 version of Point Gray, and the mounted lens is M0814-MP of FA (FACTORY AUTOMATION LENS). The camera sensor is installed at a height of 1.2m, about 2/3 of the first car, at an angle of about 30 degrees.
The GPS device is a satellite navigation device that measures distance and relative position with respect to a stop within a few meters of the vehicle and informs the driver whether or not to perform auto docking when the driver approaches the stop within a distance of several ten meters And performs a function of performing auto docking according to a selection of a given driver.
The image processing apparatus is an image data processor, and performs a function of calculating stop data by identifying a stop boundary line and a stop entry direction using the image data photographed by the camera.
The steering angle and the torque sensor are sensors for measuring the steering angle of the bus or measuring the torque and forming stop data important for steering control for auto docking and using the image data from the camera and the data measured from the steering angle and torque sensor An electronic control unit (ECU) uses a neural network circuit learning model to calculate the target steering angle.
When the target steering angle is calculated, the auto gearbox controls the steering as an actuator of the automatic steering apparatus to perform auto docking, and a dock of the stopping point, which is an induction bus target point such as a bi- modality tram, (About 5cm ~ 10cm) to the station.
Hereinafter, the flow of a method of controlling an auto-docking of a bus according to an embodiment of the present invention will be described step by step.
First, as a step (a), when the bus enters the vicinity of the stop and enters the auto-docking period, the driver forms a process as shown in FIGS. 3 and 4 in which the driver can select the auto-docking.
3 is a diagram illustrating a flow of steering control in a method of controlling an auto-docking of a bus according to an embodiment of the present invention. That is, in the auto docking control method according to an embodiment of the present invention, a driver can perform auto docking through automatic or autonomous operation through an input command of a switch button, and can perform an operation for docking manually.
As shown in FIG. 3, the flow of the steering control for selecting the driver is performed by performing an initialization operation for steering control and determining whether the steering control is autonomous control ). When the auto-docking is selected, the target steering angle is calculated and steering-driven as described above. When the autonomous control is not selected, the driver manually controls the steering for the auto-docking.
4 is a diagram illustrating a flow of state control in a method of controlling an auto-docking of a bus according to an embodiment of the present invention. The automatic docking control method according to an embodiment of the present invention preferably includes an auto-docking selection button for stopping a stop on a part of a front surface of a driver's seat of a bus, wherein the selection button indicates whether the bus has entered the auto docking area And when the auto docking is ended or released, it is clearly displayed through an alarm or the like, thereby enhancing the usability of the driver and preparing for accidental accidents due to a system malfunction.
In the control flow, when the bus senses a stop through the GPS device and enters the vicinity of the stop, the driver is alarmed or alarmed that the stop section or the auto-docking period is present. When the driver turns on the auto docking switch, The docking state is accurately displayed and the steering is autonomously controlled to perform auto docking.
Then, when the bus enters the stop position and the auto-docking is completed, the vehicle is stopped (vehicle speed = 0), and when the bus leaves the stop position again, the auto-docking switch is released and the steering can be manually controlled . If the speed of the vehicle is not zero, it is a matter of course that the steering is autonomously controlled in an auto-docking state continuously.
In step (b), the image processing apparatus calculates the stop data by identifying the position of the bus, the stop stop boundary line, and the stop entry direction of the image photographed by the camera. In the embodiment of the present invention, It is preferable to use the lane recognition algorithm as an algorithm for recognizing the lane recognition algorithm.
5 is a schematic diagram showing a lane recognition algorithm used in a method of controlling an auto-docking of a bus according to an embodiment of the present invention. As shown in FIG. 5, the lane recognition algorithm first converts an RGB image into a gray image, detects a Sobel edge, and then performs a Hough transformation. Then, a virtual top view is generated by inverse perspective mapping, and stop data such as a straight line and a distance are calculated.
FIG. 6 is a schematic diagram illustrating an automatic aftermath algorithm used in a method of controlling an auto-docking of a bus according to an embodiment of the present invention. As shown in FIG. 6, in the process of the automatic parking algorithm applied to the embodiment of the present invention, the left and right vertical lines of the stop zone are found in the camera image of the stop area, and then the upper and lower horizontal lines and the center points are calculated . Then, the center points of the left and right vertical lines of the stop zone are calculated, and the stop data for the stop of the specified stop zone is calculated.
(c), the target steering angle is output by the electronic controller learned in the neural network model using the stop data calculated in the step (b). Here, the neural network circuit refers to a model in which the electronic controller calculates the target steering angle using the respective data. The neural network is a technique to model intelligence by modeling the neural patterns of the human brain. The neural network consists of several computational elements that interact in parallel. Each computational element has a weighted sum Perform only the same simple calculation.
The computational ability of a neural network is obtained by performing a parallel computation on a large number of computational elements, learning a weight through learning data, and performing a learning function by generalizing the characteristics of the data.
Using the neural network model, the electronic controller learns the target steering angle calculation model and finally calculates the target steering angle using the stop data calculated in the step (b).
It is preferable that the embodiment of the present invention uses a bicycle model as a vehicle dynamics model for calculating a target steering angle. FIG. 7 is a diagram illustrating modeling of the Ekkman steer angle in a bicycle model applied in an embodiment of the present invention.
In the embodiment of the present invention, the steering angle used for the lateral control of the autonomous vehicle is calculated using the bicycle model, and the moving radius of the vehicle is calculated as the steering angle using the Ackerman Angle equation used in the model do.
As shown in Fig. 7, it can be seen that the steering angle of the vehicle is closely related to the steering radius of the vehicle, and a method of obtaining the steering radius ahead of the calculation of the steering angle is proposed. Therefore, in the embodiment of the present invention, the calculation is performed using the trigonometric function to obtain the radius for traveling the vehicle to the target point by using the past vehicle position to know the target point, the current vehicle position, and the vehicle direction.
FIG. 8 is a schematic diagram for calculating a distance difference in a steering control model using a neural network model in a method of controlling an autodocking of a bus according to an embodiment of the present invention, FIG. 9 is a schematic diagram for predicting a departure distance, Fig. 9, the distance from the head of the vehicle to the lane (stop boundary line) is predicted using the distance gap. Then, as shown in Fig. 9, the distance value in front of the
Then, as shown in FIG. 10, the angle between the bus and the lane is calculated using the trigonometric function to calculate the final bus departure angle h err .
11 is a schematic diagram illustrating a steering angle calculation model using a neural network model applied to a method of controlling an auto-docking of a bus according to an embodiment of the present invention.
As shown in Fig. 11, when the bus departure distance d err and the bus departure angle h err are calculated, the target steering angle calculation model as the neural network model in the embodiment of the present invention is expressed by Equation (2). Here, D LA Represents the bus front distance.
here,
The target steering angle calculated in step (c) is used to control the steering by driving the actuator of the automatic gearbox in step (d). Such calculation is repeated at intervals of 50 ms to 100 ms (step (e)), and the target steering angle of the bus is calculated, and the steering is finally controlled to stop (5 cm to 10 cm) closest to the stop boundary line.
While the invention has been shown and described with respect to the specific embodiments thereof, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined by the appended claims. Anyone with it will know easily.
Claims (9)
(a) inputting an auto-docking command by a switch when entering a predetermined distance from a stop;
(b) The image processing apparatus calculates the position of the vehicle by the GPS mounted on the bus through the image photographed by the camera, and calculates a stop stop boundary line and a stop entry direction using the stop recognition algorithm and the automatic parking algorithm Calculating the stop data to be identified;
(c) outputting a target steering angle by the electronic controller which has learned the calculated stop data by a neural network;
(d) driving an actuator of the auto gear box through the output target steering angle; And
(e) repeating the steps (b) and (b) until the bus enters a stop position.
The output of the steering angle,
And calculating the steering angle using a bicycle model as a vehicle dynamics model of the bus.
The steering angle,
A bus departure distance indicating a distance between the bus side and the stop boundary line,
And calculating a bus leaving angle indicating a difference between the bus traveling direction and the stopping boundary line direction.
In the step (e)
Wherein the cycle of repeating the steps (b) to (d) is 50 ms to 100 ms.
The step (a)
And displaying an alarm alarm indicating whether or not the bus stop is detected, when the bus stop is detected.
A camera installed on the side or front of the bus for photographing the front of the bus;
An image processing device for calculating the stop data through the image photographed by the camera;
An electronic controller for calculating a target steering angle through the stop data;
A sensor for measuring a current steering angle and a torque of the bus; And
And an auto gearbox for controlling steering operation through a target steering angle calculated from the electronic controller and a current steering angle measured from the sensor.
Further comprising a GPS device for measuring a position of the bus on the bus.
Wherein the communication between the camera, the image processing apparatus, the sensor, and the electronic controller uses CAN communication.
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Cited By (8)
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| CN105761538A (en) * | 2016-05-06 | 2016-07-13 | 厦门蓝斯通信股份有限公司 | Assistant station reporting method and system based on video recognition and vehicle-mounted terminal |
| JP2017196962A (en) * | 2016-04-26 | 2017-11-02 | 日野自動車株式会社 | bus |
| CN110481635A (en) * | 2019-08-13 | 2019-11-22 | 中山大学 | Personalize steering system and its method based on convolutional neural networks and conventional geometric controller |
| CN110968086A (en) * | 2018-09-30 | 2020-04-07 | 郑州宇通客车股份有限公司 | Vehicle arrival control method and system |
| CN112083728A (en) * | 2020-09-09 | 2020-12-15 | 上海擎朗智能科技有限公司 | Parking method, device, equipment and storage medium for driving equipment |
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