WO2020103532A1

WO2020103532A1 - Multi-axis electric bus self-guiding method

Info

Publication number: WO2020103532A1
Application number: PCT/CN2019/104833
Authority: WO
Inventors: 王睿; 杨颖�; 杜求茂; 赵青选; 陈勇; 陈平安; 王虎高
Original assignee: 中车株洲电力机车有限公司
Priority date: 2018-11-20
Filing date: 2019-09-09
Publication date: 2020-05-28
Also published as: CN109492599A

Abstract

Disclosed is a multi-axis electric bus self-guiding method, comprising: step A, inputting original images captured by cameras at front and rear sides of a vehicle into a vehicle-mounted device and pre-processing the original images, so as to obtain pre-processed images; step B, with regard to the pre-processed images, performing local enhancement on a vehicle driving area; step C, extracting lane lines; step D, determining the position of the vehicle according to the positions of the cameras and the lane lines; and step E, according to a vehicle driving constraint condition, using a PID control method, a fuzzy PID control method, a prediction control method and a non-linear control method to control the vehicle to travel in an allowable track. The present invention can improve the accuracy and redundancy of a vehicle self-guiding system, and realizes a lane-keeping function and a multi-wheel synchronous steering function of a vehicle by means of comprehensively using optical and satellite inertial navigation systems; and the vehicle is also easily controlled by means of transmitting an electrical signal to an electrically-controlled steering device.

Description

Self-directing method of multi-axis electric passenger car

Technical field

The invention belongs to the field of urban transportation, and particularly relates to a multi-axis electric bus self-directing method.

Background technique

Because of the increase in population, the ageing of the population, and the severe traffic congestion, more and more people choose large-scale public transportation that does not require driving. Therefore, large-scale public transportation is becoming more and more a choice for people to travel. Considering the small passenger capacity of BRT, the control of heavy-duty buses is more difficult, and the cost of tramway laying is higher, so solving the problems facing these three vehicles will be the inevitable development direction of urban transportation in the future. Due to the small passenger capacity of BRT, the laying of tracks by trams is an attribute of the vehicle itself, which is more difficult to achieve. Therefore, solving the problem of difficult control of heavy-duty buses will become the most feasible solution for the next stage of the transportation industry.

At present, the multi-axis electric bus self-guiding system is divided into two types: contact type and non-contact type. Contact type guidance has curbs and guide rails. Non-contact type guidance has optical and electromagnetic methods.

The physical guide systems that use guide rails include the Raul system and the Bombardier rail system. The Rail system in the Tianjin Development Zone Rail Line 1 and Shanghai Zhangjiang Tram are both used. Optical guidance requires the use of image processing technology, using the camera in front of the vehicle to scan the ground guidance marking line, and transmit the collected image data to the on-board computer in real time. The on-board computer transmits these data to dynamic parameters such as vehicle speed, deviation, wheel angle, etc. And analysis and processing, and then send instructions to the steering system to control the vehicle's driving direction, so that the actual running trajectory of the vehicle and the ground guide marking line basically match.

However, contact self-guided systems are prone to wear, have poor stability, and require road redesigns, which is costly. Among the non-contact methods, the electromagnetic method has problems such as being susceptible to electromagnetic interference. At this stage, the optical method mainly uses the lane line recognition technology, that is, the special track line on the road surface is recognized by the on-board camera, and the virtual track is intelligently controlled , So as to transmit the running information to the train "brain" (central control unit), according to the "brain" instructions, while ensuring that the train achieves normal actions such as traction, braking, steering, etc., it can accurately control the train to travel on the established "virtual track ", To achieve intelligent operation. However, this method is subject to factors such as light intensity caused by weather changes, light occlusion problems, photochemical pollution and ground lane lines not clean enough. These factors also directly affect the optical method to identify lane lines, and at the same time, high accuracy Moreover, the high stability optical method requires the camera to have a high shooting frequency and high resolution. Therefore, it also requires the storage capacity of the controller to be large enough, the arithmetic processing capability of the main controller is strong enough, and the data transmission capability of the ground transmission device is stable enough. At this stage, it is difficult to be commercialized on a large scale and has higher requirements on the controller.

In addition, a single optical method will be difficult to return to the original lane to complete the lane line recognition function when dealing with sudden lane changes and other deviations from the intended lane. Therefore, the stability and robustness of this method are not high.

Summary of the invention

An object of the present invention is to provide a self-guided method for a multi-axis electric bus in view of the above-mentioned shortcomings in the prior art.

To solve the above technical problems, the technical solutions adopted by the present invention are:

A multi-axis electric bus self-guided method, which is characterized by the following steps:

Step A. Input the original images captured by the cameras on the front and rear sides of the vehicle to the on-board equipment and preprocess the original images to obtain the preprocessed images;

Step B. For the pre-processed image, the vehicle driving area is locally enhanced;

Step C. Extract the lane line;

Step D. Determine the position of the vehicle according to the position of the camera and the lane line;

Step E. According to the vehicle travel constraints, the PID control method, fuzzy PID control method, predictive control method, and nonlinear control method are used to control the vehicle to run within the allowed trajectory.

Further, in the step D, the method further includes using a satellite positioning system and / or an inertial navigation system installed in the intermediate vehicle to determine the position of the vehicle.

As a preferred manner, the pre-processing process in step A includes image compression, gray-scale conversion, filtering processing, and equalization processing in this order.

As a preferred way, in the step E, the control system of the through passage of the steering mechanism is designed according to the PID control method:

Where K _d is the differential factor of the PID control system, K _p is the proportional factor of the PID control system, and K _i is the integral factor of the PID control system.

Further, in the step E, it also includes constructing a generation value function according to driving rules, comfort requirements, line length, noise limitation and other conditions to find the optimal vehicle running trajectory.

Compared with the prior art, the present invention can improve the accuracy and redundancy of the vehicle's self-guided system, and comprehensively use the optical and satellite inertial navigation system to realize the vehicle's lane keeping function and multi-wheel synchronous steering function, using electrical signal transmission The electronically controlled steering device is also easy to control the vehicle.

BRIEF DESCRIPTION

Figure 1 is the control principle diagram of the present invention.

Figure 2 is the layout of the camera and satellite antenna.

Figure 3 is a schematic diagram of satellite system positioning.

4 is a schematic diagram of vehicle position recognition.

FIG. 5 is a schematic diagram of a lane keeping method for finely adjusting the turning angle of a straight line segment.

6 is a schematic diagram of a vehicle crossing a curve.

detailed description

As shown in FIGS. 1 and 2, the present invention is applied to a multi-axle heavy-duty passenger vehicle. Cameras are provided on the front and rear sides of the vehicle to capture the front and rear pictures of the vehicle and pass them to the central processor. , Image compression, grayscale conversion, image preprocessing, partial image enhancement, lane line detection and other methods directly extract the lane line, so as to determine the position of the vehicle on the current route according to the lane line.

1. Lane line recognition and satellite inertial navigation system

1. Lane line recognition

(1) Image input and compression

The image captured by the camera is input into the vehicle-mounted device and the image is compressed according to requirements, simplifying the calculation amount.

(2) Grayscale conversion

The RGB color model is converted into a binary model for analysis to further simplify the calculation.

(3) Filter processing and equalization processing

Filtering methods such as mean filtering or histogram equalization are used to preprocess the image to remove possible optical noise.

(4) Lane line recognition

An operator that sets the threshold of the lane line according to weather conditions, records the area that meets the threshold, and extracts the lane line.

After extracting the lane line, lock the lane line, considering that there may be some clutter signals or factors such as obstacles, weather conditions, other vehicles covering the lane line, so the clutter signal is removed by the filter or the lane fitting method will be The lane line is complete.

Areas of interest can be selected for road conditions, that is, areas where vehicles are driving for local enhancement. For straight lane lines, a 5 * 5 matrix can be used. Considering that lane lines have a special shape, this method can be used for comparison. The characteristics are shown in the following table:

00	00	00	00	127127
00	00	00	127127	127127
00	00	127127	127127	127127
00	127127	127127	127127	127127
127127	127127	127127	127127	127127

Table 1 Lane line feature matrix

(5) Vehicle position recognition

Considering that the roads on which vehicles travel have lane lines, they are generally divided into special lane lines and ordinary lane lines. Relatively speaking, the special lane lines designed for heavy-duty electric buses are relatively easy to identify. Therefore, the general lane lines are mainly analyzed here.

Considering that the vehicle is driving in the middle of both sides of the lane line, the position of the vehicle can be uniquely determined according to the position of the camera and the lane line in the image, thereby completing the lane line recognition.

At the same time, a satellite positioning system and an inertial navigation system are installed in the middle of the vehicle at the same time, and the position of the vehicle is determined by the satellite system and the inertial navigation system to ensure that the vehicle is driving in the area where the lane line can be recognized. Considering that a satellite can receive satellite signals and base station signals through an antenna, a system composed of several satellites and base stations can be used to determine the time Δt of the GPS signal reaching the receiver based on the instantaneous position of the satellite as a known value, and then determine the distance according to the propagation speed Finally, the geometric relationship is constructed according to the triangle rule. The more satellites, the more accurate the position. The description image is shown in Figure 3.

The illustration of the vehicle position is shown in Figure 4. When the vehicle is in different positions, the position of the lane line is also different. Therefore, the position of the vehicle can be uniquely determined according to the position of the lane line recognized by the camera, so that in the main controller, according to Vehicle constraints and PID control, fuzzy PID control, predictive control, nonlinear control and other methods to ensure that the vehicle runs within the allowed trajectory.

Therefore, this method can infer the position of the vehicle by comparing the lane line information and provide a basis for the main controller to execute the control command.

2. Satellite system and inertial navigation system

The differential satellite signal system uses a differential satellite reference station with known accurate three-dimensional coordinates to obtain the pseudorange correction amount or position correction amount, and then sends this correction amount to the user in real time or afterwards to correct the user's measurement data to improve Satellite positioning accuracy. This system can completely capture the position information of the vehicle through the multi-satellite system installed in the head car and the middle car. The more satellites, the higher the accuracy. The current can reach the centimeter level, and the worst can reach the sub-meter level. Consider The optical video system must be recognized under the condition of the lane line, and the recognition situation is also unstable. Therefore, the use of a differential satellite signal system can ensure that the vehicle runs within the lane line. At the same time, under the condition of unstable optical system, it can also directly The positioning signal and map information given by the satellite directly provide the basis for the steering, while the optical system will only serve as a feedback system to give the system a feedback signal to improve the robustness and stability of the system.

An inertial navigation system (INS, hereinafter referred to as inertial navigation) is an autonomous navigation system that does not depend on external information or radiate energy to the outside. The basic working principle of inertial navigation is based on Newton's law of mechanics. By measuring the acceleration of the carrier in the inertial reference system, integrating it with time, and transforming it into the navigation coordinate system, you can get the Information such as speed, yaw angle and position, installed on the center axis of the vehicle, can make up for the problem of vehicle satellite system errors, so as to ensure the accuracy of vehicle positioning, and consider the interference with the platform when installing the camera in the middle car , Generally do not consider the use of cameras, so in the middle car often consider the use of inertial navigation system as an aid, in the event of a satellite system failure, according to the inertial navigation system to provide vehicle position information, so as to complete the steering function of the actuator.

As shown in Figures 2 and 3, base station satellite heavy-duty vehicles can be interconnected and interconnected, the transmission time is fixed, the position can be measured according to the speed, and then connected according to the triangle rule, so as to uniquely determine the specific position, considering the more satellites, The more complex its shape, the polygon method can be used to solve it, thereby improving accuracy.

Considering that the antenna position is known, the antenna position of each vehicle can be uniquely determined by the antenna position. If two antennas are used, when the satellite accuracy reaches the centimeter or even millimeter level, the plane attitude angle can also be determined according to the positions of the two antennas (Plane analysis with two points in line), if you use three antennas, you can determine the three-dimensional attitude angle (three-point plane analysis), the more antennas, the higher the recognition accuracy, and then according to the satellite system's own map system Accurately realize the positioning function. Therefore, the vehicle's position, attitude angle, etc. are determined according to the vehicle's position information and related systems.

After the antenna receives the information, the information of the received vehicle is fed back to the main controller through the interactive device, the terminal and the network cable. After the main controller comprehensively processes the information, the vehicle position is uniquely determined. Considering that there is a vehicle trajectory circuit diagram in the vehicle controller, it can be According to the line conditions and the lane lines identified by the integrated camera, under severe weather conditions, the positioning is mainly achieved through the satellite system. Under the tunnel and bridge and other places where the satellite signal is lost, the camera will be used to identify the positioning, and the inertial navigation is in the middle. The vehicle is positioned to provide a basis for controlling the vehicle's steering or fine-tuning the steering actuator to realize the lane keeping function.

2. Determine vehicle location information

(1) Through the camera installed on the vehicle, the information on the front of the vehicle can be captured synchronously, displayed on the image, and the relevant image data is transferred to the processor, which is converted into an image with multiple pixels to obtain image information , Combined with positioning algorithms to identify lane lines and vehicle positions, considering that multi-axis heavy-duty electric passenger cars need to control multiple steering devices, there is also a need to run in two directions. Therefore, it is necessary to set up two cameras in front and behind to jointly identify the lane line, so as to completely determine the relative position of the vehicle and the lane line, and determine the vehicle position according to the lane line.

(2) The vehicle is connected through the through channel, and the head and tail vehicles are determined by the camera. The remaining steering mechanisms complete the wheel following function according to PID control, fuzzy PID control, predictive control, nonlinear control and other methods. The satellite system and inertial navigation system installed on the vehicle are integrated to achieve positioning within the sub-meter level of the vehicle, thereby keeping the vehicle within the lane line. The specific installation position is shown in Figure 2. The satellite and inertial navigation system are integrated Identify the position of the vehicle in the satellite plane through the positioning device, and ensure that the vehicle is within the lane line, and the camera captures the lane line information and gives a feedback signal to the position determined by the satellite system, so as to more accurately determine the vehicle's steering and fine-tuning. At the same time, when the satellite system data is not accurate enough (such as in a tunnel, high-rise effect, there are obstacles blocking the sky, etc.), the optical recognition will be used as the basis to adjust Kp, Ki, Kd in PID in the algorithm, and Strengthen the proportion of non-linear algorithms, while using the inertial navigation system as a supplement, so that the positioning system can maintain the positioning at the submeter level or the centimeter level as much as possible, and when the satellite positioning is lost for too long or the satellite positioning system fails, the optical is directly switched The system and the inertial navigation system replace the function of the satellite system within a certain length through the inertial navigation system. The optical system continues to recognize the lane line to maintain the precise positioning of the vehicle. The weather and other bad weather cover the lane line, then the positioning function is directly realized through the satellite system and the inertial navigation system, and assisted by the inertial navigation to obtain the complete vehicle position. Therefore, this method can obtain the vehicle position in all weather and under any conditions To provide a basis for steering by wire. The scenario analysis is as follows:

The schematic diagram of the lane keeping method for fine-tuning the turning angle of the straight line segment is shown in FIG. 5, and the schematic diagram of the vehicle crossing curve method is shown in FIG. 6.

3. Vehicle control and execution

(1) As shown in Figure 6, under the premise of knowing the position of the vehicle, there is a trajectory constraint line of the vehicle inside the controller, and each wheel cannot exceed the wheel constraint line, so it is converted into a constrained model, and then According to the PID control method, the control system of the through channel of the steering mechanism is comprehensively designed:

After the main controller completes the data analysis, the stability and controllability of the system are guaranteed according to the transfer function matrix. Considering that the PID system can only control linear systems and relatively simple nonlinear systems, it is also necessary to design predictive control models and gain scheduling Non-linear control, so as to more accurately determine the trajectory of the vehicle within the constraint line. After calculating the running track, the main controller processes the running track as an electrical signal, and feeds back the calculated electrical signal to the actuator to complete the rotation requirements within the constraint line, so that the steering mechanism turns according to the predetermined track and performs according to the lane line. Check and give feedback signals to ensure the accuracy and stability of the steering function completion; the actuator controls the wheel pairs separately according to the electrical signals, so as to realize the simultaneous steering of multi-axis wheels, and complete the lane keeping and steering functions.

Considering that there may be multi-valued results under constraints, and considering vehicle dynamics models and possible errors, it is necessary to generate multiple splines or through simulation methods, that is, in the main controller according to driving rules, comfort requirements , Needs to evaluate the length of each line, etc., and construct a trajectory algebraic value function Y _cost = f (R, S, L, W, O) (R driving rules, S comfort requirements, L line length, W noise, O Other conditions) and Y _opt = Max (Y _cost1 , Y _cost2 , Y _cost3 …) to determine the final driving trajectory to find the best vehicle trajectory, find the running trajectory of the vehicle through the control algorithm, and build its transfer function matrix, and Construct a vehicle dynamics model, construct a multi-dimensional control matrix for the steering mechanism and the through channel to ensure the robustness and stability of the vehicle, and at the same time, complete the correspondence between the vehicle trajectory and the position of the wheels under the constraint conditions to analyze whether the vehicle needs Fine-tune each wheelset, or execute the steering instructions of the wheelset, and use this as a basis to generate an electrical signal and pass it to the actuator.

(2) The actuator adopts an electronic control system, which directly transmits the electric signal to the electronically controlled steering mechanism. Considering that each wheel set can be controlled, the position of the wheel at the next moment can be known according to the running track of the vehicle. At the same time, due to the existence of the flexibility of the through channel, under the condition of slight errors and other errors due to dynamic conditions, it can still ensure that each wheel pair of the vehicle reaches the specified position according to the trajectory generated by the main controller at the next moment, so that Control multiple steering mechanisms, the steering mechanism adjusts according to the received electrical signals to complete the requirements of steering and lane keeping. Simplify or even eliminate the man-made steering operation, reduce the interference caused by human factors, thereby improving the efficiency of handling vehicle-related matters.

(3) The vehicle obtains the vehicle's position through the navigation system and the video system, and provides a basis for the vehicle's driving according to the position. Because of the existence of the satellite system and the inertial navigation system, the vehicle can directly detect the vehicle's displacement and speed through the system. Attitude angle, etc., avoid the design of the wheel mechanism to monitor the wheel steering system. Directly passing the vehicle's body position provides a basis for the steering system's steering. At the same time, compared with the current vehicle first-wheel steering and rear-wheel following scheme, this scheme can provide the vehicle position more quickly and directly control each wheel, improving the redundancy of the system. Since this method can synchronize vehicle position data in real time, errors can be avoided. At the same time, it is also possible to fine-tune the rotation angle of the wheelset to avoid the occurrence of tail flicking.

(4) In response to the all-weather operation requirements, the satellite system is mainly used to provide positioning and complete the path planning function in the main controller, and the camera is mainly used to achieve close-range lane line recognition and environment detection. The integrated use of the positioning device of the satellite and the inertial navigation system can identify the position of the vehicle in the satellite plane and can ensure that the vehicle is within the lane line. To determine the vehicle's steering and fine-tuning. At the same time, when the satellite system data is not accurate enough (such as in a tunnel, high-rise effect, there are obstacles blocking the sky, etc.), the optical recognition will be mainly used as the basis to adjust the K _p , K _i , K in the PID of the algorithm _d , and strengthen the proportion of the nonlinear algorithm, while using the inertial navigation system as a supplement, so that the positioning system can maintain the positioning accuracy at the centimeter level as much as possible, and when the satellite positioning is lost for too long or the satellite positioning system fails, the optical is directly switched The system and the inertial navigation system replace the function of the satellite system within a certain length through the inertial navigation system. The optical system continues to recognize the lane line to maintain the precise positioning of the vehicle. Weather and other bad weather cover the lane line, then the positioning function is directly realized through the satellite system and the inertial navigation system, and assisted by the inertial navigation to obtain the complete vehicle position. Under day and night conditions, K _p , K _i , K _d can be adjusted , And adjust the algorithm of the non-linear system according to the climatic conditions, thus ensuring the requirement that both day and night conditions can be used. At the same time, this method can return to the original lane through the satellite system and continue to complete the operation after encountering the problem of sudden lane changes such as obstacles on the road, which has high redundancy, stability and robustness. Therefore, this method is less affected by the weather and the day and night environment, and can obtain the position of the vehicle in all weather and most extreme conditions, thereby providing a basis for steering by wire.

The invention is suitable for multi-axis heavy-duty electric passenger cars, which can improve the accuracy and redundancy of the vehicle's self-guided system, and comprehensively uses optical and satellite inertial navigation systems to realize the vehicle's lane keeping function and multi-wheel synchronous steering function. The signal transmission to the electronically controlled steering device is also easy to control the vehicle; it provides a human-machine interaction interface to facilitate the driver to understand the vehicle information, and can also improve the driver's maneuverability during the assisted driving phase.

The embodiments of the present invention have been described above with reference to the drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are only schematic, not limiting, and those of ordinary skill in the art Under the enlightenment of the present invention, many forms can be made without departing from the scope of the present invention and the scope of the claims, which all fall within the protection scope of the present invention.

Claims

A multi-axis electric bus self-guided method, characterized in that it includes the following steps:

Step A. Input the original images captured by the cameras on the front and rear sides of the vehicle to the on-board equipment and preprocess the original images to obtain the preprocessed images;

Step B. For the pre-processed image, the vehicle driving area is locally enhanced;

Step C. Extract the lane line;

Step D. Determine the position of the vehicle according to the position of the camera and the lane line;

Step E. According to the vehicle travel constraints, the PID control method, fuzzy PID control method, predictive control method, and nonlinear control method are used to control the vehicle to run within the allowed trajectory.
The self-guiding method for a multi-axis electric passenger car according to claim 1, wherein

In the step D, the method further includes using a satellite positioning system and / or an inertial navigation system installed in the intermediate vehicle to determine the position of the vehicle.
The multi-axis electric bus self-directing method according to claim 1, wherein the pre-processing process in step A includes image compression, gray-scale conversion, filter processing, and equalization processing in this order.
The self-guiding method for a multi-axis electric passenger car according to claim 1, characterized in that in step E, a control system for the through passage of the steering mechanism is designed according to the PID control method:
Where K d is the differential factor of the PID control system, K p is the proportional factor of the PID control system, and K i is the integral factor of the PID control system.
The multi-axis electric bus self-guiding method according to claim 1, wherein step E further includes constructing a generation value function according to driving rules, comfort requirements, line length, and noise limits to find the optimal vehicle operation Track.