NZ752017B2 - Method and system utilized by multi-axle articulated vehicle tracking central lane line - Google Patents

Abstract

method and system utilized by a multi-axle articulated vehicle tracking a central lane line. The method comprises: S1. acquiring a visual navigation deviation and a navigation estimate of a vehicle; and S2. computing, according to the visual navigation deviation and the navigation estimate of the vehicle, and using an incremental PID algorithm, an expected front wheel turning angle of the vehicle, and controlling a front wheel of the vehicle to change a direction. The system comprises: a lane line identification module, a data acquisition unit, a determination control unit, and an execution unit. The system has advantages of ensuring a vehicle to effectively track, in an automatic driving process, a central lane line, implementing automatic tracking and automatic control of the vehicle with respect to the central lane line, enhancing safe driving, and reducing labor intensity of a driver. vehicle, and using an incremental PID algorithm, an expected front wheel turning angle of the vehicle, and controlling a front wheel of the vehicle to change a direction. The system comprises: a lane line identification module, a data acquisition unit, a determination control unit, and an execution unit. The system has advantages of ensuring a vehicle to effectively track, in an automatic driving process, a central lane line, implementing automatic tracking and automatic control of the vehicle with respect to the central lane line, enhancing safe driving, and reducing labor intensity of a driver.

Description

METHOD AND SYSTEM UTILIZED BY MULTI-AXLE ARTICULATED VEHICLE TRACKING CENTRAL LANE LINE FIELD

[0001] The present disclosure relates to the technical field of multi-axle steering vehicle control, and in particular to a steering control method for a multi-axle steering vehicle tracking a central lane line, and a steering control system for a multi-axle steering vehicle tracking a central lane line.

BACKGROUND As a new-type urban public vehicle, the multi-axle steering rubber-tyred train has the following features: being railless, sharing the road with conventional vehicles, and travelling without tracking a fixed rail. The multi-axle steering rubber-tyred train not only has the advantages of running flexibly and having low capital and maintenance cost like the bus, but also has the large transportation capacity. Further, the multi-axle steering rubber-tyred train does not have disadvantages of having high cost in construction of infrastructures and vehicle purchase as that in the subway, the light railway and the tramcar, which requires a specific power system and a specific rail.

The multi-axle steering rubber-tyred train flexibly travels along the ground mark lines under support of rubber wheels like the cars, rather than tracking a steel rail. Since the train does not track the fixed rail, the capital cost is decreased, and the multi-axle steering rubber-tyred train has larger operation advantages than the tramcar. The steering of the tramcar is not required to be controlled by a driver, while the steering of the multi-axle steering rubber-tyred train is required to be controlled by the driver by continuously adjusting the steering wheel to track the ground mark line (such as a central lane line, a white double dashed line) in a real time manner. In this case, driver fatigue is inevitable due to the long term driving. Therefore, it is desired to provide a device for automatically recognizing the central lane line to achieve the lane keeping of the multi-axle steering vehicle, so as to reduce the driver fatigue.

SUMMARY In view of the technical problems existing in the conventional technology, a steering control method for a multi-axle steering vehicle tracking a central lane line and a steering control system for a multi-axle steering vehicle tracking a central lane line are provided in the present disclosure, which can ensure the vehicle to effectively track the central lane line during an automatic driving process, so that the automatic tracking and keeping of the vehicle with respect to the central lane line can be achieved, thereby improving the driving safety and reducing the workload of a driver.

In order to solve the above technical problems, a steering control method for a multi-axle steering vehicle tracking a central lane line is provided according to technical solutions of the present disclosure. The method includes: S1, acquiring a visual course deviation and a predicted course of the vehicle; and S2, calculating, based on the visual course deviation and the predicted course, a desired front wheel steering angle of the vehicle by using an incremental PID algorithm to control front wheel steering of the vehicle, wherein the visual course deviation indicates an angle between the central line of the vehicle and a line connecting a preview point and a center point of the vehicle, and the predicted course indicates a predicated change amount of a course of the vehicle in a preset time period.

As a further improvement for the preset disclosure, the visual course deviation is acquired by a lane line visual recognition system of the vehicle.

As a further improvement for the preset disclosure, the predicted course is determined by calculating a ratio of a longitudinal displacement of the vehicle to a turning radius of the vehicle in a preset time period.

As a further improvement for the preset disclosure, a front wheel steering angle of the vehicle and a distance between a front axle and a rear axle of the vehicle are acquired, and the turning radius of the vehicle is calculated from a trigonometric relationship between the turning radius of the vehicle, the distance between the front axle and the rear axle of the vehicle, and the front wheel steering angle of the vehicle.

As a further improvement for the preset disclosure, the incremental PID algorithm in step S2 is expressed as the following formula (1): u = u(k) −u(k −1) = K [e(k) −e(k −1)] + K e(k) + K [e(k) −2e(k −1) + e(k −2)] p i d in the formula (1), u represents an increment of the front wheel steering angle of the uk () vehicle, represents a front wheel steering angle at a k-th sampling time instant, where ek () k=0, 1, 2, …, represents an equivalent course deviation at the k-the sampling time instant, ek () =  −  ,   represents the visual course deviation, and   represents the predicted course.

As a general inventive concept, a steering control system for a multi-axle steering vehicle tracking a central lane line is further provided in the present disclosure. The steering control system includes a lane line visual recognition module, a data acquisition unit, a decision control unit and an execution unit. The lane line visual recognition module is configured to recognize a lane line and provide information on a visual course deviation of the vehicle to the decision control unit. The data acquisition unit is configured to: acquire speed information of the vehicle and front wheel steering angle information of the vehicle, and provide the speed information of the vehicle and the front wheel steering angle information of the vehicle to the decision control unit. The decision control unit is configured to: calculate a predicted course of the vehicle based on the speed information of the vehicle and the front wheel steering angle information of the vehicle provided by the data acquisition unit; calculate a desired front wheel steering angle of the vehicle based on the predicted course and the visual course deviation; and provide the desired front wheel steering angle to the execution unit. The execution unit is configured to control steering of the vehicle based on the desired front wheel steering angle. The visual course deviation indicates an angle between the central line of the vehicle and a line connecting a preview point and a center point of the vehicle, and the predicted course indicates a predicated change amount of a course of the vehicle in a preset time period.

[0011] As a further improvement for the preset disclosure, the lane line visual recognition module includes a camera and an image processing system. The camera is configured to acquire an image of the lane line. The image processing system is configured to perform an analysis process on the image of the lane line to acquire the visual course deviation.

As a further improvement for the preset disclosure, the data acquisition unit includes a front wheel steering angle sensor and a vehicle speed sensor. The front wheel steering angle sensor is configured to acquire the front wheel steering angle information of the vehicle. The vehicle speed sensor is configured to acquire the speed information of the vehicle.

[0013] As a further improvement for the preset disclosure, the decision control unit has a manual driving mode in which the decision control unit does not provide the desired front wheel steering angle to the execution unit.

As a further improvement for the preset disclosure, the data acquisition unit further includes a torque sensor. The torque sensor is configured to acquire information on an external force applied to a steering wheel of the vehicle, and transmit the information on the external force to the decision control unit. The decision control unit operates in the manual driving mode in response to the information on the external force.

Compared with the conventional technology, the present disclosure has the following advantages. With the technical solutions of the present disclosure, the vehicle can be ensured to effectively track the central lane line during an automatic driving process, so that automatic tracking and keeping of the vehicle with respect to the central lane line can be achieved, thereby improving the driving safety and reducing the workload of a driver.

BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 is a schematic flowchart of a steering control method according an embodiment of the present disclosure; Figure 2 is a schematic diagram showing an arrangement of a system for tracking a central lane line provided in the present disclosure; Figure 3 is a schematic diagram showing course prediction and preview provided in the present disclosure; Figure 4 is a schematic diagram showing a central lane line provided in the present disclosure; Figure 5 is a schematic diagram showing a two-degree-of-freedom course prediction model for a vehicle provided in the present disclosure; Figure 6 is a schematic flowchart of calculating a desired front wheel steering angle provided in the present disclosure; Figure 7 is a schematic diagram showing a first structure of a steering control system for tracking a central lane line provided in the present disclosure; and

[0023] Figure 8 is a schematic diagram showing a second structure of the steering control system for tracking a central lane line provided in the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS The present disclosure is further described below in combination with the drawings and some preferred embodiments, and the protection scope of the present disclosure is not limited hereto.

As shown in Figure 1, a steering control method for a multi-axle steering vehicle tracking a central lane line is provided according to an embodiment of the present embodiment. The steering control method includes the following steps S1 and S2. In S1, a visual course deviation and a predicted course of the vehicle are acquired. In S2, a desired front wheel steering angle of the vehicle is calculated based on the visual course deviation and the predicted course by using an incremental PID algorithm to control front wheel steering of the vehicle.

In this embodiment, based on vehicle road information provided by a lane line visual recognition module, information such as a lateral distance deviation yL between a preview point and a center of a lane line, a course angle Δψ, and a curvature of a road are acquired to determine the desired front wheel steering angle. The determined desired front wheel steering angle is transmitted to a steering execution unit, to control the vehicle to follow a desired path.

[0027] In this embodiment, the visual course deviation is acquired by a lane line visual recognition system of the vehicle. As shown in Figure 2, in the lane line visual recognition system, a camera module is installed on the head of the vehicle to acquire image information of the central lane line by using a monocular camera, and an image processing system performs an analysis process on the image information to obtain the information such as a lateral distance deviation between a center of the vehicle and the lane line, the course deviation, and the curvature of the road. Then a course angle deviation between the preview point and a central line of the vehicle is calculated. In this embodiment, by setting a position in front of the vehicle as the preview point, the automatical control for the lane line tracking is inherently consistent with the manual steering performed by a driver, so that the steering control for the vehicle performed based on the visual course deviation acquired by the lane line visual recognition system provided in the present disclosure just like being controlled by the driver. As shown in Figure 3, which shows course prediction and preview in the lane line visual recognition system, XOY represents a ground coordinate system, xoy represents a vehicle coordinate system, p represents a preview point, d represents a preview distance, y1 represents a predicted deviation, and f(d) represents a distance deviation at a preview point.

The central lane line is indicated by a mark painted on a ground of a central lane, and is represented as a double dashed line having a certain length and a certain width. As shown in Figure 4, l =50cm, l =50cm, l =10cm and l =25cm. The lane may be accurately determined 1 2 3 4 by processing the image of the lane acquired by the camera.

[0028] In the conventional course control, a controller acquires a course deviation based on a desired course and an actual course of the vehicle to calculate a control quantity, and an executing mechanism executes the control quantity after one sampling period elapses. In this case, the actual course of the vehicle is changed, that is, there is a delay of one sampling period when the control quantity is executed. However, in the course prediction method provided in the present disclosure, a change trend of the course of the vehicle is predicted in advance, and is added in the control deviation, so that an output of the controller is affected by the change trend of the course of the vehicle, and thus the central lane line is tracked better.

In this embodiment, the predicted course is determined by calculating a ratio of a longitudinal displacement of the vehicle to a turning radius of the vehicle in a preset time period. Reference is made to Figure 5, which shows a two-degree-of-freedom course prediction model (i.e., a half model) of the vehicle. In this embodiment, a sampling period T  () rad of the controller is taken as a preset time period, and a change quantity of the vehicle course in a control period may be expressed as the following formula (2): =  vT / R in the formula (2),   represents a predicted course, represents a longitudinal speed of the vehicle, T represents a sampling period of the controller, and R represents a turning radius of the vehicle.

In this embodiment, a front wheel steering angle of the vehicle and a distance between a front axle and a rear axle of the vehicle are acquired, and the turning radius of the vehicle is calculated from a trigonometric relationship between the turning radius of the vehicle, the distance between the front axle and the rear axle of the vehicle, and the front wheel steering angle of the vehicle. As shown in Figure 5, G represents a gravity center of the vehicle, R represents a turning radius of the vehicle, represents a distance between the gravity center of the vehicle and the front axle of the vehicle, b represents a distance between the gravity center of the vehicle and the rear axle of the vehicle, represents a front wheel steering angle of the vehicle, and ab + represents a distance between the front axle of the vehicle and the rear axle of the vehicle. Since a connection line between a center O of a turning circle of the vehicle and the front axle of the vehicle is perpendicular to a direction in which the front wheel of the vehicle travels, the following formula (3) is obtained from the trigonometric relationship in a right-angled triangle shown in Figure 6.

R=+ (a b) / sin(  ) The formula (3) is substituted into the formula (2) to obtain the predicted course expressed by the following formula (4):  = vT sin( ) / (a + b) The parameters in the formula (4) are identical to those in the formulas (2) and (3).

Reference is made to Figure 6. In this embodiment, the incremental PID algorithm in step S2 is expressed as the following formula (1): u = u(k) −u(k −1) = K [e(k) −e(k −1)] + K e(k) + K [e(k) −2e(k −1) + e(k −2)] p i d in the formula (1), u represents an increment of the front wheel steering angle of the vehicle, uk () represents a front wheel steering angle at a k-th sampling time instant, where k=0, 1, 2, …, ek () represents an equivalent course deviation at the k-the sampling time ek () =  −      instant, , represents the visual course deviation, and represents the predicted course.

As shown in Figure 7 and Figure 8, a steering control system for a multi-axle steering vehicle tracking a central lane line is provided according to an embodiment of the present disclosure. The steering control system includes a lane line visual recognition module, a data acquisition unit, a decision control unit and an execution unit. The lane line visual recognition module is configured to recognize a lane line and provide information on a visual course deviation of the vehicle to the decision control unit. The data acquisition unit is configured to: acquire speed information of the vehicle and front wheel steering angle information of the vehicle, and provide the speed information of the vehicle and the front wheel steering angle information of the vehicle to the decision control unit. The decision control unit is configured to: calculate a predicted course of the vehicle based on the speed information of the vehicle and the front wheel steering angle information of the vehicle provided by the data acquisition unit; calculate a desired front wheel steering angle of the vehicle based on the predicted course and the visual course deviation; and provide the desired front wheel steering angle to the execution unit. The execution unit is configured to control steering of the vehicle based on the desired front wheel steering angle.

In this embodiment, the lane line visual recognition module includes a camera and an image processing system. The camera is configured to acquire an image of the lane line.

The image processing system is configured to perform an analysis process on the image of the lane line to acquire the visual course deviation. The data acquisition unit includes a front wheel steering angle sensor and a vehicle speed sensor. The front wheel steering angle sensor is configured to acquire the front wheel steering angle information of the vehicle. The vehicle speed sensor is configured to acquire the speed information of the vehicle.

In this embodiment, the decision control unit has a manual driving mode in which the decision control unit does not provide the desired front wheel steering angle to the execution unit. The data acquisition unit further includes a torque sensor. The torque sensor is configured to acquire information on an external force applied to a steering wheel of the vehicle, and transmit the information on the external force to the decision control unit. The decision control unit operates in the manual driving mode in response to the information on the external force. In this embodiment, the decision control unit may acquire a signal for switching on the manual driving mode via a human machine interface connected with the decision control unit. The decision control unit includes a computer communication interface via which a computer is connected to the decision control unit. In this case, control programs are written via a CAN bus, and changes of the parameters are monitored.

The above description shows only some preferred embodiments of the present disclosure, and is not intended to limit the present disclosure in any form. Although the present disclosure has been disclosed as above in the preferred embodiments, the present disclosure is not limited thereto. Therefore, simple variants, equivalent changes and modifications made to the above embodiments from the technical essences of the present disclosure without departing from the technical solutions of the present disclosure should fall in the protection scope of the present disclosure.

Claims (10)

1. A steering control method for a multi-axle steering vehicle tracking a central lane line, the steering control method comprising: 5 S1, acquiring a visual course deviation and a predicted course of the vehicle; and S2, calculating, based on the visual course deviation and the predicted course, a desired front wheel steering angle of the vehicle by using an incremental PID algorithm to control front wheel steering of the vehicle, wherein the visual course deviation indicates an angle between the central line of the vehicle and 10 a line connecting a preview point and a center point of the vehicle, and the predicted course indicates a predicated change amount of a course of the vehicle in a preset time period.

2. The steering control method for a multi-axle steering vehicle tracking a central lane line according to claim 1, wherein the visual course deviation is acquired by a lane line visual recognition system of the vehicle. 15

3. The steering control method for a multi-axle steering vehicle tracking a central lane line according to claim 2, wherein the predicted course is determined by calculating a ratio of a longitudinal displacement of the vehicle to a turning radius of the vehicle in a preset time period.

4. The steering control method for a multi-axle steering vehicle tracking a central lane 20 line according to claim 3, wherein a front wheel steering angle of the vehicle and a distance between a front axle and a rear axle of the vehicle are acquired, and the turning radius of the vehicle is calculated from a trigonometric relationship between the turning radius of the vehicle, the distance between the front axle and the rear axle of the vehicle, and the front wheel steering angle of the vehicle. 25 5. The steering control method for a multi-axle steering vehicle tracking a central lane line according to any one of claims 1 to 4, wherein the incremental PID algorithm in step S2 is expressed as the following formula (1): u = u(k) −u(k −1) = K [e(k) −e(k −1)] + K e(k) + K [e(k) −2e(k −1) + e(k −2)] p i d in the formula (1), u represents an increment of the front wheel steering angle of the vehicle, uk () represents a front wheel steering angle at a k-th sampling time instant, wherein ek () k=0, 1, 2, …, represents an equivalent course deviation at the k-the sampling time ek () =  −      instant, , represents the visual course deviation, and represents the predicted course.

5

6. A steering control system for a multi-axle steering vehicle tracking a central lane line, the steering control system comprising: a lane line visual recognition module; a data acquisition unit; a decision control unit; and 10 an execution unit, wherein the lane line visual recognition module is configured to recognize a lane line and provide information on a visual course deviation of the vehicle to the decision control unit; the data acquisition unit is configured to: acquire speed information of the vehicle and front wheel steering angle information of the vehicle, and provide the speed information of 15 the vehicle and the front wheel steering angle information of the vehicle to the decision control unit; the decision control unit is configured to: calculate a predicted course of the vehicle based on the speed information of the vehicle and the front wheel steering angle information of the vehicle provided by the data acquisition unit; calculate a desired front wheel steering 20 angle of the vehicle based on the predicted course and the visual course deviation; and provide the desired front wheel steering angle to the execution unit; and the execution unit is configured to control steering of the vehicle based on the desired front wheel steering angle, wherein the visual course deviation indicates an angle between the central line of the vehicle and 25 a line connecting a preview point and a center point of the vehicle, and the predicted course indicates a predicated change amount of a course of the vehicle in a preset time period.

7. The steering control system for a multi-axle steering vehicle tracking a central lane line according to claim 6, wherein the lane line visual recognition module comprises: a camera configured to acquire an image of the lane line; and an image processing system configured to perform an analysis process on the image of 5 the lane line to acquire the visual course deviation.

8. The steering control system for a multi-axle steering vehicle tracking a central lane line according to claim 7, wherein the data acquisition unit comprises: a front wheel steering angle sensor configured to acquire the front wheel steering angle information of the vehicle; and 10 a vehicle speed sensor configured to acquire the speed information of the vehicle.

9. The steering control system for a multi-axle steering vehicle tracking a central lane line according to claim 8, wherein the decision control unit has a manual driving mode in which the decision control unit does not provide the desired front wheel steering angle to the execution unit. 15

10. The steering control system for a multi-axle steering vehicle tracking a central lane line according to claim 9, wherein the data acquisition unit further comprises: a torque sensor configured to: acquire information on an external force applied to a steering wheel of the vehicle, and transmit the information on the external force to the decision control unit, wherein 20 the decision control unit operates in the manual driving mode in response to the information on the external force. Acquire a visual course deviation and a predicted course of a vehicle Calculate, based on the visual course deviation and the predicted course, a desired front wheel steering angle of the vehicle by using an incremental PID algorithm to control front wheel steering of the vehicle