S curve tracking method and device for intelligent driving bus

Abstract

The embodiment of the invention discloses an S curve tracking method and device for an intelligent driving bus, and the method comprises the steps: determining linear fitting segmentation points according to the slope change rate of all local path reference points calculated in real time, and segmenting all local paths according to the segmentation points; by taking the current position of the vehicle as a starting point and the segmentation point closest to the vehicle as a terminal point, performing linear fitting on local path reference points between the two points to obtain a trajectory tracking curve; and according to the trajectory tracking curve, adjusting a steering wheel turning angle to perform trajectory tracking, and when the distance between the vehicle and the segment point is smaller than a preset distance, repeating linear fitting on each local path reference point in the next segment until trajectory tracking of all local path reference points is completed. In this way, the slope inflection point of the front S curve road can be automatically recognized, and the tracking precision of the intelligent driving bus on the S curve is improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of vehicle control technology, and specifically to an S-curve tracking method and device for an intelligent driving bus. Background Art

[0002] Trajectory tracking is an important part of intelligent driving control. When the decision-making control software collects the environment around the vehicle and combines it with the destination information given by the navigation system to plan a path in real time, it is usually given as discrete trajectory points in front of the vehicle. The vehicle needs to track the planned trajectory points to ensure that the intelligent driving vehicle does not collide during driving and can safely reach the designated point.

[0003] At present, the mainstream trajectory tracking method for intelligent driving is to fit the discrete trajectory into a quintic equation, simulate the driver's driving method, and calculate the steering wheel angle required for the current tracking trajectory by previewing. The linear fitting method is suitable for most straight-line driving and high-rate curve driving, but for the fitting of S-curves, linear fitting is obviously not very suitable, and it is easy to overfit or underfit. In a typical case, a section of S-curve is fitted by using a quintic equation to approximate a straight line, resulting in the vehicle not being able to drive according to the predetermined trajectory. Summary of the invention

[0004] In view of the above problems, an embodiment of the present invention provides an S-curve tracking method and device for an intelligent driving bus, which overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of an embodiment of the present invention, an S-curve tracking method for an intelligent driving bus is provided, the method comprising: determining segmentation points for linear fitting according to the slope change rate of all local path reference points calculated in real time, and segmenting all local paths according to the segmentation points; taking the current position of the vehicle as the starting point and the segmentation point closest to the vehicle as the end point, linearly fitting the local path reference points between the two to obtain a trajectory tracking curve; adjusting the steering wheel angle according to the trajectory tracking curve to perform trajectory tracking, and repeating the linear fitting of each local path reference point in the next segment when the distance between the vehicle and the segmentation point is less than a preset distance, until the trajectory tracking of all local path reference points is completed.

[0006] In an optional manner, determining the segmentation point of the linear fit based on the slope change rate of all local path reference points calculated in real time includes: calculating the slope and slope change rate of all local path reference points in real time; if the slope change rate undergoes a positive or negative transition, calculating the curvature of the local path reference point corresponding to the slope change rate; if the curvature is less than a preset curvature, determining that the local path reference point is a segmentation point of the linear fit.

[0007] In an optional manner, the method takes the current position of the vehicle as the starting point and the segmentation point closest to the vehicle as the end point, linearly fits the local path reference points between the two to obtain a trajectory tracking curve, including: taking the current position of the vehicle as the starting point and the segmentation point closest to the vehicle as the end point, linearly fitting the local path reference points between the two into a cubic curve or a quartic curve to obtain a trajectory tracking curve.

[0008] In an optional manner, adjusting the steering wheel angle according to the trajectory tracking curve includes: calculating the required steering wheel angle as a feedforward quantity based on the trajectory tracking curve and using the Ackerman steering principle; predicting a predicted trajectory of the vehicle within a preset time based on the feedforward quantity; and adjusting the steering wheel angle according to a deviation between a tracking trajectory determined by the trajectory tracking curve and the predicted trajectory.

[0009] In an optional manner, predicting the predicted trajectory of the vehicle within a preset time based on the feedforward amount includes: taking the feedforward amount as input, using a two-degree-of-freedom single-vehicle model to predict the trajectory of the public transport vehicle within a preset time in the future, and obtaining a predicted trajectory.

[0010] In an optional manner, adjusting the steering wheel angle according to the deviation between the tracking trajectory determined by the trajectory tracking curve and the predicted trajectory includes: calculating the deviation between the tracking trajectory determined by the trajectory tracking curve and the predicted trajectory; and compensating the steering wheel angle by proportional adjustment according to the deviation to control vehicle driving.

[0011] In an optional manner, when the distance between the vehicle and the segmentation point is less than a preset distance, the linear fitting of each local path reference point in the next segment is repeated, including: when the distance between the vehicle and the segmentation point is less than a preset distance, determining the current position of the vehicle and the next segmentation point, and then determining the next segment; repeating the linear fitting of each local path reference point in the next segment.

[0012] According to another aspect of an embodiment of the present invention, an S-curve tracking device for an intelligent driving bus is provided, the device comprising: a path segmentation unit, used to determine the segmentation points for linear fitting according to the slope change rate of all local path reference points calculated in real time, and segment all local paths according to the segmentation points; a linear fitting unit, used to take the current position of the vehicle as the starting point and the segmentation point closest to the vehicle as the end point, perform linear fitting on the local path reference points between the two, and obtain a trajectory tracking curve; an angle adjustment unit, used to adjust the steering wheel angle according to the trajectory tracking curve to perform trajectory tracking, and repeat the linear fitting on each local path reference point in the next segment when the distance between the vehicle and the segmentation point is less than a preset distance, until the trajectory tracking of all local path reference points is completed.

[0013] According to another aspect of an embodiment of the present invention, there is provided a computing device, comprising: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, wherein the executable instruction enables the processor to execute the steps of the above-mentioned S-curve tracking method for the intelligent driving bus.

[0014] According to another aspect of an embodiment of the present invention, a computer storage medium is provided, wherein at least one executable instruction is stored in the storage medium, and the executable instruction enables the processor to execute the steps of the above-mentioned S-curve tracking method of the intelligent driving bus.

[0015] The embodiment of the present invention determines the segmentation points of linear fitting according to the slope change rate of all local path reference points calculated in real time, and segments all local paths according to the segmentation points; takes the current position of the vehicle as the starting point and the segmentation point closest to the vehicle as the end point, linearly fits the local path reference points between the two, and obtains a trajectory tracking curve; adjusts the steering wheel angle according to the trajectory tracking curve to perform trajectory tracking, and repeats the linear fitting of each local path reference point in the next segment when the distance between the vehicle and the segmentation point is less than a preset distance, until the trajectory tracking of all local path reference points is completed, and the slope inflection point of the S-curve road ahead can be autonomously identified, thereby improving the tracking accuracy of the smart driving bus on the S curve.

[0016] The above description is only an overview of the technical solution of the embodiment of the present invention. In order to more clearly understand the technical means of the embodiment of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the embodiment of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0018] Figure 1 A schematic diagram showing a flow chart of an S-curve tracking method for an intelligent driving bus provided in an embodiment of the present invention;

[0019] Figure 2 A schematic diagram of the control architecture of an intelligent driving bus provided by an embodiment of the present invention is shown;

[0020] Figure 3A schematic diagram showing the structure of an S-curve tracking device for an intelligent driving bus provided in an embodiment of the present invention is shown;

[0021] Figure 4 A schematic diagram of the structure of a computing device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0022] The exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to enable the scope of the present invention to be fully communicated to those skilled in the art.

[0023] Figure 1 The flow chart of the S-curve tracking method of the intelligent driving bus provided by the embodiment of the present invention is shown. The S-curve tracking method of the intelligent driving bus is applied to a decision controller arranged on the vehicle, such as Figure 1 As shown, the S-curve tracking method of the intelligent driving bus includes:

[0024] Step S11: determining the segmentation points of the linear fitting according to the slope change rates of all local path reference points calculated in real time, and segmenting all local paths according to the segmentation points.

[0025] In the embodiment of the present invention, the control architecture of the intelligent driving bus is as follows: Figure 2 As shown in the figure, after the global map software is started, it will first read the map file and obtain positioning information from the inertial navigation device to determine the current position of the smart driving bus on the map. Then, when the driver sets the specific location of the starting point, the decision control software generates a global path point for driving. If there are no obstacles, the smart driving bus can completely track the global path point to reach the end point. The decision control software generates a local path reference point allowed for driving in the current environment based on the actual road conditions sent by the perception fusion and the global path point. The local path reference point is tracked by the vehicle control model, and then the steering wheel angle of the smart driving vehicle is adjusted, and transmitted to the vehicle controller through the gateway to control the driving of the smart driving vehicle.

[0026] The smart driving bus is equipped with a wire-controlled chassis, which can control the vehicle to move forward, backward, turn, accelerate and decelerate by issuing instructions such as acceleration, steering wheel angle, and steering wheel angle speed. The decision control software package contains a path planning module, a positioning and navigation module, a perception module, and a trajectory tracking module. The positioning and navigation module is used to calculate the position of the vehicle in the map in real time, and calculate the global optimal path from the starting point to the destination through the navigation algorithm to ensure that the vehicle reaches the destination in the shortest time. The global optimal path is not a single path, but a road. The road may contain multiple lanes. The global optimal path calculated by the path planning module only ensures that the vehicle travels on this road and avoids obstacles by changing lanes or parking, so as not to be blocked by static obstacles and unable to automatically reach the destination. The path planning module is based on the global optimal path calculated by the positioning and navigation module, and uses the road environment information around the vehicle body sent by the perception module to calculate a path in real time to enable the smart driving vehicle to always travel on the global optimal path. This path is the path to be tracked. The path to be tracked given by the path planning module is the trajectory that the vehicle needs to travel, which is generally about 100 meters long and is also given in discrete points. The trajectory tracking module ensures that the vehicle always travels on this planned trajectory by controlling the vehicle's speed, steering wheel angle and corner speed.

[0027] After the path planning module obtains the path to be tracked based on the global optimal path, the local path reference points on the path to be tracked are tracked. In step S11, the slope and slope change rate of all local path reference points are calculated in real time; if the slope change rate undergoes a positive or negative transition, the curvature of the local path reference point corresponding to the slope change rate is calculated; if the curvature is less than the preset curvature, the local path reference point is determined to be a segmentation point of the linear fit. The preset curvature can be set as needed, preferably 0.02. All local path reference points are segmented according to all segmentation points determined on the path to be tracked.

[0028] Step S12: Taking the current position of the vehicle as the starting point and the segment point closest to the vehicle as the end point, linear fitting is performed on the local path reference points between the two to obtain a trajectory tracking curve.

[0029] In an embodiment of the present invention, the current position of the vehicle is taken as the starting point, the segmentation point closest to the vehicle is taken as the end point, and the local path reference points between the two are linearly fitted into a cubic curve or a quartic curve to obtain a trajectory tracking curve.

[0030] Step S13: adjusting the steering wheel angle according to the trajectory tracking curve to perform trajectory tracking, and repeating the linear fitting of each local path reference point in the next segment when the distance between the vehicle and the segment point is less than a preset distance, until the trajectory tracking of all local path reference points is completed.

[0031] After obtaining the trajectory tracking curve, firstly, the required steering wheel angle is calculated as the feedforward value based on the trajectory tracking curve using the Ackerman steering principle. That is, the required steering wheel angle of the vehicle is calculated based on the trajectory tracking algorithm of Pure Pursuit. Specifically, a geodetic coordinate system is established, the (x, y) coordinate point represents the position of the coordinate plane, the x and y axis units are m, and the vehicle is simplified to a two-degree-of-freedom model. The center point of the rear wheel of the vehicle is taken as (x0, y0), where (x, y) is the preview point on the desired path, and the sine theorem is applied to obtain:

[0032] d+(x-x0)=R,d 2 +(y-y0) 2 =R 2 ,

[0033]

[0034] The calculation can be obtained:

[0035]

[0036] Among them, L is the front and rear wheelbase of the intelligent driving bus, R is the wheel radius, δ1 is the front wheel turning angle, and L a is the distance between the center of the vehicle's rear wheel and the preview point. It can be seen that the effect of the trajectory tracking algorithm based on Pure Pursuit depends largely on the selection of the preview point.

[0037] Then, the predicted trajectory of the vehicle within a preset time is predicted based on the feedforward amount. Specifically, the calculated feedforward amount of the vehicle's target steering wheel angle is used as input, and a two-degree-of-freedom single-vehicle model is used to predict the trajectory of the bus within a preset time in the future to obtain a predicted trajectory. The preset time can be set as needed, and is preferably 1s in the embodiment of the present invention. That is, based on the feedforward amount, a two-degree-of-freedom single-vehicle model is used to predict the trajectory of the bus within the next 1 second, and the predicted trajectory is obtained. The embodiment of the present invention can greatly improve the curve tracking accuracy by adopting a two-degree-of-freedom single-vehicle model prediction method, and has high universality.

[0038] Finally, the steering wheel angle is adjusted according to the deviation between the tracking trajectory determined by the trajectory tracking curve and the predicted trajectory. Optionally, the deviation between the tracking trajectory determined by the trajectory tracking curve and the predicted trajectory is calculated; the steering wheel angle is compensated by proportional (PI) adjustment according to the deviation to control the vehicle driving and reduce the trajectory deviation. The decision controller transmits the compensated steering wheel angle to the vehicle controller through the gateway, and the vehicle controller controls the vehicle driving according to the compensated steering wheel angle.

[0039] When the distance between the vehicle and the segmentation point is less than the preset distance, the current position of the vehicle and the next segmentation point are determined, and then the next segmentation is determined; the linear fitting of each local path reference point in the next segment is repeated. When the vehicle controller controls the vehicle to travel to a distance less than the preset distance from the segmentation point as the end point of the current segmentation, the current position of the vehicle and the next segmentation point are determined, and the next segmentation point is the segmentation point closest to the current position of the vehicle. Then repeat steps S12 and S13, that is, take the current position of the vehicle as the starting point and the next segmentation point as the end point, determine the next segment, and perform linear fitting according to each local path reference point in the next segment to obtain the corresponding trajectory tracking curve, and then adjust the steering wheel angle until the trajectory tracking of all local path reference points is completed. The preset distance can be set as needed, preferably 2m.

[0040] The S-curve tracking method of the intelligent driving bus of the embodiment of the present invention can autonomously calculate the inflection point of the curve slope through the curvature of each local path reference point, and set it as the segmentation point, and then perform linear fitting in segments, and can autonomously identify the inflection point of the slope of the S-curve road ahead, and can easily solve the problem that the vehicle is easy to press the line or exceed the road boundary on the S-net curve path caused by the linear fitting S curve, and the structural changes to the original intelligent driving are small, and the tracking accuracy of the intelligent driving bus in the S curve can be improved, and the overfitting or underfitting degree can be minimized; by calculating the steering wheel angle required for tracking the current curve, the two-degree-of-freedom single-vehicle model is used to predict the coincidence of the driving trajectory of the intelligent driving bus in the next 1 second and the current reference point, and the deviation between the two is calculated, and the target steering wheel angle is compensated by PI adjustment, thereby improving the tracking accuracy of the intelligent driving trajectory. Since the segmented curve may be relatively short, if the target steering wheel angle is calculated by the preview method, the distance of the segmentation point selected from the fitted curve may be greater than the actual reference point length, resulting in a certain deviation in the calculated steering wheel angle, so it is necessary to use a compensation method to offset this deviation.

[0041] The S-curve tracking method of the intelligent driving bus in the embodiment of the present invention determines the segmentation points of linear fitting according to the slope change rate of all local path reference points calculated in real time, and segments all local paths according to the segmentation points; takes the current position of the vehicle as the starting point and the segmentation point closest to the vehicle as the end point, linearly fits the local path reference points between the two, and obtains a trajectory tracking curve; adjusts the steering wheel angle according to the trajectory tracking curve to perform trajectory tracking, and repeats the linear fitting of each local path reference point in the next segment when the distance between the vehicle and the segmentation point is less than a preset distance, until the trajectory tracking of all local path reference points is completed, and the slope inflection point of the S-curve road ahead can be autonomously identified, thereby improving the tracking accuracy of the intelligent driving bus on the S curve.

[0042] Figure 3The schematic diagram of the structure of the S-curve tracking device of the intelligent driving bus according to the embodiment of the present invention is shown. Figure 3 As shown, the S-curve tracking device of the intelligent driving bus is set in the decision controller of the vehicle, and its function is equivalent to the previous trajectory tracking module, including: a path segmentation unit, a linear fitting unit and a turning angle adjustment unit.

[0043] A path segmentation unit, used to determine the segmentation points of the linear fit according to the slope change rates of all local path reference points calculated in real time, and segment all local paths according to the segmentation points;

[0044] A linear fitting unit is used to take the current position of the vehicle as the starting point and the segment point closest to the vehicle as the end point, perform linear fitting on the local path reference points between the two, and obtain a trajectory tracking curve;

[0045] The angle adjustment unit is used to adjust the steering wheel angle according to the trajectory tracking curve to perform trajectory tracking, and when the distance between the vehicle and the segment point is less than a preset distance, repeatedly linearly fit each local path reference point in the next segment until the trajectory tracking of all local path reference points is completed.

[0046] For the convenience of description, the above device is described in terms of functions and is divided into various units and described separately. Of course, when implementing the embodiment of the present invention, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0047] The device of the above embodiment is applied to the corresponding method in the above embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0048] Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method described in any one of the above embodiments is implemented.

[0049] An embodiment of the present invention provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the method described in any of the above embodiments.

[0050] Figure 4 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 401, a memory 402, an input / output interface 403, a communication interface 404, and a bus 405. The processor 401, the memory 402, the input / output interface 403, and the communication interface 404 are connected to each other in communication within the device through the bus 405.

[0051] The processor 401 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solution provided by the method embodiment of the present invention.

[0052] The memory 402 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 402 can store an operating system and other application programs. When the technical solution provided by the method embodiment of the present invention is implemented by software or firmware, the relevant program code is stored in the memory 402 and called and executed by the processor 401.

[0053] The input / output interface 403 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0054] The communication interface 404 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

[0055] The bus 405 comprises a pathway for transmitting information between the various components of the device (eg, the processor 401 , the memory 402 , the input / output interface 403 , and the communication interface 404 ).

[0056] It should be noted that, although the above device only shows the processor 401, the memory 402, the input / output interface 403, the communication interface 404 and the bus 405, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiment of the present invention, and does not necessarily include all the components shown in the figure.

[0057] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure is limited to these examples. Based on the concept of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present invention as described above, which are not provided in detail for the sake of simplicity.

[0058] This application is intended to cover all such substitutions, modifications and variations that fall within the broad scope of all embodiments. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present invention should be included in the scope of protection of this disclosure.

Claims

1. An S-curve tracking method for an intelligent driving bus, characterized in that: The S-curve tracking method of the intelligent driving bus includes: Determine the segmentation points of the linear fitting according to the slope change rates of all local path reference points calculated in real time, and segment all local path reference points according to the segmentation points; Taking the current position of the vehicle as the starting point and the segment point closest to the vehicle as the end point, the local path reference points between the two are linearly fitted to obtain the trajectory tracking curve; The steering wheel angle is adjusted according to the trajectory tracking curve to perform trajectory tracking, and when the distance between the vehicle and the segmentation point is less than a preset distance, linear fitting is repeated for each local path reference point in the next segment until the trajectory tracking of all local path reference points is completed.

2. The method according to claim 1, characterized in that The step of determining the segmentation points of the linear fitting according to the slope change rates of all local path reference points calculated in real time includes: Calculate the slope and slope change rate of all local path reference points in real time; If the slope change rate changes from positive to negative, calculating the curvature of the local path reference point corresponding to the slope change rate; If the curvature is less than a preset curvature, the local path reference point is determined to be a segmentation point of the linear fit.

3. The method according to claim 1, characterized in that The method takes the current position of the vehicle as the starting point and the segment point closest to the vehicle as the end point, linearly fits the local path reference points between the two, and obtains the trajectory tracking curve, including: Taking the current position of the vehicle as the starting point and the segment point closest to the vehicle as the end point, the local path reference points between the two are linearly fitted into a cubic curve or a quartic curve to obtain the trajectory tracking curve.

4. The method according to claim 1, characterized in that: The adjusting the steering wheel angle according to the trajectory tracking curve comprises: Based on the trajectory tracking curve, the required steering angle of the steering wheel is calculated using the Ackerman steering principle as a feedforward amount; Predicting a predicted trajectory of the vehicle within a preset time based on the feedforward amount; The steering wheel angle is adjusted according to a deviation between a tracking trajectory determined by the trajectory tracking curve and the predicted trajectory.

5. The method according to claim 4, characterized in that The predicting of the predicted trajectory of the vehicle within a preset time based on the feedforward amount includes: The feedforward amount is used as input, and a two-degree-of-freedom single-vehicle model is used to predict the trajectory of the public transport vehicle within a preset time in the future to obtain a predicted trajectory.

6. The method according to claim 4, characterized in that The step of adjusting the steering wheel angle according to the deviation between the tracking trajectory determined by the trajectory tracking curve and the predicted trajectory comprises: Calculating a deviation between a tracking trajectory determined by the trajectory tracking curve and the predicted trajectory; The steering wheel angle is compensated by proportional adjustment according to the deviation to control the vehicle driving.

7. The method according to claim 1, characterized in that The method of repeatedly performing linear fitting on each local path reference point in the next segment when the distance between the vehicle and the segment point is less than a preset distance comprises: When the distance between the vehicle and the segmentation point is less than the preset distance, the current position of the vehicle and the next segmentation point are determined, and then the next segment is determined; Repeat the linear fitting for each local path reference point in the next segment.

8. An S-curve tracking device for an intelligent driving bus, characterized in that: The device comprises: A path segmentation unit, used to determine the segmentation points of the linear fit according to the slope change rates of all local path reference points calculated in real time, and segment all local paths according to the segmentation points; A linear fitting unit is used to take the current position of the vehicle as the starting point and the segment point closest to the vehicle as the end point, perform linear fitting on the local path reference points between the two, and obtain a trajectory tracking curve; The angle adjustment unit is used to adjust the steering wheel angle according to the trajectory tracking curve to perform trajectory tracking, and when the distance between the vehicle and the segment point is less than a preset distance, repeatedly linearly fit each local path reference point in the next segment until the trajectory tracking of all local path reference points is completed.

9. A computing device, characterized in that include: A processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other via the communication bus; The memory is used to store at least one executable instruction, and the executable instruction enables the processor to execute the steps of the S-curve tracking method of the intelligent driving bus according to any one of claims 1-7.

10. A computer storage medium, characterized in that: The storage medium stores at least one executable instruction, and the executable instruction enables the processor to execute the steps of the S-curve tracking method for the intelligent driving bus according to any one of claims 1-7.

Images