Method and system for generating lane changing trajectory in intelligent driving, electronic device, and medium

Abstract

A method and system for generating a lane changing trajectory in intelligent driving, an electronic device, and a medium. The method comprises: acquiring lane center line information, the lane center line information comprising coordinate information of each acquisition point of a lane center line and a correlation coefficient of a lane line function expression (S21); determining a fitting coefficient of the lane center line on the basis of the lane center line information (S22); with reference to the lane changing time, the road width, and the current vehicle speed, performing trajectory compensation processing on the fitting coefficient to determine a compensated fitting coefficient (S23); and determining a lane changing trajectory on the basis of the compensated fitting coefficient (S24). According to the method for generating the lane changing trajectory in intelligent driving, a method for generating a lane changing trajectory with low computing power is provided, and an outputted lane changing trajectory is stable and smooth without significant noise, thereby meeting the intelligent driving requirements.

Description

Method, system, electronic device and medium for generating lane change trajectory for intelligent driving Technical Field

[0001] The present application belongs to the technical field of intelligent driving and relates to a trajectory generation method, and in particular to a method, system, electronic device and medium for generating an intelligent driving lane change trajectory. Background Art

[0002] Currently, in the autonomous driving industry, various methods for generating lane change trajectories include point-scattering curve construction ("fitting a quintic polynomial"), Lattice, and widely scattering points followed by Bezier curve fitting. These methods also incorporate some upstream decision-making principles; the resulting fitted lane change trajectory curves are continuous and stable, facilitating stable output for back-end control.

[0003] However, the trajectory calculated by the above algorithm has high requirements for chip computing power, so there are certain challenges in resource allocation within an overall autonomous driving architecture. Because in addition to the trajectory planning module, the perception module and control module also require high computing power to maintain the real-time and continuity of the system operation.

[0004] Summary of the Invention

[0005] The present application provides a method, system, electronic device, and medium for generating lane change trajectories for intelligent driving, which are used to solve the problem of how to generate lane change trajectories that can be used for autonomous driving in a low-computing manner.

[0006] In a first aspect, the present application provides a method for generating an intelligent driving lane change trajectory, the method comprising: obtaining lane centerline information; the lane centerline information comprises coordinate information of each acquisition point of the lane centerline and a correlation coefficient of a lane line function expression; determining a fitting coefficient of the lane centerline based on the lane centerline information; performing trajectory compensation processing on the fitting coefficient in combination with the lane change time, road width and current speed of the vehicle to determine a compensated fitting coefficient; and determining a lane change trajectory based on the compensated fitting coefficient.

[0007] In an implementation of the first aspect, the step of obtaining lane centerline information includes at least one of the following steps: obtaining a cubic polynomial pre-fitted to the lane centerline based on coordinate information; obtaining coordinate information of dispersed collection points on the lane centerline, and fitting a cubic polynomial based on the coordinate information of the dispersed collection points.

[0008] In an implementation of the first aspect, the step of determining the fitting coefficient of the lane centerline based on the lane centerline information includes: determining the fitting coefficient of the lane centerline based on the cubic polynomial, the fitting coefficient including a constant term coefficient, a linear term coefficient, and a quadratic term coefficient.

[0009] In an implementation of the first aspect, the step of performing trajectory compensation processing on the fitting coefficient in combination with the lane change time, road width and current speed of the vehicle to determine the compensated fitting coefficient includes: performing deviation compensation processing on the constant term coefficient in combination with the lane change time and road width to determine a first compensation coefficient; performing angle compensation processing on the linear term coefficient in combination with the lane change time and the current speed of the vehicle to determine a second compensation coefficient; and performing curvature compensation processing on the quadratic term coefficient in combination with the second compensation coefficient, the lane change time and the current speed of the vehicle to determine a third compensation coefficient.

[0010] In an implementation of the first aspect, the step of determining the lane changing trajectory based on the compensation fitting coefficient includes: using the first compensation coefficient as a new constant term coefficient, using the second compensation coefficient as a new linear term coefficient, and using the third compensation coefficient as a new quadratic term coefficient; the expression for determining the lane changing trajectory is: Y = first compensation coefficient + second compensation coefficient * x + third compensation coefficient * x2 + cubic term coefficient * x3; wherein Y represents the vertical coordinate of the midpoint of the lane changing trajectory, and x represents the horizontal coordinate of the midpoint of the lane changing trajectory.

[0011] In an implementation of the first aspect, after the step of determining the lane change trajectory based on the compensation fitting coefficient, the method further includes: performing trajectory compensation processing on the first compensation coefficient, the second compensation coefficient, and the third compensation coefficient in combination with the lane change time, the road width, and the current speed of the vehicle to iteratively update the lane change trajectory.

[0012] In an implementation of the first aspect, the method further includes: presetting a correspondence between the vehicle speed and / or road curvature and the lane change time; and calibrating the lane change time based on the current vehicle speed and / or road curvature in an actual driving scenario.

[0013] In a second aspect, the present application provides a system for generating a lane change trajectory for intelligent driving, the system comprising: a lane information acquisition module configured to acquire lane centerline information; the lane centerline information comprises coordinate information of each acquisition point of the lane centerline and a correlation coefficient of a lane line function expression; a coefficient determination module configured to determine a fitting coefficient of the lane centerline based on the lane centerline information; a coefficient compensation module configured to perform trajectory compensation processing on the fitting coefficient in combination with the lane change time, road width and current speed of the vehicle to determine a compensated fitting coefficient; a lane change trajectory determination module configured to determine the lane change trajectory based on the compensated fitting coefficient.

[0014] In a third aspect, the present application provides an electronic device, comprising: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the described method.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the described method when executed by an electronic device.

[0016] As described above, the method, system, electronic device, and medium for generating intelligent driving lane change trajectories described in this application have the following beneficial effects:

[0017] This application realizes the generation of lane change trajectories under low computing power project requirements through compensation and iterative update of cubic polynomial coefficients; the algorithm does not contain algorithms with high computing power requirements such as point scattering, matrix operations, quadratic programming, and extreme value solving; the lane change trajectory output by this application is different from previous complex solutions such as cubic spline curve interpolation or curve splicing, and can be continuously iterated and updated; the lane change trajectory output by the algorithm of this application is stable and smooth, without large noise, and can also meet the needs of intelligent driving lane change. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] FIG1 is a schematic diagram showing an application scenario of the method for generating a lane change trajectory for intelligent driving according to an embodiment of the present application.

[0019] FIG2 is a flow chart showing the principle of the method for generating an intelligent driving lane change trajectory according to an embodiment of the present application.

[0020] FIG3 shows a coefficient compensation flow chart of the method for generating an intelligent driving lane change trajectory according to an embodiment of the present application.

[0021] FIG4 shows a first compensation coefficient variation diagram of the method for generating an intelligent driving lane change trajectory according to an embodiment of the present application.

[0022] FIG5 shows a second compensation coefficient variation diagram of the method for generating an intelligent driving lane change trajectory according to an embodiment of the present application.

[0023] FIG6 shows a third compensation coefficient variation diagram of the method for generating an intelligent driving lane change trajectory according to an embodiment of the present application.

[0024] FIG7 shows a vehicle lane change trajectory diagram of the method for generating an intelligent driving lane change trajectory according to an embodiment of the present application.

[0025] FIG8 shows a schematic diagram of the structure of the system for generating the intelligent driving lane change trajectory according to an embodiment of the present application.

[0026] FIG9 is a schematic diagram showing the structural connection of the electronic device according to an embodiment of the present application.

[0027] Component Number Description 8 Intelligent Driving Lane Change Trajectory Generation System 81 Lane Information Acquisition Module 82 Coefficient Determination Module 83 Coefficient Compensation Module 84 Lane Change Trajectory Determination Module S21-S24 Steps S231-S233 Steps DETAILED DESCRIPTION

[0028] The following describes the embodiments of the present application through specific examples. Those skilled in the art can easily understand the other advantages and effects of the present application from the content disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other unless they conflict.

[0029] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application. Therefore, the illustrations only show components related to the present application and are not drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component can be changed at will, and the component layout type may also be more complicated.

[0030] The following embodiments of the present application provide methods, systems, electronic devices, and media for generating intelligent driving lane change trajectories, including but not limited to applications in intelligent driving vehicle lane change scenarios. This application scenario will be described below as an example.

[0031] Please refer to Figure 1, which shows a schematic diagram of an application scenario for the method for generating a lane change trajectory for intelligent driving described in an embodiment of this application. As shown in Figure 1, this embodiment illustrates an intelligent driving vehicle lane change scenario. The autonomous vehicle is traveling in lane L1 and will change lanes from lane L1 to lane L2 across the lane centerline. In this scenario, the method for generating a lane change trajectory for intelligent driving described in this application is used to generate a lane change trajectory for the vehicle from lane L1 to lane L2.

[0032] The technical solutions in the embodiments of the present application will be described in detail below with reference to the accompanying drawings in the embodiments of the present application.

[0033] Please refer to Figure 2, which shows a flow chart of the principle of the method for generating a lane change trajectory for intelligent driving according to an embodiment of the present application. As shown in Figure 2, this embodiment provides a method for generating a lane change trajectory for intelligent driving, which specifically includes the following steps:

[0034] S21, obtaining lane centerline information; the lane centerline information includes coordinate information of each acquisition point on the lane centerline and a correlation coefficient of a lane line function expression.

[0035] In one embodiment, the step of obtaining lane centerline information includes at least one of the following steps:

[0036] (1) Obtain the lane centerline using a pre-fitted cubic polynomial based on the coordinate information.

[0037] Specifically, if the lane centerline is pre-perceived or a cubic polynomial Y=A0+A1*x+A2*x is obtained, which is called by a data storage party 2 +A3*x 3 , it can be used directly.

[0038] (2) Obtain the coordinate information of the scattered collection points on the lane centerline, and fit a cubic polynomial based on the coordinate information of the scattered collection points.

[0039] Specifically, if the coordinate information received is the coordinate information of the sensor collection points scattered on the lane centerline, a cubic polynomial fitting is required: Y = A0 + A1*x + A2*x 2 +A3*x 3 .

[0040] S22: Determine a lane centerline fitting coefficient based on the lane centerline information.

[0041] In one embodiment, the step of determining a lane centerline fitting coefficient based on the lane centerline information includes:

[0042] The fitting coefficient of the lane centerline is determined according to the cubic polynomial, where the fitting coefficient includes a constant term coefficient, a linear term coefficient, and a quadratic term coefficient.

[0043] Specifically, for the cubic polynomial Y=A0+A1*x+A2*x 2 +A3*x 3 The determined fitting coefficients include the constant term coefficient A0, the linear term coefficient A1, the quadratic term coefficient A2 and the cubic term coefficient A3.

[0044] S23 , performing trajectory compensation processing on the fitting coefficient in combination with the lane change time, the road width, and the current speed of the vehicle to determine a compensated fitting coefficient.

[0045] Please refer to FIG3, which shows a coefficient compensation flow chart of the method for generating a lane change trajectory for intelligent driving according to an embodiment of the present application. As shown in FIG3, step S23 specifically includes:

[0046] S231 , performing deviation compensation processing on the constant term coefficient in combination with the lane change time and the road width to determine a first compensation coefficient.

[0047] Specifically, based on a Cartesian coordinate system (centered on the vehicle's rear axle), the constant term coefficient A0 of the perception output cubic polynomial is compensated for deviations based on independent variables such as lane change time Ts and road width Lw to obtain Lc_A0 (lane change A0). The formula is as follows:

[0048] In actual applications, formula (1) is output at the beginning of the lane change, and formula (2) is output when the vehicle crosses the lane line and moves from the current lane to the target lane. Here, Lw is the lane width, and Ts is the lane change time (which can be calibrated in actual applications). The change of the Lc_A0 coefficient of the vehicle's driving trajectory during the lane change process is shown in Figure 4, where the horizontal axis is time and the vertical axis is the lane change trajectory value.

[0049] S232 , performing angle compensation processing on the linear term coefficient in combination with the lane change time and the current speed of the vehicle to determine a second compensation coefficient.

[0050] Specifically, the linear term coefficient A1 of the cubic polynomial output by the perception is compensated for angles based on independent variables such as the lane change time Ts and the current vehicle speed V to obtain a new coefficient Lc_A1, as shown in the following formula:

[0051] Where sign is the sign of the vehicle coordinate system transformation (positive or negative 1) when the vehicle crosses the lane line and moves from the current lane to the target lane. The change of the Lc_A1 coefficient of the vehicle's driving trajectory during the lane change is shown in Figure 5, where the horizontal axis is time and the vertical axis is the lane change trajectory value.

[0052] S233: Perform curvature compensation on the quadratic term coefficient in combination with the second compensation coefficient, the lane change time, and the current speed of the vehicle to determine a third compensation coefficient.

[0053] Specifically, the quadratic term coefficient A2 of the perception output cubic polynomial is subjected to curvature compensation based on the angle compensation Lc_A1 calculated in step S232 and independent variables such as the lane change time Ts and the current vehicle speed V to obtain a new coefficient Lc_A2; the formula is as follows:

[0054] Where sign is the sign of the vehicle coordinate system transformation when the vehicle crosses the lane line and moves from the current lane to the target lane (positive or negative 1). The change in the Lc_A2 coefficient of the vehicle's driving trajectory during the lane change process is shown in Figure 6, where the horizontal axis is time and the vertical axis is the lane change trajectory value.

[0055] S24: Determine a lane change trajectory based on the compensation fitting coefficient.

[0056] In one embodiment, step S24 specifically includes:

[0057] (1) The first compensation coefficient is used as a new constant term coefficient, the second compensation coefficient is used as a new linear term coefficient, and the third compensation coefficient is used as a new quadratic term coefficient.

[0058] (2) The expression for determining the lane change trajectory is: Y = first compensation coefficient + second compensation coefficient * x + third compensation coefficient * x2 + cubic term coefficient * x3; wherein Y represents the ordinate of the midpoint of the lane change trajectory, and x represents the abscissa of the midpoint of the lane change trajectory.

[0059] Specifically, according to the above steps, the relevant new coefficients Lc_A0, Lc_A1, Lc_A2 are input into the original cubic equation, and the equation is Y=Lc_A0+Lc_A1*x+Lc_A2*x 2 +A3*x 3 .

[0060] In one embodiment, after the step of determining the lane change trajectory based on the compensation fitting coefficient, the method further includes:

[0061] In combination with the lane change time, the road width, and the current speed of the vehicle, trajectory compensation processing is performed on the first compensation coefficient, the second compensation coefficient, and the third compensation coefficient to iteratively update the lane change trajectory.

[0062] Specifically, steps S23 and S24 are repeated to iterate the data within a continuous time, and the lane change trajectory of the vehicle during the lane change process changes as shown in FIG7 below, where the horizontal axis is time and the vertical axis is the lane change trajectory value.

[0063] In practical applications, the lane line function expression of the lane change trajectory generation method of the present application takes the cubic spline curve as an example, and can also be derived into an N-order curve, and the lane change trajectory can be obtained using the lane change trajectory generation method of the present application; and the coefficient compensation method of the N-order curve can also be different; it is possible to compensate N-1 or Nn.

[0064] In one embodiment, the method further includes:

[0065] A correspondence between the vehicle speed and / or road curvature and the lane change time is pre-set; and the lane change time is calibrated based on the vehicle's current speed and / or road curvature in an actual driving scenario.

[0066] Specifically, for example, based on vehicle speed, if the vehicle speed is 50km / h, the lane change time is set to 7s, and if the vehicle speed is 80km / h, the lane change time is set to 5s; similarly, based on the lane change condition on a curve, if the road curvature is smaller, the lane change time is set to be slightly shorter, and if the road curvature is larger, the lane change time can be set to be slightly longer. The specific scenario is subject to the actual situation; if the lane is changed in a condition with a larger road curvature, such as a curve ramp with a curvature radius of 50m, considering safety factors, it is not recommended to change lanes at this time, so no lane change time calibration is performed. In actual applications, the correspondence between the vehicle speed and / or road curvature and the lane change time can be found through a one-dimensional table lookup or a two-dimensional table lookup to find the calibration value of the lane change time corresponding to the actual driving scenario.

[0067] The protection scope of the method for generating a lane change trajectory for intelligent driving described in the embodiment of the present application is not limited to the execution order of the steps listed in this embodiment. All solutions implemented by adding, subtracting, or replacing steps in the prior art based on the principles of the present application are included in the protection scope of the present application.

[0068] An embodiment of the present application also provides a system for generating an intelligent driving lane change trajectory. The system for generating an intelligent driving lane change trajectory can implement the method for generating an intelligent driving lane change trajectory described in the present application. However, the implementation device of the method for generating an intelligent driving lane change trajectory described in the present application includes but is not limited to the structure of the system for generating an intelligent driving lane change trajectory listed in the present embodiment. All structural variations and replacements of the prior art made according to the principles of the present application are included in the scope of protection of the present application.

[0069] Please refer to Figure 8, which shows a schematic diagram of the structure of a system for generating intelligent driving lane change trajectories according to an embodiment of the present application. As shown in Figure 8, this embodiment provides an intelligent driving lane change trajectory generation system 8, which specifically includes a lane information acquisition module 81, a coefficient determination module 82, a coefficient compensation module 83, and a lane change trajectory determination module 84.

[0070] The lane information acquisition module 81 is configured to acquire lane centerline information; the lane centerline information includes coordinate information of each acquisition point on the lane centerline and a correlation coefficient of a lane line function expression.

[0071] In one embodiment, the lane information acquisition module 81 is specifically configured to obtain a cubic polynomial pre-fitted based on the coordinate information of the lane centerline; or obtain the coordinate information of scattered collection points on the lane centerline and fit a cubic polynomial based on the coordinate information of the scattered collection points.

[0072] The coefficient determination module 82 is configured to determine the lane centerline fitting coefficient according to the lane centerline information.

[0073] In one embodiment, the coefficient determination module 82 is specifically configured to determine the fitting coefficients of the lane centerline according to the cubic polynomial, where the fitting coefficients include a constant term coefficient, a linear term coefficient, and a quadratic term coefficient.

[0074] The coefficient compensation module 83 is configured to perform trajectory compensation processing on the fitting coefficient in combination with the lane change time, the road width and the current speed of the vehicle to determine the compensated fitting coefficient.

[0075] In one embodiment, the coefficient compensation module 83 is specifically configured to perform deviation compensation on the constant term coefficient in combination with the lane change time and the road width to determine a first compensation coefficient; perform angle compensation on the linear term coefficient in combination with the lane change time and the current speed of the vehicle to determine a second compensation coefficient; and perform curvature compensation on the quadratic term coefficient in combination with the second compensation coefficient, the lane change time and the current speed of the vehicle to determine a third compensation coefficient.

[0076] The lane change trajectory determination module 84 is configured to determine a lane change trajectory based on the compensation fitting coefficient.

[0077] In one embodiment, the lane change trajectory determination module 84 is specifically configured to use the first compensation coefficient as a new constant term coefficient, the second compensation coefficient as a new linear term coefficient, and the third compensation coefficient as a new quadratic term coefficient; the expression for determining the lane change trajectory is: Y = first compensation coefficient + second compensation coefficient * x + third compensation coefficient * x2 + cubic term coefficient * x3; wherein Y represents the vertical coordinate of the midpoint of the lane change trajectory, and x represents the horizontal coordinate of the midpoint of the lane change trajectory.

[0078] In one embodiment, the system further includes: an iterative update module configured to perform trajectory compensation processing on the first compensation coefficient, the second compensation coefficient, and the third compensation coefficient in combination with the lane change time, the road width, and the current speed of the vehicle to iteratively update the lane change trajectory.

[0079] In one embodiment, the system further includes: a lane change time calibration module configured to pre-set a correspondence between the vehicle speed and / or road curvature and the lane change time; and calibrate the lane change time based on the vehicle's current speed and / or road curvature in an actual driving scenario.

[0080] In the several embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.

[0081] The modules / units described as separate components may or may not be physically separate, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place or distributed across multiple network elements. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into a processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into a single module / unit.

[0082] Those skilled in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the composition and steps of each example according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0083] The present application provides an electronic device, which includes: a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device performs the method described.

[0084] Please refer to Figure 9, which shows a schematic diagram of the structural connection of the electronic device described in an embodiment of the present application. As shown in Figure 9, the electronic device 9 of the present application includes: a processor 91, a memory 92, a communication interface 93 and / or a system bus 94. The memory 92 and the communication interface 93 are connected to the processor 91 via the system bus 94 and communicate with each other. The memory 92 is used to store computer programs, the communication interface 93 is used to communicate with other devices, and the processor 91 is used to run the computer program to enable the electronic device 9 to execute each step of the method for generating an intelligent driving lane change trajectory.

[0085] The above-mentioned processor 91 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0086] The memory 92 may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0087] The system bus 94 mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The system bus 94 can be divided into an address bus, a data bus, a control bus, etc. The communication interface is used to implement communication between the database access device and other devices (such as clients, read-write libraries, and read-only libraries).

[0088] The embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, which implements the method described above when executed by an electronic device.

[0089] Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiment can be performed by instructing a processor through a program, and the program can be stored in a computer-readable storage medium, which is a non-transitory medium, such as a random access memory, a read-only memory, a flash memory, a hard disk, a solid-state drive, a magnetic tape, a floppy disk, an optical disc, and any combination thereof. The above storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (DVD)), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0090] The descriptions of the processes or structures corresponding to the above figures have different focuses. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0091] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Anyone skilled in the art may modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by one of ordinary skill in the art without departing from the spirit and technical concepts disclosed in this application shall be covered by the claims of this application.

Claims

1. A method for generating a lane-changing trajectory of intelligent driving, characterized in that, The method includes: Obtaining lane centerline information; the lane centerline information includes the coordinate information of each acquisition point of the lane centerline and the correlation coefficient of the lane line function expression; Determining the fitting coefficient of the lane centerline according to the lane centerline information; Combining the lane change time, road width, and current vehicle speed, performing trajectory compensation processing on the fitting coefficient to determine the compensated fitting coefficient; Determining the lane change trajectory based on the compensated fitting coefficient.

2. The method according to claim 1, wherein The step of obtaining lane centerline information includes at least one of the following steps: Obtaining a cubic polynomial pre-fitted for the lane centerline based on coordinate information; Obtaining the coordinate information of scattered acquisition points on the lane centerline, and fitting them into a cubic polynomial based on the coordinate information of the scattered acquisition points.

3. The method according to claim 2, characterized in that The step of determining the fitting coefficient of the lane centerline according to the lane centerline information includes: Determining the fitting coefficient of the lane centerline according to the cubic polynomial, and the fitting coefficient includes the constant term coefficient, the first-order term coefficient, and the second-order term coefficient.

4. The method according to claim 3, wherein The step of combining the lane change time, road width, and current vehicle speed, performing trajectory compensation processing on the fitting coefficient to determine the compensated fitting coefficient includes: Combining the lane change time and road width, performing deviation compensation processing on the constant term coefficient to determine the first compensation coefficient; Combining the lane change time and the current vehicle speed, performing angle compensation processing on the first-order term coefficient to determine the second compensation coefficient; Combining the second compensation coefficient, the lane change time, and the current vehicle speed, performing curvature compensation processing on the second-order term coefficient to determine the third compensation coefficient.

5. The method according to claim 4, wherein The step of determining the lane change trajectory based on the compensated fitting coefficient includes: Taking the first compensation coefficient as the new constant term coefficient, taking the second compensation coefficient as the new first-order term coefficient, and taking the third compensation coefficient as the new second-order term coefficient; The expression for determining the lane-changing trajectory is: Y = First compensation coefficient + Second compensation coefficient * x + Third compensation coefficient * x 2 + Cubic term coefficient * x 3 ; where Y represents the ordinate of the midpoint of the lane-changing trajectory, and x represents the abscissa of the midpoint of the lane-changing trajectory.

6. The method according to claim 5, wherein After the step of determining the lane change trajectory based on the compensated fitting coefficient, the method further includes: Combining the lane change time, the road width, and the current vehicle speed, performing trajectory compensation processing on the first compensation coefficient, the second compensation coefficient, and the third compensation coefficient to iteratively update the lane change trajectory.

7. The method according to claim 1, characterized in that, The method further includes: Pre-setting the correspondence between the vehicle speed and / or road curvature and the lane change time; Calibrating the lane change time in combination with the current vehicle speed and / or road curvature in the actual driving scenario.

8. An intelligent driving lane-changing trajectory generation system, characterized in that, The system includes: A lane information acquisition module configured to acquire lane centerline information; the lane centerline information includes the coordinate information of each acquisition point of the lane centerline and the correlation coefficient of the lane line function expression; A coefficient determination module configured to determine the fitting coefficient of the lane centerline according to the lane centerline information; A coefficient compensation module configured to combine the lane change time, road width, and current vehicle speed, perform trajectory compensation processing on the fitting coefficient to determine the compensated fitting coefficient; A lane change trajectory determination module configured to determine the lane change trajectory based on the compensated fitting coefficient.

9. An electronic device, characterized in that, The electronic device includes: a processor and a memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the electronic device, the method according to any one of claims 1 to 7 is implemented.

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