A trajectory fast resampling method, system, device and medium based on unmanned motion constraints

Abstract

This invention relates to the field of autonomous driving technology, specifically to a method, system, device, and medium for fast trajectory resampling based on autonomous driving motion constraints. This method introduces vehicle kinematics to control a virtual vehicle to move along a desired trajectory, thereby generating a trajectory that satisfies the kinematics, overcoming the shortcomings of filtering methods that cannot take kinematics into account. Simultaneously, to address the problem that control-based methods cannot converge at the final target point of the trajectory, a combination of forward and backward filtering is used, solving the lag problem caused by simple filtering methods while inheriting the advantage of fast computation speed of filtering methods for rapid trajectory resampling.

Description

Technical Field

[0001] This invention relates to the field of autonomous driving technology, and more specifically, to a method, system, device, and medium for rapid trajectory resampling based on autonomous driving motion constraints. Background Technology

[0002] During autonomous vehicle operation, the driving trajectory is typically fixed, requiring the pre-recording of a series of discrete points as a reference trajectory to control the vehicle's movement along that trajectory. However, the original recorded points are often noisy and sparse. Directly using such a trajectory for tracking may not meet the requirements for driving comfort, and could even cause the vehicle to fail to track the trajectory due to abrupt changes in curvature. Therefore, it is necessary to smooth and resample the original discrete points.

[0003] In existing technologies, optimization-based methods are typically used to process the original trajectory to satisfy kinematic constraints and improve trajectory smoothness. However, these methods have high computational complexity and consume significant computational resources.

[0004] The Chinese invention patent with publication number "CN117367454A" and titled "An Autonomous Driving Trajectory Planning Method, Device, Equipment and Storage Medium Published in Chinese Patent Documents" proposes a method for optimizing trajectory smoothing based on quadratic programming. It can generate smooth trajectories and meet the vehicle kinematic requirements, but the calculation is complex and time-consuming.

[0005] Similarly, the reference line smoothing method mentioned in the Chinese patent document "A planning path reference line smoothing method based on curvature constraint pre-smoothing" (publication number CN115563440A, application date: 2023.01.03) is also a trajectory smoothing method based on quadratic programming optimization.

[0006] The Chinese invention patent with publication number "CN116400681A" and titled "A method and device for real-time fitting of the running trajectory of unmanned logistics vehicles" mentions that a method of fitting discrete points using a fifth-order polynomial is used. This is slightly faster than the optimization-based method and can ensure the continuity of curvature, but it cannot guarantee that the minimum turning radius of the vehicle is met.

[0007] Besides optimization-based and multinomial fitting-based methods, there are also filtering-based methods. Optimization-based methods are computationally complex and time-consuming. Fitting-based methods are faster than optimization-based methods, but still relatively time-consuming, and are a two-stage method involving fitting followed by resampling. Filtering-based methods are faster, but struggle to meet kinematic constraints. Therefore, there is an urgent need for a method that can quickly resample trajectories while ensuring kinematic constraints. Summary of the Invention

[0008] This invention addresses the problem that existing resampling methods struggle to satisfy kinematic constraints. It proposes a fast trajectory resampling method, system, device, and medium based on autonomous driving motion constraints. This method introduces vehicle kinematics to control a virtual vehicle along a desired trajectory, generating a kinematically satisfactory trajectory and overcoming the limitation of filtering methods in not considering kinematics. Furthermore, to address the issue of control-based methods failing to converge at the final target point, a combination of forward and backward filtering is used. This solves the lag problem inherent in simple filtering methods while retaining the high computational speed of traditional filtering methods, enabling rapid trajectory resampling while ensuring smooth transportation.

[0009] The specific implementation details of this invention are as follows:

[0010] A fast trajectory resampling method based on motion constraints in autonomous driving includes the following steps:

[0011] Step S1: Convert the acquired raw trajectory into a list of trajectory points;

[0012] Step S2: Based on the list of trajectory points, initialize the current point c and assign it as the starting point. Find the point p that is closest to the current point c in the original trajectory, and start from point p to find a point q that is outside the pre-aiming distance until point p is the last point of the original trajectory, thus completing the forward filtering.

[0013] Step S3: Initialize the current point c' and assign it the endpoint value. Find the point p' that is closest to the current point c' in the original trajectory, and start from point p' to find a point q' that is outside the pre-aiming distance until point p' is the first point of the original trajectory, thus completing the backward filtering.

[0014] Step S4: Repeat steps S2-S3, using the filtered trajectory as the new smooth trajectory.

[0015] To better realize the present invention, step S1 further includes the following steps:

[0016] Step S11: Obtain the original trajectory from the autonomous driving system;

[0017] Step S12: Convert the original trajectory into a list of trajectory points Traj_Input stored in a set format, and define the first point of the trajectory as the starting point P. start The last point is defined as the endpoint P. end The trajectory point is defined as

[0018] To better realize the present invention, step S2 further includes the following steps:

[0019] Step S21: Initialize the current point c and assign it the value P start Initialize an empty trajectory Traj_Output and initialize the filter coefficients;

[0020] Step S22: Find the point p that is closest to the current point c in the original trajectory, and starting from point p, sequentially find the first point q outside the preview distance, and update the filter coefficients;

[0021] Step S23: Control the current point c to move in a direction closer to point q by a step size of step; the step size is the resampling interval;

[0022] Step S24: Insert the moved point c into the trajectory Traj_Output;

[0023] Step S25: Repeat steps S22-S24 until point p is the last point of the trajectory Traj_Output, then overwrite the trajectory point list Traj_Input with the trajectory Traj_Output.

[0024] To better realize the present invention, step S23 further includes the following steps:

[0025] Step S231: Calculate the positional deviation dx and positional deviation dy between the current point c and point q;

[0026] Step S232: Calculate the straight-line distance d from the current point c to the point q based on the position deviation dx and the position deviation dy;

[0027] Step S233: Calculate the heading deviation α between the line connecting current point c and point q and current point c;

[0028] Step S234: Based on the heading deviation α, calculate the heading deviation β of the line connecting point q with the current point c and point q.

[0029] Step S235: Calculate the new curvature k based on the set control gain, straight distance d, heading deviation α, heading deviation β, and filter coefficients;

[0030] Step S236: Calculate the radius of curvature r of the trajectory and the instantaneous center O of the trajectory based on the curvature k. x The new heading angle θ;

[0031] Step S237: Based on the instantaneous center O of the trajectory x Given the trajectory curvature radius r and heading angle θ, calculate the new coordinate values ​​and update the value of the current point c to (x, y, θ, k).

[0032] To better realize the present invention, step S3 further includes the following steps:

[0033] Step S31: Initialize the current point c' and assign it the value P end Initialize an empty trajectory Traj_Output and initialize the filter coefficients;

[0034] Step S32: Find the point p' that is closest to the current point c' in the original trajectory Traj_Input, and starting from point p', search in reverse order for the first point q' outside the preview distance preview_distance, and update the filter coefficients;

[0035] Step S33: Control the current point c' to move in a direction closer to point q' by a step size of step; the step size is the resampling interval;

[0036] Step S34: Insert the moved point c' into the trajectory Traj_Output;

[0037] Step S35: Repeat steps S32-S34 until point p' is the first point of the trajectory Traj_Output, then overwrite the trajectory point list Traj_Input with the trajectory Traj_Output.

[0038] Based on the above-mentioned fast trajectory resampling method based on autonomous driving motion constraints, in order to better realize the present invention, a fast trajectory resampling system based on autonomous driving motion constraints is further proposed to execute the above-mentioned fast trajectory resampling method based on autonomous driving motion constraints; including an original trajectory input unit, a forward filtering unit, a backward filtering unit, and an iteration unit;

[0039] The original trajectory input unit is used to convert the acquired original trajectory into a list of trajectory points;

[0040] The forward filtering unit is used to initialize the current point c and assign it as the starting point according to the trajectory point list, find the point p that is closest to the current point c in the original trajectory, and start from point p to find a point q that is outside the pre-aiming distance until point p is the last point of the original trajectory, thus completing the forward filtering;

[0041] The backward filtering unit is used to initialize the current point c' and assign it the endpoint, find the point p' closest to the current point c' in the original trajectory, and start from point p' to find a point q' outside the pre-aiming distance until point p' is the first point of the original trajectory, thus completing the backward filtering;

[0042] The iterative unit is used to repeatedly perform forward filtering and backward filtering, using the filtered trajectory as a new smooth trajectory.

[0043] Based on the above-mentioned fast trajectory resampling method based on autonomous driving motion constraints, in order to better realize the present invention, an electronic device is further proposed, including a memory and a processor; the memory stores a computer program; when the computer program is executed on the processor, the above-mentioned fast trajectory resampling method based on autonomous driving motion constraints is implemented.

[0044] Based on the above-mentioned fast trajectory resampling method based on autonomous driving motion constraints, in order to better realize the present invention, a computer-readable storage medium is further proposed, wherein computer instructions are stored on the computer-readable storage medium; when the computer instructions are executed on the above-mentioned electronic device, the above-mentioned fast trajectory resampling method based on autonomous driving motion constraints is realized.

[0045] The present invention has the following beneficial effects:

[0046] (1) This invention combines forward filtering and backward filtering to quickly resample the trajectory while ensuring kinematic constraints, thus solving the lag problem caused by filtering.

[0047] (2) This invention makes the calculation speed between the filtering method and the polynomial fitting method, thus improving the calculation speed;

[0048] (3) This invention introduces vehicle kinematics to control the virtual car to move toward the desired trajectory, thereby generating a trajectory that satisfies the kinematics and makes up for the shortcomings of the filtering method that cannot take kinematics into account. By combining kinematic calculation with filtering, the problem that simple filtering cannot satisfy geometric kinematics is solved. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the original input trajectory provided by the present invention.

[0050] Figure 2 This is a schematic diagram of the heading deviation angle and instantaneous center of the circle provided by the present invention.

[0051] Figure 3 This is a schematic diagram illustrating the kinematic geometry recursion provided by the present invention.

[0052] Figure 4 A schematic diagram of the smoothed trajectory provided by the present invention. Detailed Implementation

[0053] To more clearly illustrate the technical solutions of the embodiments of the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments, and therefore should not be regarded as a limitation on the scope of protection. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "set up," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0055] Example 1:

[0056] This embodiment proposes a fast trajectory resampling method based on motion constraints of autonomous driving, which specifically includes the following steps:

[0057] A fast trajectory resampling method based on motion constraints in autonomous driving includes the following steps:

[0058] Step S1: Convert the acquired raw trajectory into a list of trajectory points;

[0059] Step S1 specifically includes the following steps:

[0060] Step S11: Obtain the original trajectory from the autonomous driving system;

[0061] Step S12: Convert the original trajectory into a list of trajectory points Traj_Input stored in a set format, and define the first point of the trajectory as the starting point P. start The last point is defined as the endpoint P. end The trajectory point is defined as

[0062] Step S2: Based on the list of trajectory points, initialize the current point c and assign it as the starting point. Find the point p that is closest to the current point c in the original trajectory, and start from point p to find a point q that is outside the pre-aiming distance until point p is the last point of the original trajectory, thus completing the forward filtering.

[0063] Step S2 specifically includes the following steps:

[0064] Step S21: Initialize the current point c and assign it the value Pstart Initialize an empty trajectory Traj_Output and initialize the filter coefficients;

[0065] Step S22: Find the point p that is closest to the current point c in the original trajectory, and starting from point p, sequentially find the first point q outside the preview distance, and update the filter coefficients;

[0066] Step S23: Control the current point c to move in a direction closer to point q by a step size of step; the step size is the resampling interval;

[0067] Step S23 specifically includes the following steps:

[0068] Step S231: Calculate the positional deviation dx and positional deviation dy between the current point c and point q;

[0069] Step S232: Calculate the straight-line distance d from the current point c to the point q based on the position deviation dx and the position deviation dy;

[0070] Step S233: Calculate the heading deviation α between the line connecting current point c and point q and current point c;

[0071] Step S234: Based on the heading deviation α, calculate the heading deviation β of the line connecting point q with the current point c and point q.

[0072] Step S235: Calculate the new curvature k based on the set control gain, straight distance d, heading deviation α, heading deviation β, and filter coefficients;

[0073] Step S236: Calculate the radius of curvature r of the trajectory and the instantaneous center O of the trajectory based on the curvature k. x The new heading angle θ;

[0074] Step S237: Based on the instantaneous center O of the trajectory x Given the trajectory curvature radius r and heading angle θ, calculate the new coordinate values ​​and update the value of the current point c to (x, y, θ, k).

[0075] Step S24: Insert the moved point c into the trajectory Traj_Output;

[0076] Step S25: Repeat steps S22-S24 until point p is the last point of the trajectory Traj_Output, then overwrite the trajectory point list Traj_Input with the trajectory Traj_Output.

[0077] Step S3: Initialize the current point c' and assign it the endpoint value. Find the point p' that is closest to the current point c' in the original trajectory, and start from point p' to find a point q' that is outside the pre-aiming distance until point p' is the first point of the original trajectory, thus completing the backward filtering.

[0078] Step S3 specifically includes the following steps:

[0079] Step S31: Initialize the current point c' and assign it the value P end Initialize an empty trajectory Traj_Output and initialize the filter coefficients;

[0080] Step S32: Find the point p' that is closest to the current point c' in the original trajectory Traj_Input, and starting from point p', search in reverse order for the first point q' outside the preview distance preview_distance, and update the filter coefficients;

[0081] Step S33: Control the current point c' to move in a direction closer to point q' by a step size of step; the step size is the resampling interval;

[0082] Step S34: Insert the moved point c' into the trajectory Traj_Output;

[0083] Step S35: Repeat steps S32-S34 until point p' is the first point of the trajectory Traj_Output, then overwrite the trajectory point list Traj_Input with the trajectory Traj_Output.

[0084] Step S4: Repeat steps S2-S3, using the filtered trajectory as the new smooth trajectory.

[0085] Working principle: This embodiment introduces vehicle kinematics to control the virtual car to move towards the desired trajectory, thereby generating a trajectory that satisfies the kinematics and makes up for the shortcomings of filtering methods that cannot take kinematics into account. At the same time, in order to solve the problem that the control-based method cannot converge at the final target point of the trajectory, a combination of forward filtering and backward filtering is used to solve the lag problem caused by the simple filtering method, while inheriting the advantage of fast calculation speed of the filtering method, so as to ensure transportation while quickly resampling the trajectory.

[0086] Example 2:

[0087] This embodiment is based on the above embodiment 1, such as... Figure 1 , Figure 2 , Figure 3 , Figure 4 As shown, a specific embodiment will be described in detail.

[0088] This embodiment primarily generates a new smooth trajectory from the original trajectory by combining the concepts of filtering and control. Specifically, by introducing vehicle kinematics, the virtual car is controlled to move towards the desired trajectory to generate a trajectory that satisfies the kinematics, thus overcoming the limitation of filtering methods in not taking kinematics into account. Simultaneously, to address the issue that control-based methods cannot converge at the final target point of the trajectory, this application proposes a method combining forward and backward filtering. This method simultaneously solves the lag problem caused by simple filtering methods while retaining the advantage of fast computation speed.

[0089] Specifically, the trajectory smoothing method described in this application includes the following steps:

[0090] Step S1: Sample the raw trajectory obtained from the autonomous driving system, such as a continuous list of path points obtained through GNSS / RTK system for specific road path layout. The aim is to convert the raw trajectory into a list of trajectory points stored in a specified format, named Traj_Input. The specified trajectory points consist of x-coordinate, y-coordinate, and heading angle. Curvature κ description, i.e. And save the first point of the trajectory as the starting point P. start The last point is the destination P. end .

[0091] Step S2: Forward filtering.

[0092] Step S21: Initialize the current point c and assign it the value P. start Initialize an empty trajectory Traj_Output and initialize the filter coefficient λ = 1.0;

[0093] Step S22: Find the point p that is closest to the current point c in the original trajectory, and starting from point p, sequentially find the first point q outside the set preview distance, and update the filter coefficient λ = 0.9λ;

[0094] Step S23: Kinematic interpolation calculation: Control the current point c to move in a direction approaching point q by a step distance of step; where step is the resampling interval.

[0095] Step S24: Insert the new value of the current point, i.e., the point c after the movement, into the trajectory Traj_Output;

[0096] Step S25: Return to step S22 and repeat until point p is the last point of the input trajectory, then overwrite Traj_Input with Traj_Output.

[0097] The kinematic interpolation described in step S23 specifically includes the following steps:

[0098] Step S231: Calculate the position deviation between the current point c and the point q;

[0099] d x = x q - x c

[0100] d y = y q - y c

[0101] Step S232: Calculate the straight-line distance from the current point c to the point q;

[0102]

[0103] Step S233: Calculate the course deviation between the direction of the line connecting the current points c and q and the course of the current point c;

[0104]

[0105] where, is the course angle of point c;

[0106] Step S234: Calculate the course deviation of the current point q from the direction of the line connecting the current points c and q;

[0107]

[0108] where, is the course angle of point q;

[0109] Step S235: Set the control gains kp, kd. After testing, it works well when kp = 5 and kd = 2, and calculate the new curvature;

[0110]

[0111] Step S236: Calculate the radius

[0112] Limit the turning radius: Let the minimum physical turning radius of the vehicle be R. If |r| < R, then

[0113] The instantaneous center of the trajectory

[0114] The new course angle where

[0115] Step S237: Calculate the new coordinates; x = O x + r × sin(θ) y = O y - r × cos(θ);

[0116] Step S238: Update the value of the current point c to (x, y, θ, κ).

[0117] Step S3: Backward filtering.

[0118] Step S31: Initialize the current point c' and assign it the value P. end Initialize an empty trajectory Traj_Output. Initialize the filter coefficients λ = 1.0;

[0119] Step S32: Find the point p' closest to the current point c' in the trajectory Traj_Input, and starting from point p', search in reverse order for the first point q' outside the preview distance preview_distance, and update λ = 0.9λ;

[0120] Step S33: Kinematic interpolation calculation: Control the current point c' to move a distance of step in the direction approaching point q';

[0121] Step S34: Insert the new value of the current point into the trajectory Traj_Output;

[0122] Step S35: Return to step S32 and repeat until point p' is the first point of the input trajectory. Overwrite Traj_Input with Traj_Output.

[0123] Working principle: This embodiment is used to smooth and resample noisy, sparse original sampled trajectory points, such as GNSS measurement data, to obtain a smooth, dense trajectory that satisfies kinematic constraints.

[0124] This embodiment combines forward and backward filtering to address the lag problem caused by filtering. By incorporating kinematic calculations into the filtering module, it solves the problem that simple filtering cannot satisfy geometric kinematics. The calculation speed is fast, and dense and smooth trajectory sampling can be obtained in one go.

[0125] The other parts of this embodiment are the same as those in Embodiment 1 above, so they will not be described again.

[0126] Example 3:

[0127] Based on any one of Embodiments 1-2 above, this embodiment proposes a fast trajectory resampling system based on autonomous driving motion constraints, used to execute the above-mentioned fast trajectory resampling method based on autonomous driving motion constraints; it includes an original trajectory input unit, a forward filtering unit, a backward filtering unit, and an iteration unit;

[0128] The original trajectory input unit is used to convert the acquired original trajectory into a list of trajectory points;

[0129] The forward filtering unit is used to initialize the current point c and assign it as the starting point according to the trajectory point list, find the point p that is closest to the current point c in the original trajectory, and start from point p to find a point q that is outside the pre-aiming distance until point p is the last point of the original trajectory, thus completing the forward filtering;

[0130] The backward filtering unit is used to initialize the current point c' and assign it the endpoint, find the point p' closest to the current point c' in the original trajectory, and start from point p' to find a point q' outside the pre-aiming distance until point p' is the first point of the original trajectory, thus completing the backward filtering;

[0131] The iterative unit is used to repeatedly perform forward filtering and backward filtering, using the filtered trajectory as a new smooth trajectory.

[0132] This embodiment also proposes an electronic device, including a memory and a processor; the memory stores a computer program; when the computer program is executed on the processor, it implements the above-mentioned fast trajectory resampling method based on autonomous driving motion constraints.

[0133] This embodiment also proposes a computer-readable storage medium storing computer instructions; when the computer instructions are executed on the aforementioned electronic device, the aforementioned fast trajectory resampling method based on autonomous driving motion constraints is implemented.

[0134] The other parts of this embodiment are the same as any one of the above embodiments 1-2, so they will not be described again.

[0135] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any simple modifications or equivalent changes made to the above embodiments based on the technical essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A fast trajectory resampling method based on motion constraints in autonomous driving, characterized in that, Specifically, the following steps are included: Step S1: Convert the acquired raw trajectory into a list of trajectory points; Step S2: Based on the list of trajectory points, initialize the current point c and assign it as the starting point. Find the point p that is closest to the current point c in the original trajectory, and start from point p to find a point q that is outside the pre-aiming distance until point p is the last point of the original trajectory, thus completing the forward filtering. Step S3: Initialize the current point c' and assign it the endpoint value. Find the point p' that is closest to the current point c' in the original trajectory, and start from point p' to find a point q' that is outside the pre-aiming distance until point p' is the first point of the original trajectory, thus completing the backward filtering. Step S4: Repeat steps S2-S3, using the filtered trajectory as the new smoothed trajectory; Step S1 specifically includes the following steps: Step S11: Obtain the original trajectory from the autonomous driving system; Step S12: Convert the original trajectory into a list of trajectory points Traj_Input stored in a set format, and define the first point of the trajectory as the starting point P. start The last point is defined as the endpoint P. end The trajectory point is defined as (x,y,φ,κ); Step S2 specifically includes the following steps: Step S21: Initialize the current point c and assign it the value P start Initialize an empty trajectory Traj_Output and initialize the filter coefficients; Step S22: Find the point p that is closest to the current point c in the original trajectory, and starting from point p, sequentially find the first point q outside the preview distance, and update the filter coefficients; Step S23: Control the current point c to move in a direction closer to point q by a step size of step; the step size is the resampling interval; Step S24: Insert the moved point c into the trajectory Traj_Output; Step S25: Repeat steps S22-S24 until point p is the last point of the trajectory Traj_Output, then overwrite the trajectory point list Traj_Input with the trajectory Traj_Output.

2. The fast trajectory resampling method based on autonomous driving motion constraints according to claim 1, characterized in that, Step S23 specifically includes the following steps: Step S231: Calculate the positional deviation dx and positional deviation dy between the current point c and point q; Step S232: Calculate the straight-line distance d from the current point c to the point q based on the position deviation dx and the position deviation dy; Step S233: Calculate the heading deviation α between the line connecting current point c and point q and current point c; Step S234: Based on the heading deviation α, calculate the heading deviation β of the line connecting point q with the current point c and point q. Step S235: Calculate the new curvature k based on the set control gain, straight distance d, heading deviation α, heading deviation β, and filter coefficients; Step S236: Calculate the radius of curvature r of the trajectory and the instantaneous center O of the trajectory based on the curvature k. x The new heading angle θ; Step S237: Based on the instantaneous center O of the trajectory x Given the trajectory curvature radius r and heading angle θ, calculate the new coordinate values ​​and update the value of the current point c to (x, y, θ, k).

3. The fast trajectory resampling method based on autonomous driving motion constraints according to claim 1, characterized in that, Step S3 specifically includes the following steps: Step S31: Initialize the current point c' and assign it the value P end Initialize an empty trajectory Traj_Output and initialize the filter coefficients; Step S32: Find the point p' that is closest to the current point c' in the original trajectory Traj_Input, and starting from point p', search in reverse order for the first point q' outside the preview distance preview_distance, and update the filter coefficients; Step S33: Control the current point c' to move in a direction closer to point q' by a step size of step; the step size is the resampling interval; Step S34: Insert the moved point c' into the trajectory Traj_Output; Step S35: Repeat steps S22-S24 until point p' is the first point of the trajectory Traj_Output, then overwrite the trajectory point list Traj_Input with the trajectory Traj_Output.

4. A trajectory fast resampling system based on autonomous driving motion constraints, used to execute the trajectory fast resampling method based on autonomous driving motion constraints as described in claim 1; characterized in that, It includes a raw trajectory input unit, a forward filtering unit, a backward filtering unit, and an iteration unit; The original trajectory input unit is used to convert the acquired original trajectory into a list of trajectory points; Converting the acquired raw trajectory into a list of trajectory points specifically includes: acquiring the raw trajectory from the autonomous driving system; converting the raw trajectory into a list of trajectory points Traj_Input stored in a set format, and defining the first point of the trajectory as the starting point P. start The last point is defined as the endpoint P. end The trajectory point is defined as (x,y,φ,κ); The forward filtering unit is used to initialize the current point c and assign it as the starting point according to the trajectory point list, and then filter from the original... Find the point p that is closest to the current point c in the initial trajectory, and start from point p to find a point q that is outside the pre-aiming distance, until point p is the last point of the original trajectory, thus completing the forward filtering; Forward filtering specifically includes: initializing the current point c and assigning it the value P. start Initialize an empty trajectory Traj_Output and initialize the filter coefficients; find the point p that is closest to the current point c in the original trajectory, and starting from point p, sequentially find the first point q outside the preview distance preview_distance, and update the filter coefficients; control the current point c to move in a direction closer to point q with a step size of step; the step size is the resampling interval; insert the moved point c into the trajectory Traj_Output; until point p is the last point of the trajectory Traj_Output, overwrite the trajectory point list Traj_Input with the trajectory Traj_Output; The backward filtering unit is used to initialize the current point c' and assign it the endpoint, find the point p' closest to the current point c' in the original trajectory, and start from point p' to find a point q' outside the pre-aiming distance until point p' is the first point of the original trajectory, thus completing the backward filtering; The iterative unit is used to repeatedly perform forward filtering and backward filtering, using the filtered trajectory as a new smooth trajectory.

5. An electronic device, characterized in that, It includes a memory and a processor; the memory stores a computer program; when the computer program is executed on the processor, it implements the trajectory fast resampling method based on unmanned driving motion constraints as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions; when the computer instructions are executed on the electronic device as described in claim 5, the trajectory fast resampling method based on unmanned driving motion constraints as described in any one of claims 1-3 is implemented.

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