METHOD AND APPARATUS FOR GENERATING OBSTACLE MOTION INFORMATION FOR AUTONOMOUS VEHICLE

1 . A method for generating obstacle motion information for an autonomous vehicle, the autonomous vehicle equipped with a lidar, and the method comprising:

acquiring an obstacle point cloud in a current frame and the obstacle point cloud in a reference frame characterizing a target obstacle with to-be-generated motion information, wherein the obstacle point cloud in the current frame is obtained based on a current laser point cloud frame collected by the lidar, and the obstacle point cloud in the reference frame is obtained based on a laser point cloud characterizing the target obstacle in a preset number of laser point cloud frames prior to the current laser point cloud frame collected by the lidar;

calculating a first observed displacement of the target obstacle corresponding to a first observed displacement amount in each of M first observed displacement amounts based on the obstacle point cloud in the current frame and the obstacle point cloud in the reference frame;

determining motion information of the target obstacle corresponding to the first observed displacement amount in the each of the M first observed displacement amounts based on M first observed displacements obtained through calculation and a sampling period of the lidar;

determining observed motion information of the target obstacle in accordance with a kinematic rule or a statistical rule based on the determined M types of motion information and historical motion information of the target obstacle; and

generating current motion information of the target obstacle using a preset filtering algorithm with the motion information of the target obstacle as a state variable, and the observed motion information as an observed amount.

2 . The method according to claim 1 , wherein before the determining observed motion information of the target obstacle in accordance with a kinematic rule or a statistical rule based on the determined M types of motion information and historical motion information of the target obstacle, the method further comprises:

determining whether the determined M types of motion information are ambiguous based on the determined M types of motion information and the historical motion information of the target obstacle; and

the determining observed motion information of the target obstacle in accordance with a kinematic rule or a statistical rule based on the determined M types of motion information and historical motion information of the target obstacle comprises:

determining the observed motion information of the target obstacle in accordance with the kinematic rule or the statistical rule based on the determined M types of motion information and the historical motion information of the target obstacle in response to determining the determined M types of motion information being not ambiguous.

3 . The method according to claim 2 , wherein the determining whether the determined M types of motion information are ambiguous based on the determined M types of motion information and the historical motion information of the target obstacle comprises:

determining, for each type of motion information in the determined M types of motion information, a residual vector between the motion information and motion information of the target obstacle in a last cycle;

determining a residual vector with a minimum modulus in the M residual vectors obtained through calculation as a first minimum residual vector;

determining the determined M types of motion information being not ambiguous in response to the modulus of the first minimum residual vector being less than a first preset modulus threshold; and

determining the determined M types of motion information being ambiguous in response to the modulus of the first minimum residual vector being greater than or equal to the first preset modulus threshold.

4 . The method according to claim 2 , wherein the determining whether the determined M types of motion information are ambiguous based on the determined M types of motion information and the historical motion information of the target obstacle comprises:

determining, for each type of motion information in the determined M types of motion information, a residual vector between the motion information and motion information of the target obstacle in a last cycle;

calculating an average vector of the determined M residual vectors;

determining a residual vector with a minimum modulus of a vector difference from the average vector obtained through calculation in the determined M residual vectors as a second minimum residual vector;

determining the determined M types of motion information being not ambiguous in response to the modulus of the second minimum residual vector being less than a second preset modulus threshold; and

determining the determined M types of motion information being ambiguous in response to the modulus of the second minimum residual vector being greater than or equal to the second preset modulus threshold.

5 . The method according to claim 3 , wherein the determining, for each type of motion information in the determined M types of motion information, a residual vector between the motion information and motion information of the target obstacle in a last cycle comprises:

determining, for each type of motion information in the determined M types of motion information, a differential vector between the motion information and the motion information of the target obstacle in the last cycle as the residual vector between the motion information and the motion information of the target obstacle in the last cycle.

6 . The method according to claim 3 , wherein the determining, for each type of motion information in the determined M types of motion information, a residual vector between the motion information and motion information of the target obstacle in a last cycle comprises:

executing for each type of motion information in the determined M types of motion information: generating estimated motion information of the target obstacle using the preset filtering algorithm with the motion information of the target obstacle as a state variable, and the motion information as an observed amount; and determining a differential vector between the generated estimated motion information and the motion information of the target obstacle in the last cycle as the residual vector between the motion information and the motion information of the target obstacle in the last cycle.

7 . The method according to claim 2 , further comprising:

calculating a second observed displacement of the target obstacle corresponding to a second observed displacement amount in each of N second observed displacement amounts based on the obstacle point cloud in the current frame and the obstacle point cloud in the reference frame in response to determining the determined M types of motion information being ambiguous, wherein the calculation amount of the second observed displacement amount in the each of the N second observed displacement amounts is greater than the calculation amount of the first observed displacement amount in the each of the M first observed displacement amounts;

determining motion information of the target obstacle corresponding to the second observed displacement amount in the each of the N second observed displacement amounts based on N second observed displacements obtained through calculation and the sampling period of the lidar; and

determining the observed motion information of the target obstacle in accordance with the kinematic rule or the statistical rule based on the determined N types of motion information, the determined M types of motion information and the historical motion information of the target obstacle.

8 . The method according to claim 7 , wherein before the generating current motion information of the target obstacle using a preset filtering algorithm with the motion information of the target obstacle as a state variable, and the observed motion information as an observed amount, the method further comprises:

determining whether the modulus of a residual vector between the observed motion information and the motion information of the target obstacle in the last cycle is greater than a third preset modulus threshold; and

updating the observed motion information using motion information obtained through multiplying the observed motion information by a first ratio in response to determining the modulus of the residual vector between the observed motion information and the motion information of the target obstacle in the last cycle being greater than the third preset modulus threshold, wherein the first ratio is obtained through dividing the third preset modulus threshold by the modulus of the residual vector between the observed motion information and the motion information of the target obstacle in the last cycle.

9 . The method according to claim 8 , wherein the generating current motion information of the target obstacle using a preset filtering algorithm with the motion information of the target obstacle as a state variable, and the observed motion information as an observed amount comprises:

adjusting a filtering parameter in the preset filtering algorithm based on a similarity between the obstacle point cloud in the current frame and the obstacle point cloud in the reference frame; and

generating current motion information of the target obstacle using the preset filtering algorithm with the adjusted filtering parameter with the motion information of the target obstacle as a state variable, and the observed motion information as an observed amount.

10 . The method according to claim 9 , wherein the motion information comprises at least one of: speed information, or acceleration information.

11 . The method according to claim 10 , wherein the M first observed displacement amounts comprise at least one of: an observed center displacement amount, an observed gravity center displacement amount, an observed edge center displacement amount, or an observed corner displacement amount.

12 . The method according to claim 11 , wherein the N second observed displacement amounts comprise an observed surface displacement amount.

13 . An apparatus for generating obstacle motion information for an autonomous vehicle, the autonomous vehicle equipped with a lidar, and the apparatus comprising:

at least one processor; and

a memory storing instructions, the instructions when executed by the at least one processor, cause the at least one processor to perform operations, the operations comprising:

acquiring an obstacle point cloud in a current frame and the obstacle point cloud in a reference frame characterizing a target obstacle with to-be-generated motion information, wherein the obstacle point cloud in the current frame is obtained based on a current laser point cloud frame collected by the lidar, and the obstacle point cloud in the reference frame is obtained based on a laser point cloud characterizing the target obstacle in a preset number of laser point cloud frames prior to the current laser point cloud frame collected by the lidar;

calculating a first observed displacement of the target obstacle corresponding to a first observed displacement amount in each of M first observed displacement amounts based on the obstacle point cloud in the current frame and the obstacle point cloud in the reference frame;

determining motion information of the target obstacle corresponding to the first observed displacement amount in the each of the M first observed displacement amounts based on M first observed displacements obtained through calculation and a sampling period of the lidar;

determining observed motion information of the target obstacle in accordance with a kinematic rule or a statistical rule based on the determined M types of motion information and historical motion information of the target obstacle; and

generating current motion information of the target obstacle using a preset filtering algorithm with the motion information of the target obstacle as a state variable, and the observed motion information as an observed amount.

14 . The apparatus according to claim 13 , wherein before the determining observed motion information of the target obstacle in accordance with a kinematic rule or a statistical rule based on the determined M types of motion information and historical motion information of the target obstacle, the operations further comprise:

determining whether the determined M types of motion information are ambiguous based on the determined M types of motion information and the historical motion information of the target obstacle; and

the determining observed motion information of the target obstacle in accordance with a kinematic rule or a statistical rule based on the determined M types of motion information and historical motion information of the target obstacle comprises:

determining the observed motion information of the target obstacle in accordance with the kinematic rule or the statistical rule based on the determined M types of motion information and the historical motion information of the target obstacle in response to determining the determined M types of motion information being not ambiguous.

15 . The apparatus according to claim 14 , wherein the determining whether the determined M types of motion information are ambiguous based on the determined M types of motion information and the historical motion information of the target obstacle comprises:

determining, for each type of motion information in the determined M types of motion information, a residual vector between the motion information and motion information of the target obstacle in a last cycle;

determining a residual vector with a minimum modulus in the M residual vectors obtained through calculation as a first minimum residual vector;

determining the determined M types of motion information being not ambiguous in response to the modulus of the first minimum residual vector being less than a first preset modulus threshold; and

determining the determined M types of motion information being ambiguous in response to the modulus of the first minimum residual vector being greater than or equal to the first preset modulus threshold.

16 . The apparatus according to claim 14 , wherein the determining whether the determined M types of motion information are ambiguous based on the determined M types of motion information and the historical motion information of the target obstacle comprises:

determining, for each type of motion information in the determined M types of motion information, a residual vector between the motion information and motion information of the target obstacle in a last cycle;

calculating an average vector of the determined M residual vectors;

determining a residual vector with a minimum modulus of a vector difference from the average vector obtained through calculation in the determined M residual vectors as a second minimum residual vector;

determining the determined M types of motion information being not ambiguous in response to the modulus of the second minimum residual vector being less than a second preset modulus threshold; and

determining the determined M types of motion information being ambiguous in response to the modulus of the second minimum residual vector being greater than or equal to the second preset modulus threshold.

17 . The apparatus according to claim 15 , wherein the determining, for each type of motion information in the determined M types of motion information, a residual vector between the motion information and motion information of the target obstacle in a last cycle comprises:

determining, for each type of motion information in the determined M types of motion information, a differential vector between the motion information and the motion information of the target obstacle in the last cycle as the residual vector between the motion information and the motion information of the target obstacle in the last cycle.

18 . The apparatus according to claim 15 , wherein the determining, for each type of motion information in the determined M types of motion information, a residual vector between the motion information and motion information of the target obstacle in a last cycle comprises:

executing for each type of motion information in the determined M types of motion information: generating estimated motion information of the target obstacle using the preset filtering algorithm with the motion information of the target obstacle as a state variable, and the motion information as an observed amount; and determining a differential vector between the generated estimated motion information and the motion information of the target obstacle in the last cycle as the residual vector between the motion information and the motion information of the target obstacle in the last cycle.

19 . The apparatus according to claim 14 , wherein the operations further comprise:

calculating a second observed displacement of the target obstacle corresponding to a second observed displacement amount in each of N second observed displacement amounts based on the obstacle point cloud in the current frame and the obstacle point cloud in the reference frame in response to determining the determined M types of motion information being ambiguous, wherein the calculation amount of the second observed displacement amount in the each of the N second observed displacement amounts is greater than the calculation amount of the first observed displacement amount in the each of the M first observed displacement amounts;

determining motion information of the target obstacle corresponding to the second observed displacement amount in the each of the N second observed displacement amounts based on N second observed displacements obtained through calculation and the sampling period of the lidar;

determining the observed motion information of the target obstacle in accordance with the kinematic rule or the statistical rule based on the determined N types of motion information, the determined M types of motion information and the historical motion information of the target obstacle.

20 . The apparatus according to claim 19 , wherein before the generating current motion information of the target obstacle using a preset filtering algorithm with the motion information of the target obstacle as a state variable, and the observed motion information as an observed amount, the operations further comprise:

determining whether the modulus of a residual vector between the observed motion information and the motion information of the target obstacle in the last cycle is greater than a third preset modulus threshold; and

updating the observed motion information using motion information obtained through multiplying the observed motion information by a first ratio in response to determining the modulus of the residual vector between the observed motion information and the motion information of the target obstacle in the last cycle being greater than the third preset modulus threshold, wherein the first ratio is obtained through dividing the third preset modulus threshold by the modulus of the residual vector between the observed motion information and the motion information of the target obstacle in the last cycle.

21 . The apparatus according to claim 20 , wherein the generating current motion information of the target obstacle using a preset filtering algorithm with the motion information of the target obstacle as a state variable, and the observed motion information as an observed amount comprises:

adjusting a filtering parameter in the preset filtering algorithm based on a similarity between the obstacle point cloud in the current frame and the obstacle point cloud in the reference frame; and

generating current motion information of the target obstacle using the preset filtering algorithm with the adjusted filtering parameter with the motion information of the target obstacle as a state variable, and the observed motion information as an observed amount.

22 . The apparatus according to claim 21 , wherein the motion information comprises at least one of: speed information, or acceleration information.

23 . The apparatus according to claim 22 , wherein the M first observed displacement amounts comprise at least one of: an observed center displacement amount, an observed gravity center displacement amount, an observed edge center displacement amount, or an observed corner displacement amount.

24 . The apparatus according to claim 23 , wherein the N second observed displacement amounts comprise an observed surface displacement amount.

25 . A non-transitory computer-readable storage medium storing a computer program, the computer program when executed by one or more processors, causes the one or more processors to perform operations, the operations comprising:

acquiring an obstacle point cloud in a current frame and the obstacle point cloud in a reference frame characterizing a target obstacle with to-be-generated motion information, wherein the obstacle point cloud in the current frame is obtained based on a current laser point cloud frame collected by the lidar, and the obstacle point cloud in the reference frame is obtained based on a laser point cloud characterizing the target obstacle in a preset number of laser point cloud frames prior to the current laser point cloud frame collected by the lidar;

calculating a first observed displacement of the target obstacle corresponding to a first observed displacement amount in each of M first observed displacement amounts based on the obstacle point cloud in the current frame and the obstacle point cloud in the reference frame;

determining motion information of the target obstacle corresponding to the first observed displacement amount in the each of the M first observed displacement amounts based on M first observed displacements obtained through calculation and a sampling period of the lidar;

determining observed motion information of the target obstacle in accordance with a kinematic rule or a statistical rule based on the determined M types of motion information and historical motion information of the target obstacle; and

generating current motion information of the target obstacle using a preset filtering algorithm with the motion information of the target obstacle as a state variable, and the observed motion information as an observed amount.