Lidar detection device of detecting close-distance obstacle and method thereof

• US 10,634,793 B1
• Filed: 12/24/2018
• Issued: 04/28/2020
• Est. Priority Date: 12/24/2018
• Status: Active Grant

• Current Assignee: Automotive Research & Testing Center
• Original Assignee: Automotive Research & Testing Center
• Inventors: Siao, Siang-Min, Chen, Ying-Ren, Hsu, Li-You
• Primary Examiner(s): Patel, Shardul D
• Application Number:
  US16/231,789
• Time in Patent Office: 491 Days
• Field of Search: 701 41, 701 42, 701301, 340439, 342109, 342450, 348118, 250330, 2502081
• CPC Class Codes: G01S 17/42 Simultaneous measurement of distance and other co-ordinates indirect measurement G01S17/46
• CPC Class Codes: G01S 17/58 Velocity or trajectory determination systems; Sense-of-movement determination systems
• CPC Class Codes: G01S 17/87 Combinations of systems using electromagnetic waves other than radio waves
• CPC Class Codes: G01S 17/931 of land vehicles
• CPC Class Codes: G01S 2013/9323 Alternative operation using light waves
• CPC Class Codes: G01S 2013/93271 in the front of the vehicles
• CPC Class Codes: G01S 2013/93272 in the back of the vehicles
• CPC Class Codes: G01S 7/4808 Evaluating distance, position or velocity data
• CPC Class Codes: G06F 18/25 Fusion techniques
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30261 Obstacle
• CPC Class Codes: G06T 5/70 Denoising; Smoothing
• CPC Class Codes: G06T 7/277 involving stochastic approaches, e.g. using Kalman filters
• CPC Class Codes: G06V 10/30 Noise filtering
• CPC Class Codes: G06V 10/80 Fusion, i.e. combining data from various sources at the sensor level, preprocessing level, feature extraction level or classification level multimodal speaker identification or verification G10L17/10
• CPC Class Codes: G06V 20/58 Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• CPC Class Codes: G08G 1/165 for passive traffic, e.g. including static obstacles, trees
• CPC Class Codes: G08G 1/166 for active traffic, e.g. moving vehicles, pedestrians, bikes

Abstract

The lidar detection device uses four two-dimensional lidars to scan an obstacle, to obtain original point-cloud data corresponding to the obstacle, and the original point-cloud data includes a relative distance, a relative angle and a relative speed of the obstacle relative to the vehicle, Next, the point-cloud data is classified into at least one point-cloud group corresponding to the obstacle, and a border length of the obstacle is obtained according to a contour of the point-cloud group. Kalman filter and extrapolation are used to estimate and track a movement path of a dynamic obstacle, and transmit the relative speed and the border length of the dynamic obstacle to an automatic driving controlling device. According to the relative distance, a coordinate of the dynamic obstacle nearest the vehicle can be obtained and transmitted to the automatic driving controlling device efficiently.

Claims

1. A lidar detection device of detecting close-distance obstacle, wherein the lidar detection device is disposed on a vehicle, at least one obstacle exists around the vehicle, and the lidar detection device comprises:

four two-dimensional lidars, wherein two of the four two-dimensional lidars are disposed on a front end of the vehicle, the others of four two-dimensional lidars are disposed on a rear end of the vehicle, and the four two-dimensional lidars are configured to scan the at least one obstacle to obtain original point-cloud data corresponding to the at least one obstacle, and the original point-cloud data comprises a relative distance, a relative angle and a relative speed of the at least one obstacle relative to the vehicle;

a noise filter electrically connected to the four two-dimensional lidars, and configured to receive the original point-cloud data and filter out noise of the original point-cloud data, to generate filtered point-cloud data; and

a processor electrically connected to the noise filter and an automatic driving controlling device disposed in the vehicle, and configured to receive the filtered point-cloud data, wherein the processor classifies the filtered point-cloud data, by a preset length, into at least one point-cloud group corresponding to the at least one obstacle, and obtains a border length of the at least one obstacle according to a contour of the at least one point-cloud group, and when the relative speed of the at least one obstacle is changed during an preset time interval, the processor determines that the at least one obstacle is a dynamic obstacle, and the processor uses Kalman filter and extrapolation to estimate and track a movement path of the dynamic obstacle, and transmits the relative speed and the border length of the dynamic obstacle to the automatic driving controlling device, and the processor obtains, according to the relative distance of the dynamic obstacle, a coordinate of the dynamic obstacle nearest the vehicle, and transmits the coordinate to the automatic driving controlling device.

2. The lidar detection device according to claim 1, wherein the noise filter is a linear filter or a nonlinear filter.

3. The lidar detection device according to claim 2, wherein the linear filter is a Gaussian filter.

4. The lidar detection device according to claim 2, wherein the nonlinear filter is a median filter.

5. The lidar detection device according to claim 1, wherein when the relative speed is not changed during the preset time interval, the processor determines that the at least one obstacle is a static obstacle, and the processor transmits the relative distance of the static obstacle nearest the vehicle to the automatic driving controlling device.

6. The lidar detection device according to claim 1, wherein the extrapolation is Lagrange polynomial interpolation and least squares.

7. The lidar detection device according to claim 1, wherein a distance from the at least one obstacle to the vehicle is below 20 m and above 0.3 m.

8. The lidar detection device according to claim 1, wherein the two-dimensional lidars disposed on the front end of the vehicle are at different heights, and the two-dimensional lidars disposed on the rear end of the vehicle are at different heights.

9. The lidar detection device according to claim 8, wherein the two-dimensional lidars disposed on the front end of the vehicle are disposed at heights of 75 cm and 25 cm from the ground, respectively, and the two-dimensional lidars disposed on the rear end of the vehicle are at heights of 75 cm and 25 cm from the ground, respectively.

10. The lidar detection device according to claim 1, wherein the preset length is 50 cm.

11. A lidar detecting method of detecting at least one obstacle around a vehicle, and the lidar detecting method comprise:

using four two-dimensional lidars to scan the at least one obstacle, to obtain original point-cloud data corresponding to the at least one obstacle, wherein the original point-cloud data comprises a relative distance, a relative angle and a relative speed of the at least one obstacle relative to the vehicle;

receiving the original point-cloud data, and filtering out noise of the original point-cloud data, to generate filtered point-cloud data;

receiving the filtered point-cloud data, and classifying, by a preset length, the filtered point-cloud data into at least one point-cloud group corresponding to the at least one obstacle, and obtaining a border length of the at least one obstacle according to a contour of the at least one point-cloud group;

determining whether the relative speed of the at least one obstacle is changed during a preset time interval;

when the relative speed of the at least one obstacle is changed during the preset time interval, determining that the at least one obstacle is a dynamic obstacle, and using Kalman filter and extrapolation to estimate and track a movement path of the dynamic obstacle, and transmitting the relative speed and the border length of the dynamic obstacle to an automatic driving controlling device which is electrically connected to the four two-dimensional lidars, and obtaining, according to the relative distance of the at least one dynamic obstacle, a coordinate of the dynamic obstacle nearest the vehicle, and transmitting the coordinate to the automatic driving controlling device; and

when the relative speed of the at least one obstacle is not changed during the preset time interval, determining that the at least one obstacle is a static obstacle, and transmitting the relative distance of the static obstacle, which is nearest the vehicle, to the automatic driving controlling device.

12. The lidar detecting method according to claim 11, wherein the extrapolation is Lagrange polynomial interpolation and least squares.

13. The lidar detecting method according to claim 11, wherein a distance from the at least one obstacle to the vehicle is below 20 m and higher than above 0.3 m.

14. The lidar detecting method according to claim 11, wherein the preset length is 50 cm.