SYSTEMS AND METHODS FOR DETECTING AND LABELING A COLLIDABILITY OF ONE OR MORE OBSTACLES ALONG TRAJECTORIES OF AUTONOMOUS VEHICLES

1 . A method for detecting and labeling a collidability of obstacles within a vehicle environment, comprising:

generating one or more data points from one or more sensors coupled to a vehicle, wherein:

the one or more sensors comprise:

a Light Detection and Ranging (LiDAR) sensor; and

a camera, and

the one or more data points comprise:

a LiDAR point cloud generated by the LiDAR sensor; and

an image captured by the camera; and

using a processor:

detecting one or more obstacles within the LiDAR point cloud;

performing a factor query on the image for each of the one or more detected obstacles;

for each of the one or more detected obstacles, based on the factor query, determining a label for the obstacle,

wherein the label indicates whether each of the one or more detected obstacles is:

collidable; and

not non-collidable; and

for each of the one or more detected obstacles, labeling the obstacle with the label.

2 . The method of claim 1 , wherein the performing the factor query comprises one or more of:

performing a color query on the image for each of the one or more detected obstacles;

performing a shape query on the image for each of the one or more detected obstacles; and

performing a movement query on the image for each of the one or more detected obstacles.

3 . The method of claim 1 , further comprising:

using the processor:

for each of the one or more detected obstacles, based on the label of the obstacle, determining one or more vehicle actions for the vehicle to perform; and

causing the vehicle to perform the one or more actions.

4 . The method of claim 3 , wherein the one or more actions comprises one or more of:

increasing a speed of the vehicle;

decreasing a speed of the vehicle;

stopping the vehicle; and

adjusting a trajectory of the vehicle.

5 . The method of claim 1 , further comprising:

generating a patch for each of the one or more detected obstacles;

projecting the LiDAR point cloud into the image, wherein:

each patch represents a region of the image for each of the one or more detected obstacles, and

each patch forms a bounding box on the image; and

cropping the region of the image within the bounding box, forming a cropped image.

6 . The method of claim 5 , further comprising resizing the cropped image, forming a resized image,

wherein performing the factor query comprises performing the factor query on the resized image.

7 . The method of claim 1 , further comprising:

for each of the one or more detected obstacles, based on the factor query, determining whether the obstacle is one or more of:

a piece of vegetation;

a pedestrian; and

a vehicle.

8 . A system for detecting and labeling a collidability of obstacles within a vehicle environment, comprising:

a vehicle;

one or more sensors, coupled to the vehicle, configured to generate one or more data points, wherein:

the one or more sensors comprise:

a Light Detection and Ranging (LiDAR) sensor; and

a camera, and

the one or more data points comprise:

a LiDAR point cloud generated by the LiDAR sensor; and

an image captured by the camera; and

a processor configured to:

detect one or more obstacles within the LiDAR point cloud;

perform a factor query on the image for each of the one or more detected obstacles;

for each of the one or more detected obstacles, based on the factor query, determine a label for the obstacle,

wherein the label indicates whether each of the one or more detected obstacles is:

collidable; and

not non-collidable; and

for each of the one or more detected obstacles, label the obstacle with the label.

9 . The system of claim 8 , wherein the performing the factor query comprises one or more of:

performing a color query on the image for each of the one or more detected obstacles;

performing a shape query on the image for each of the one or more detected obstacles; and

performing a movement query on the image for each of the one or more detected obstacles.

10 . The system of claim 8 , wherein the processor is further configured to:

for each of the one or more detected obstacles, based on the label of the obstacle, determine one or more vehicle actions for the vehicle to perform; and

cause the vehicle to perform the one or more actions.

11 . The system of claim 10 , wherein the one or more actions comprises one or more of:

increasing a speed of the vehicle;

decreasing a speed of the vehicle;

stopping the vehicle; and

adjusting a trajectory of the vehicle.

12 . The system of claim 8 , wherein the processor is further configured to:

generate a patch for each of the one or more detected obstacles;

project the LiDAR point cloud into the image,

wherein:

each patch represents a region of the image for each of the one or more detected obstacles, and

each patch forms a bounding box on the image; and

crop the region of the image within the bounding box, forming a cropped image.

13 . The system of claim 12 , wherein:

the processor is further configured to resize the cropped image, forming a resized image, and

the performing the factor query comprises performing the factor query on the resized image.

14 . The system of claim 8 , wherein the processor is further configured to:

for each of the one or more detected obstacles, based on the factor query, determine whether the obstacle is one or more of:

a piece of vegetation;

a pedestrian; and

a vehicle.

15 . A system for detecting and labeling a collidability of obstacles within a vehicle environment, comprising:

a vehicle;

one or more sensors, coupled to the vehicle, configured to generate one or more data points, wherein:

the one or more sensors comprise:

a Light Detection and Ranging (LiDAR) sensor; and

a camera, and

the one or more data points comprise:

a LiDAR point cloud generated by the LiDAR sensor; and

an image captured by the camera; and

a computing device, comprising a processor and a memory, coupled to the vehicle, configured to store programming instructions that, when executed by the processor, cause the processor to:

detect one or more obstacles within the LiDAR point cloud;

perform a factor query on the image for each of the one or more detected obstacles;

for each of the one or more detected obstacles, based on the factor query, determine a label for the obstacle,

wherein the label indicates whether each of the one or more detected obstacles is:

collidable; and

not non-collidable; and

for each of the one or more detected obstacles, label the obstacle with the label.

16 . The system of claim 15 , wherein the performing the factor query comprises one or more of:

performing a color query on the image for each of the one or more detected obstacles;

performing a shape query on the image for each of the one or more detected obstacles; and

performing a movement query on the image for each of the one or more detected obstacles.

17 . The system of claim 15 , wherein the programming instructions are further configured, when executed by the processor, to cause the processor to:

for each of the one or more detected obstacles, based on the label of the obstacle, determine one or more vehicle actions for the vehicle to perform; and

cause the vehicle to perform the one or more actions.

18 . The system of claim 17 , wherein the one or more actions comprises one or more of:

increasing a speed of the vehicle;

decreasing a speed of the vehicle;

stopping the vehicle; and

adjusting a trajectory of the vehicle.

19 . The system of claim 15 , wherein:

the programming instructions are further configured, when executed by the processor, to cause the processor to:

generate a patch for each of the one or more detected obstacles;

project the LiDAR point cloud into the image,

wherein:

each patch represents a region of the image for each of the one or more detected obstacles, and

each patch forms a bounding box on the image;

crop the region of the image within the bounding box, forming a cropped image; and

resize the cropped image, forming a resized image, and

the performing the factor query comprises performing the factor query on the resized image.

20 . The system of claim 15 , wherein the programming instructions are further configured, when executed by the processor, to cause the processor to:

for each of the one or more detected obstacles, based on the factor query, determine whether the obstacle is one or more of:

a piece of vegetation;

a pedestrian; and

a vehicle.