SYSTEMS AND METHODS FOR GENERATING A HEATMAP CORRESPONDING TO AN ENVIRONMENT OF A VEHICLE

1 . A method for detecting a portion of an environment of a vehicle, comprising:

generating, using one or more sensors coupled to a vehicle, environment data from an environment of the vehicle, wherein the environmental data comprises one or more of the following:

ground LiDAR data from the environment;

camera data from the environment; and

path data corresponding to a change in position of one or more other vehicles within the environment;

inputting the environmental data into a machine learning model trained to generate a heatmap; and

using a processor:

based on the environmental data, determining a portion of the environment, wherein the portion of the environment comprises an area having a likelihood, greater than a minimum threshold, of being adjacent to one or more pavement markings; and

generating the heatmap,

wherein the heatmap corresponds to the portion of the environment.

2 . The method of claim 1 , wherein the one or more sensors comprise one or more of the following:

one or more LiDAR systems;

one or more cameras; and

one or more RADAR systems.

3 . The method of claim 1 , wherein the ground LiDAR data comprises a 2-dimensional grouping of data points within the environment.

4 . The method of claim 1 , wherein generating ground LiDAR data comprises:

capturing, using one or more LiDAR systems, 3-dimensional LiDAR data from the environment; and

distilling the 3-dimensional LiDAR data to the ground lidar data.

5 . The method of claim 1 , wherein:

the one or more sensors comprise one or more cameras configured to generate one or more images, and

the camera data comprises one or more images.

6 . The method of claim 1 , wherein:

the processor is configured to run the machine learning model, and

the machine learning model comprises a neural network.

7 . The method of claim 1 , wherein generating path data corresponding to a change in position of the one or more other vehicle in the environment comprises:

using the processor:

identifying, using image recognition, a first position of one of the one or more other vehicles at a first time;

identifying, using image recognition, a second position of the one of the one or more other vehicles at a second time,

wherein the second time is after the first time;

determining a change in position between the first position and the second position; and

generating a visual representation of the change in position.

8 . A system for generating a heatmap, the system comprising:

a vehicle; and

an imaging module, coupled to the vehicle, the imaging module comprising:

one or more cameras, configured to capture an image depicting an environment within view of the one or more cameras; and

a processor, configured to:

generate, using one or more sensors coupled to a vehicle, environment data from an environment of the vehicle, wherein the environmental data comprises one or more of the following:

ground LiDAR data from the environment;

camera data from the environment; and

path data corresponding to a change in position of one or more other vehicles within the environment;

input the environmental data into a machine learning model trained to generate a heatmap;

based on the environmental data, determine a portion of the environment, wherein the portion of the environment comprises an area having a likelihood, greater than a minimum threshold, of being adjacent to one or more pavement markings; and

generate the heatmap, wherein the heatmap corresponds to the portion of the environment.

9 . The system of claim 8 , wherein the one or more sensors comprise one or more of the following:

one or more LiDAR systems;

one or more cameras; and

one or more RADAR systems.

10 . The system of claim 8 , wherein the ground LiDAR data comprises a 2-dimensional grouping of data points within the environment.

11 . The system of claim 8 , wherein generating ground LiDAR data comprises:

capturing, using one or more LiDAR systems, 3-dimensional LiDAR data from the environment; and

distilling the 3-dimensional LiDAR data to the ground lidar data.

12 . The system of claim 8 , wherein:

the one or more sensors comprise one or more cameras configured to generate one or more birds-eye-view images, and

the camera data comprises one or more birds-eye-view images.

13 . The system of claim 8 , wherein:

the processor is configured to run the machine learning model, and

the machine learning model comprises a convolutional neural network.

14 . The system of claim 8 , wherein generating path data corresponding to a change in position of the one or more other vehicle in the environment comprises:

using the processor:

identifying, using image recognition, a first position of one of the one or more other vehicles at a first time;

identifying, using image recognition, a second position of the one of the one or more other vehicles at a second time,

wherein the second time is after the first time;

determining a change in position between the first position and the second position; and

generating a visual representation of the change in position.

15 . A system, comprising:

an imaging device comprising one or more cameras, the imaging device coupled to a vehicle, wherein the one or more cameras are configured to capture an image depicting an environment within view of the one or more cameras; and

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

generate, using one or more sensors coupled to a vehicle, environment data from an environment of the vehicle, wherein the environmental data comprises one or more of the following:

ground LiDAR data from the environment;

camera data from the environment; and

path data corresponding to a change in position of one or more other vehicles within the environment;

input the environmental data into a machine learning model trained to generate a heatmap;

based on the environmental data, determine a portion of the environment, wherein the portion of the environment comprises an area having a likelihood, greater than a minimum threshold, of being adjacent to one or more pavement markings; and

generate the heatmap,

wherein the heatmap corresponds to the portion of the environment.

16 . The system of claim 15 , wherein the one or more sensors comprise one or more of the following:

one or more LiDAR systems;

one or more cameras; and

one or more RADAR systems.

17 . The system of claim 15 , wherein the ground LiDAR data comprises a 2-dimensional grouping of data points within the environment.

18 . The system of claim 15 , wherein, in the generating ground LiDAR data, the programming instructions, when executed by the processor, are further configured to cause the processor to:

capture, using one or more LiDAR systems, 3-dimensional LiDAR data from the environment; and

distill the 3-dimensional LiDAR data to the ground lidar data.

19 . The system of claim 15 , wherein:

the processor is configured to run the machine learning model, and

the machine learning model comprises a convolutional neural network.

20 . The system of claim 15 , wherein, in the generating path data corresponding to a change in position of the one or more other vehicle in the environment, the programming instructions, when executed by the processor, are further configured to cause the processor to:

identify, using image recognition, a first position of one of the one or more other vehicles at a first time;

identify, using image recognition, a second position of the one of the one or more other vehicles at a second time,

wherein the second time is after the first time;

determine a change in position between the first position and the second position; and

generating a visual representation of the change in position; and

generate a visual representation of the change in position.