System and method for detecting occluded objects based on image processing

1. A vehicle comprising:

one or more cameras;

one or more actuator systems; and

a processor operatively coupled to the one or more cameras and the one or more actuator systems, the processor configured to:

identify a shadow in one or more images captured by the one or more cameras;

determine whether the shadow is moving into a path of the vehicle; and

in accordance with a determination that the shadow is moving into a path of the vehicle, reducing a speed of the vehicle using the one or more actuator systems;

wherein identifying the shadow in the one or more images comprises:

segmenting a plurality of pixels of the one or more images into groups based on a darkness of each pixel, wherein pixels within each group have darknesses within a first threshold difference of each other; and

identifying one of the groups having a plurality of dark pixels having a first darkness surrounded by one of the groups having a plurality of light pixels having a second darkness, the first darkness darker than the second darkness by at least a second threshold difference.

2. The vehicle of claim 1 , further comprising a location system and a map interface, wherein the processor is operatively coupled to the location system and the map interface, and the processor is further configured to:

identify a location of the vehicle based on one or more of the location system and the map interface; and

based on a determination that the vehicle location is in a pedestrian zone, enter a shadow detection mode, wherein the shadow detection mode causes the processor to identify the shadow and determine whether the shadow is moving.

3. The vehicle of claim 1 , wherein the processor is further configured to determine that the shadow is a shadow of an occluded object and the occluded object is not included in the one or more images captured by the one or more cameras of the vehicle.

4. The vehicle of claim 1 , wherein the processor is further configured to, in accordance with a determination that the shadow is stationary or moving in a direction away from the direction of vehicle travel, maintain the speed of the vehicle using the one or more actuator systems.

5. The vehicle of claim 1 , further comprising one or more of a LiDAR sensor, an ultrasonic sensor, a radar sensor, and a range sensor, wherein identifying the shadow in the one or more images comprises:

identifying a plurality of pixels of the one or more images illustrating an image of a ground based on data from the one or more of the LiDAR sensor, the ultrasonic sensor, the radar sensor, and the range sensor; and

identifying the shadow within the pixels illustrating the image of the ground.

6. The vehicle of claim 1 , wherein identifying the shadow in the one or more images comprises comparing the shadow to an expected shadow shape.

7. The vehicle of claim 1 , wherein identifying the shadow in the one or more images comprises:

collecting a plurality of example images;

segmenting a plurality of example shadows in the plurality of example images;

training a classifier using the plurality of example images; and

applying the classifier to the one or more images.

8. A method of operating a vehicle in an autonomous driving mode, the method comprising:

capturing one or more images at one or more cameras of the vehicle;

identifying a shadow in the one or more images;

determining whether the shadow is moving into a path of the vehicle;

in accordance with a determination that the shadow is moving into a path of the vehicle, reducing a speed of the vehicle using one or more actuator systems of the vehicle;

segmenting a plurality of pixels of the one or more images into groups based on a darkness of each pixel, wherein pixels within each group have darknesses within a first threshold difference of each other; and

identifying one of the groups having a plurality of dark pixels having a first darkness surrounded by one of the groups having a plurality of light pixels having a second darkness, the first darkness darker than the second darkness by at least a second threshold difference.

9. The method of claim 8 , further comprising:

identifying a location of the vehicle based on one or more of a location system and a map interface of the vehicle; and

based on a determination that the vehicle location is in a pedestrian zone, entering a shadow detection mode, wherein the shadow detection mode causes the processor to identify the shadow and determine whether the shadow is moving.

10. The method of claim 8 , further comprises determining that the shadow is a shadow of an occluded object and the occluded object is not included in the one or more images captured by the one or more cameras of the vehicle.

11. The method of claim 8 , further comprising: in accordance with a determination that the shadow is stationary or moving in a direction away from the direction of vehicle travel, maintaining the speed of the vehicle using the one or more actuator systems.

12. The method of claim 8 , further comprising:

identifying a plurality of pixels of the one or more images illustrating an image of a ground based on data from one or more of a LiDAR sensor, an ultrasonic sensor, a radar sensor, and a range sensor included in the vehicle; and

identifying the shadow within the pixels illustrating the image of the ground.

13. The method of claim 8 , further comprising comparing the shadow to an expected shadow shape.

14. The method of claim 8 , further comprising:

collecting a plurality of example images;

segmenting a plurality of example shadows in the plurality of example images;

training a classifier using the plurality of example images; and applying the classifier to the one or more images.