![]() The method of claim 1, wherein determining if the 3D data includes an object comprises: filtering the 3D data to remove noise separating the 3D data into groups with similar characteristics and analyzing, using machine learning, the groups to determine an object detection, wherein the method further comprises: determining a level of confidence of the object detection.ġ1. ![]() ![]() The method of claim 1, wherein determining if the 3D data includes an object comprises: analyzing the 3D data for errors and blockage signatures if errors or blockage signatures are detected, notifying a diagnostic system to determine a cause of the errors or blockage signatures and if errors or blockage signatures are not detected, analyzing the 3D data to determine if the 3D data includes an object.ġ0. The method of claim 7, wherein the analyzing, using machine learning, the camera image comprises: analyzing the camera image using a neural network that is trained to differentiate bright flashing lights from other bright lights.ĩ. The method of claim 1, wherein determining if the camera image includes a flashing light comprises: analyzing, using machine learning, the camera image and predicting a flashing light detection based on results of the analyzing, and wherein the method further comprises: determining a level of confidence of the flashing light detection.Ĩ. The method of claim 1, wherein determining if the camera image includes a flashing light comprises: filtering the camera image to remove low intensity light signals, resulting in a high intensity image comparing the high intensity image to a reference image and determining a flashing light detection based on results of the comparing.ħ. The method of claim 1, wherein determining if the camera image includes a flashing light comprises: analyzing the camera image for errors and blockage signatures if errors or blockage signatures are detected, notifying a diagnostic system to determine a cause of the errors or blockage signatures and if errors or blockage signatures are not detected, analyzing the camera image to determine if the camera image includes a flashing light.Ħ. The method of claim 1, wherein determining if the ambient sound includes a siren sound comprises: converting the ambient sound to digital signals analyzing, using machine learning, the digital signals and determining a siren sound detection based on results of the analyzing, wherein the method further comprises: determining a level of confidence of the siren sound detection.ĥ. The method of claim 1, wherein determining if the ambient sound includes a siren sound comprises: converting the ambient sound to digital signals filtering noise from the digital signals comparing the digital signals to reference signals and determining a siren sound detection based on results of the comparing.Ĥ. The method of claim 1, wherein determining if the ambient sound includes a siren sound comprises: converting the ambient sound to digital signals analyzing the digital signals for errors and blockage signatures if errors or blockage signatures are detected, notifying a diagnostic system to determine a cause of the errors or blockage signatures and if errors or blockage signatures are not detected, analyzing the digital signals for siren sounds.ģ. A method comprising: receiving, with at least one processor, ambient sound determining, with the at least one processor, if the ambient sound includes a siren sound receiving, with the at least one processor, a camera image determining, with the at least one processor, if the camera image includes a flashing light receiving, with the at least one processor, three-dimensional (3D) data determining, with the at least one processor, if the 3D data includes an object determining, with the at least one processor, a presence of an emergency vehicle based on at least two of the siren sound, the flashing light, or the object and initiating, with the at least one processor, an action related to the emergency vehicle based on the determined presence of the emergency vehicle.Ģ.
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