Apparatus and methods providing a map layer noise levels
An apparatus, method and computer program product are provided for providing a map layer of noise levels. In one example, the apparatus receives input data including an image and location data indicating an area in which the image was captured. The apparatus causes a machine learning model to generate a datapoint in a map layer as a function of the input data. The datapoint indicate a predicted decibel level at the area during an instance in which the image was captured. The machine learning model is trained to generate the datapoint as a function of the input data based on training data, where the training data include images and audio data indicating decibel levels of areas in which the images were captured during instances in which the images were captured.
1. A non-transitory computer-readable storage medium having computer program code instructions stored therein, the computer program code instructions, when executed by at least one processor, cause the at least one processor to:
receive input data including an target image and location data indicating an area in which the target image was captured;
cause a machine learning model to generate a datapoint in a map layer as a function of the input data, wherein the datapoint indicates a predicted decibel level at the area during an instance in which the target image was captured, wherein the machine learning model is trained to generate the datapoint as a function of the input data based on training data, and wherein the training data include images and audio data indicating decibel levels of areas in which the images were captured during instances in which the images were captured;
cause the machine learning model to generate the datapoint by identifying one or more objects within the target image and associating the one or more objects identified in the target image to the predicted decibel level;
compare the predicted decibel level to a threshold decibel level;
based on the area and the comparison of the predicted decibel level to the threshold decibel level, generate a route and vehicle maneuver commands for traversing the route; and
execute the vehicle maneuver commands to cause a vehicle to traverse at least a portion of the route.
2. The non-transitory computer-readable storage medium of claim 1 , wherein the computer program code instructions, when executed by the at least one processor, cause the at least one processor to cause a notification indicating the map layer on a user interface.
3. The non-transitory computer-readable storage medium of claim 1 , wherein the training data further indicate widths of road links within the areas, and wherein the input data indicate a width of one or more road links within the area.
4. The non-transitory computer-readable storage medium of claim 1 , wherein the training data further indicate a number of vehicles within the areas during the instances, and wherein the input data indicate a number of vehicles within the area during the instance.
5. The non-transitory computer-readable storage medium of claim 1 , wherein the training data further indicate historical traffic congestion levels within the areas during the instances, and wherein the input data indicate a historical traffic congestion level within the area during the instance.
6. The non-transitory computer-readable storage medium of claim 1 , wherein the training data further indicate types of points-of-interests (POIs) within the areas, and wherein the input data indicate one or more types of POIs within the area.
7. A method of providing a map layer of noise levels, the method comprising:
receiving input data including a target image and location data indicating an area in which the target image was captured;
causing a machine learning model to generate a datapoint in the map layer as a function of the input data, wherein the datapoint indicates a predicted decibel level at the area during an instance in which the target image was captured, wherein the machine learning model is trained to generate the datapoint as a function of the input data based on training data, and wherein the training data include images and audio data indicating decibel levels of instances areas in which the images were captured during instances in which the images were captured;
causing the machine learning model to generate the datapoint by identifying one or more objects within the target image and associating the one or more objects identified in the target image to the predicted decibel level;
comparing the predicted decibel level to a threshold decibel level;
based on the area and the comparison of the predicted decibel level to the threshold decibel level, generating a route and vehicle maneuver commands for traversing the route; and
executing the vehicle maneuver commands by the at least one processor to cause a vehicle to traverse at least a portion of the route.
8. The method of claim 7 , wherein the training data further indicate widths of road links within the areas, and wherein the input data indicate a width of one or more road links within the area.
9. The method of claim 7 , wherein the training data further indicate a number of vehicles within the areas during the instances, and wherein the input data indicate a number of vehicles within the area during the instance.
10. The method of claim 7 , wherein the training data further indicate historical traffic congestion levels within the areas during the instances, and wherein the input data indicate a historical traffic congestion level within the area during the instance.
11. The method of claim 7 , wherein the training data further indicate types of points-of-interests (POIs) within the areas, and wherein the input data indicate one or more types of POIs within the area.
12. An apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to:
receive input data including a target image and location data indicating an area in which the target image was captured;
cause a machine learning model to generate a datapoint in the map layer as a function of the input data, wherein the datapoint indicates a predicted decibel level at the location during an instance in which the target image was captured, wherein the machine learning model is trained to generate the datapoint as a function of the input data based on training data, and wherein the training data include images and audio data indicating decibel levels of instances areas in which the images were captured during instances in which the images were captured;
cause the machine learning model to generate the datapoint by identifying one or more objects within the target image and associating the one or more objects identified in the target image to the predicted decibel level;
compare the predicted decibel level to a threshold decibel level;
based on the area and the comparison of the predicted decibel level to the threshold decibel level, generate a route and vehicle maneuver commands for traversing the route; and
execute the vehicle maneuver commands by the at least one processor to cause a vehicle to traverse at least a portion of the route.
13. The apparatus of claim 12 , wherein the training data further indicate widths of road links within the areas, and wherein the input data indicate a width of one or more road links within the area.
14. The apparatus of claim 12 , wherein the training data further indicate a number of vehicles within the areas during the instances, and wherein the input data indicate a number of vehicles within the area during the instance.
15. The apparatus of claim 12 , wherein the training data further indicate historical traffic congestion levels within the areas during the instances, and wherein the input data indicate a historical traffic congestion level within the area during the instance.