Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: Techniques are described for determining an amount of fuel that is consumed by the body components of a vehicle, based at least partly on sensor data describing the operations of the body components and/or the location of the vehicle. A vehicle is equipped with a body that has any suitable number of body components that perform operations not directly associated with the translational movement of the vehicle from one location to another. Fuel is consumed to provide power (e.g., through power take off) to operate the body components. The vehicle includes sensor device(s) configured to sense the operations of the body components and generate sensor data that describes the operations of the body components. The sensor data is analyzed to determine an amount of fuel that is consumed to power the operations of the body components.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: Techniques are described for determining an amount of fuel that is consumed by the body components of a vehicle, based at least partly on sensor data describing the operations of the body components and/or the location of the vehicle. A vehicle is equipped with a body that has any suitable number of body components that perform operations not directly associated with the translational movement of the vehicle from one location to another. Fuel is consumed to provide power (e.g., through power take off) to operate the body components. The vehicle includes sensor device(s) configured to sense the operations of the body components and generate sensor data that describes the operations of the body components. The sensor data is analyzed to determine an amount of fuel that is consumed to power the operations of the body components.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: Techniques are described for determining an amount of fuel that is consumed by the body components of a vehicle, based at least partly on sensor data describing the operations of the body components and/or the location of the vehicle. A vehicle is equipped with a body that has any suitable number of body components that perform operations not directly associated with the translational movement of the vehicle from one location to another. Fuel is consumed to provide power (e.g., through power take off) to operate the body components. The vehicle includes sensor device(s) configured to sense the operations of the body components and generate sensor data that describes the operations of the body components. The sensor data is analyzed to determine an amount of fuel that is consumed to power the operations of the body components.

Abstract: Techniques are described for determining an amount of fuel that is consumed by the body components of a vehicle, based at least partly on sensor data describing the operations of the body components and/or the location of the vehicle. A vehicle is equipped with a body that has any suitable number of body components that perform operations not directly associated with the translational movement of the vehicle from one location to another. Fuel is consumed to provide power (e.g., through power take off) to operate the body components. The vehicle includes sensor device(s) configured to sense the operations of the body components and generate sensor data that describes the operations of the body components. The sensor data is analyzed to determine an amount of fuel that is consumed to power the operations of the body components.

Abstract: A computer-implemented method for analyzing refuse includes operations of receiving sensor data indicating an operational state of a vehicle body component of a refuse collection vehicle (RCV); analyzing the sensor data to detect a presence of a triggering condition based at least partly on a particular operational state of the vehicle body component, as indicated by the sensor data; in response to detecting the triggering condition, accessing image data indicating a physical state of refuse collected by the RCV; providing the image data as input to at least one contaminant detection model trained, using at least one machine learning (ML) algorithm, to output a classification of the image data, the classification indicating a degree of contamination of the refuse; and storing, in a machine-readable medium, the classification of the image data.

Abstract: Techniques are described for correlating entity identification information with refuse containers being serviced by a refuse collection vehicle (RCV). Location data can be collected by location sensor(s) on the RCV at a time when a triggering condition is present, such as a time when a lift arm is operating to empty a refuse container into the hopper of the RCV. The location data can be provided as input to an algorithm that estimates a container location through a vector offset to account for the distance and direction of the RCV lift arm relative to the location sensor in the RCV. The container location can be correlated with parcel data to determine the parcel that the container was on or near to when it was serviced, and the customer or other entity associated with the parcel can be correlated to the particular container based on the analysis.

Abstract: Techniques are described for determining an amount of fuel that is consumed by the body components of a vehicle, based at least partly on sensor data describing the operations of the body components and/or the location of the vehicle. A vehicle is equipped with a body that has any suitable number of body components that perform operations not directly associated with the translational movement of the vehicle from one location to another. Fuel is consumed to provide power (e.g., through power take off) to operate the body components. The vehicle includes sensor device(s) configured to sense the operations of the body components and generate sensor data that describes the operations of the body components. The sensor data is analyzed to determine an amount of fuel that is consumed to power the operations of the body components.