Abstract: A vehicle device may receive audio data and other vehicle data associated with a vehicle and may transform the audio data to transformed audio data in a frequency domain. The vehicle device may segment the transformed audio data into a plurality of audio segments and may process the plurality of audio segments, with different feature extraction techniques, to extract a plurality of feature vectors. The vehicle device may merge the plurality of feature vectors into a merged feature vector and may create an audio signature for the audio data based on the merged feature vector. The vehicle device may process the audio signature and the other vehicle data, with a model, to determine a classification of the audio signature and may perform one or more actions based on the classification of the audio signature.

Abstract: A vehicle device may receive audio data and other vehicle data associated with a vehicle and may transform the audio data to transformed audio data in a frequency domain. The vehicle device may segment the transformed audio data into a plurality of audio segments and may process the plurality of audio segments, with different feature extraction techniques, to extract a plurality of feature vectors. The vehicle device may merge the plurality of feature vectors into a merged feature vector and may create an audio signature for the audio data based on the merged feature vector. The vehicle device may process the audio signature and the other vehicle data, with a model, to determine a classification of the audio signature and may perform one or more actions based on the classification of the audio signature.

Abstract: A vehicle device may receive audio data and other vehicle data associated with a vehicle and may transform the audio data to transformed audio data in a frequency domain. The vehicle device may segment the transformed audio data into a plurality of audio segments and may process the plurality of audio segments, with different feature extraction techniques, to extract a plurality of feature vectors. The vehicle device may merge the plurality of feature vectors into a merged feature vector and may create an audio signature for the audio data based on the merged feature vector. The vehicle device may process the audio signature and the other vehicle data, with a model, to determine a classification of the audio signature and may perform one or more actions based on the classification of the audio signature.

Abstract: A device can receive fuel information associated with a vehicle and location information associated with the vehicle. The device can determine a home location associated with a user of a client device and a destination location associated with the user. The device can determine an estimated fuel usage, of the vehicle, associated with a route between the home location and the destination location. The device can determine an estimated quantity of trips between the home location and the destination location. The device can generate refueling information, associated with the vehicle, wherein the refueling information includes information identifying at least one of: the estimated quantity of trips between the home location and the destination location, a recommended date and time to refuel the vehicle, or a recommended refueling location. The device can transmit the refueling information to the client device.

Abstract: An in-vehicle device may receive an alert that includes first vehicle identifying a first vehicle and first location data identifying a first location, and may determine whether a second location of the in-vehicle device is in a geographic area associated with the first location. The in-vehicle device may cause, when the second location is determined to be in the geographic area associated with the first location, images or video to be captured of second vehicles, and may process the captured images or video, with a machine learning model, to determine second vehicle data in real time relative to capturing the images or video. The in-vehicle device may determine whether the second vehicle data substantially includes the first vehicle data, and may provide a notification to a device when the second vehicle data substantially includes the first vehicle data.

Abstract: A vehicle device may receive audio data and other vehicle data associated with a vehicle and may transform the audio data to transformed audio data in a frequency domain. The vehicle device may segment the transformed audio data into a plurality of audio segments and may process the plurality of audio segments, with different feature extraction techniques, to extract a plurality of feature vectors. The vehicle device may merge the plurality of feature vectors into a merged feature vector and may create an audio signature for the audio data based on the merged feature vector. The vehicle device may process the audio signature and the other vehicle data, with a model, to determine a classification of the audio signature and may perform one or more actions based on the classification of the audio signature.

Abstract: In some implementations, a device may receive historical telematics data associated with a vehicle. The device may determine a set of base line driving parameters based on the historical telematics data. The device may receive current telematics data associated with the vehicle. The device may determine a set of current driving parameters based on the current telematics data. The device may determine, based on processing the set of base line driving parameters and the set of current driving parameters with a machine learning model, a type of driving behavior of a driver of the vehicle. The device may determine, based on processing the set of base line driving parameters and the set of current driving parameters with another machine learning model, a severity score associated with the type of driving behavior. The device may perform an action based on the type of driving behavior and the severity score.

Abstract: A device can receive sensor data from a plurality of sensor devices. The sensor data can include information relating to vibrations detected by the plurality of sensor devices. The device can determine, based on the information relating to vibrations, whether the vibrations are likely to be associated with one or more primary waves of a seismic event. The device can predict, based on determining that the vibrations are likely to be associated with the one or more primary waves, a geographic area that is likely to be impacted by one or more secondary waves of the seismic event. The device can transmit, based on predicting the geographic area, and to one or more of a plurality of user equipments, one or more of a notification providing information relating to the seismic event or instructions to perform one or more actions.

Abstract: An in-vehicle device may receive an alert that includes first vehicle identifying a first vehicle and first location data identifying a first location, and may determine whether a second location of the in-vehicle device is in a geographic area associated with the first location. The in-vehicle device may cause, when the second location is determined to be in the geographic area associated with the first location, images or video to be captured of second vehicles, and may process the captured images or video, with a machine learning model, to determine second vehicle data in real time relative to capturing the images or video. The in-vehicle device may determine whether the second vehicle data substantially includes the first vehicle data, and may provide a notification to a device when the second vehicle data substantially includes the first vehicle data.

Abstract: A device can receive sensor data from a plurality of sensor devices. The sensor data can include information relating to vibrations detected by the plurality of sensor devices. The device can determine, based on the information relating to vibrations, whether the vibrations are likely to be associated with one or more primary waves of a seismic event. The device can predict, based on determining that the vibrations are likely to be associated with the one or more primary waves, a geographic area that is likely to be impacted by one or more secondary waves of the seismic event. The device can transmit, based on predicting the geographic area, and to one or more of a plurality of user equipments, one or more of a notification providing information relating to the seismic event or instructions to perform one or more actions.

Abstract: A device can receive fuel information associated with a vehicle and location information associated with the vehicle. The device can determine a home location associated with a user of a client device and a destination location associated with the user. The device can determine an estimated fuel usage, of the vehicle, associated with a route between the home location and the destination location. The device can determine an estimated quantity of trips between the home location and the destination location. The device can generate refueling information, associated with the vehicle, wherein the refueling information includes information identifying at least one of: the estimated quantity of trips between the home location and the destination location, a recommended date and time to refuel the vehicle, or a recommended refueling location. The device can transmit the refueling information to the client device.

Abstract: A network device receives a vehicle coordinate report from a vehicle transiting a toll-way, wherein the report includes current geographic coordinates of the vehicle. The network device determines a distance to a current toll station (DTT) of the toll-way based on the current geographic coordinates of the vehicle, and determines a cumulative distance (CD) traveled by the vehicle on the toll-way based on the current geographic coordinates of the vehicle. The network device triggers assessment of a toll charge to a customer associated with the vehicle based on the determined distance DTT and the determined cumulative distance CD.