An oil condition estimation apparatus to be applied to a vehicle in which oil is agitated by a rotator includes a storage device and an execution device. The storage device stores mapping data for defining mapping. The mapping includes, as input variables, a speed variable indicating a rotation speed of the rotator, and a pressure variable indicating a pressure of the oil, and includes, as an output variable, an air bubble variable related to air bubbles contained in the oil. The execution device executes an acquisition process for acquiring values of the input variables, and a calculation process for calculating a value of the output variable by inputting, to the mapping, the values of the input variables acquired through the acquisition process.

An oil condition estimation apparatus to be applied to a vehicle in which oil is agitated by a rotator includes a storage device and an execution device. The storage device stores mapping data for defining mapping. The mapping includes, as input variables, a speed variable indicating a rotation speed of the rotator, and a pressure variable indicating a pressure of the oil, and includes, as an output variable, an air bubble variable related to air bubbles contained in the oil. The execution device executes an acquisition process for acquiring values of the input variables, and a calculation process for calculating a value of the output variable by inputting, to the mapping, the values of the input variables acquired through the acquisition process.

The disclosure is generally directed to systems and methods for battery life cycle prediction for a preowned electrified vehicle including receiving state of charge (SOC) and mileage data associated with the preowned electrified vehicle, providing one or more driving maneuvers to be performed by a driver, providing one or more instructions to the driver to operate power-driven accessories of the preowned electrified vehicle, collecting data representing battery usage by the driver by monitoring the driving maneuvers and the operation of power-driven accessories as performed by the driver, and responsive to the collected data representing battery usage and the SOC and mileage, providing a battery life prediction for the preowned electrified vehicle.

The disclosure is generally directed to systems and methods for battery life cycle prediction for a preowned electrified vehicle including receiving state of charge (SOC) and mileage data associated with the preowned electrified vehicle, providing one or more driving maneuvers to be performed by a driver, providing one or more instructions to the driver to operate power-driven accessories of the preowned electrified vehicle, collecting data representing battery usage by the driver by monitoring the driving maneuvers and the operation of power-driven accessories as performed by the driver, and responsive to the collected data representing battery usage and the SOC and mileage, providing a battery life prediction for the preowned electrified vehicle.

A method for vehicle analytics includes receiving data from at least one condition indicator sensor of a vehicle and receiving data from at least one usage indicator sensor of the vehicle. The method also includes updating a vehicle specific model corresponding to a vehicle master model, based on the data from the at least one condition indicator sensor and the at least one usage indicator sensor. The method also includes identifying, using the vehicle specific model, at least one usage trend of the vehicle and determining an estimate of a remaining useful life of at least one aspect of the vehicle based on the at least one usage trend of the vehicle.

A method for vehicle analytics includes receiving data from at least one condition indicator sensor of a vehicle and receiving data from at least one usage indicator sensor of the vehicle. The method also includes updating a vehicle specific model corresponding to a vehicle master model, based on the data from the at least one condition indicator sensor and the at least one usage indicator sensor. The method also includes identifying, using the vehicle specific model, at least one usage trend of the vehicle and determining an estimate of a remaining useful life of at least one aspect of the vehicle based on the at least one usage trend of the vehicle.

Systems and methods detecting onboard sensor tampering are disclosed. According to embodiments, data captured by interior sensors within a vehicle may be analyzed to determine an indication that the activity of the vehicle operator either cannot be sufficiently detected or cannot be sufficiently identified using the captured data (e.g., that the captured data may be compromised). A date and time associated with the indication may be recorded, and a vehicle operator associated with the indication may be identified. A possible cause for the compromised data may be diagnosed, and notification may be generated indicating that the activity of the vehicle operator either cannot be sufficiently detected or cannot be sufficiently identified, and/or the possible cause. Additionally, a recommendation for restoring sensor functionality may be generated for the vehicle operator based the possible cause.

Systems and methods detecting onboard sensor tampering are disclosed. According to embodiments, data captured by interior sensors within a vehicle may be analyzed to determine an indication that the activity of the vehicle operator either cannot be sufficiently detected or cannot be sufficiently identified using the captured data (e.g., that the captured data may be compromised). A date and time associated with the indication may be recorded, and a vehicle operator associated with the indication may be identified. A possible cause for the compromised data may be diagnosed, and notification may be generated indicating that the activity of the vehicle operator either cannot be sufficiently detected or cannot be sufficiently identified, and/or the possible cause. Additionally, a recommendation for restoring sensor functionality may be generated for the vehicle operator based the possible cause.

An apparatus includes: a first sensor configured to provide a first input associated with an environment outside a vehicle; a second sensor configured to provide a second input associated with an operation of the vehicle; and a processing unit having a first-stage processing system and a second-stage processing system; the first-stage processing system configured to receive the first input and the second input, process the first input to obtain a first time series of information, and process the second input to obtain a second time series of information; wherein the second-stage processing system comprises a neural network model configured to receive the first time series of information and a second time series of information in parallel; and wherein the neural network is configured to process the first time series and the second time series to determine a probability of a predicted event associated with an operation of the vehicle.

An apparatus includes: a first sensor configured to provide a first input associated with an environment outside a vehicle; a second sensor configured to provide a second input associated with an operation of the vehicle; and a processing unit having a first-stage processing system and a second-stage processing system; the first-stage processing system configured to receive the first input and the second input, process the first input to obtain a first time series of information, and process the second input to obtain a second time series of information; wherein the second-stage processing system comprises a neural network model configured to receive the first time series of information and a second time series of information in parallel; and wherein the neural network is configured to process the first time series and the second time series to determine a probability of a predicted event associated with an operation of the vehicle.