While a vehicle is in an off state, expiration of a timer and receipt of a first message from a user device are monitored. Upon determining that at least one of the timer has expired or that the first message is received from the user device, a communication network onboard the vehicle is then monitored for a specified set of fault conditions. Upon detecting, on the onboard vehicle communication network, a fault condition included in the set of fault conditions, the fault condition is then identified as one of transient or persistent. A user assist feature of the vehicle is disabled upon identifying that the fault condition is transient and that a second message was received from the user device after the fault condition was detected.

While a vehicle is in an off state, expiration of a timer and receipt of a first message from a user device are monitored. Upon determining that at least one of the timer has expired or that the first message is received from the user device, a communication network onboard the vehicle is then monitored for a specified set of fault conditions. Upon detecting, on the onboard vehicle communication network, a fault condition included in the set of fault conditions, the fault condition is then identified as one of transient or persistent. A user assist feature of the vehicle is disabled upon identifying that the fault condition is transient and that a second message was received from the user device after the fault condition was detected.

A vehicle recommendation device and a method for machine learning based recommendation of vehicle is provided. The vehicle recommendation device receives vehicle log data from a plurality of sensors associated with each of a first set of vehicles. The first set of vehicles are classified based on a set of vehicle types. The vehicle recommendation device generates vehicle trip data, associated with a plurality of trips of each of the first set of vehicles, based on the received vehicle log data. The vehicle recommendation device further determines a set of features, associated with the plurality of trips or with information about the first set of vehicles, based on the generated vehicle trip data. The vehicle recommendation device further generates a machine learning model which is trained based on the determined set of features to output a first type of vehicle from the set of vehicle types.

A vehicle recommendation device and a method for machine learning based recommendation of vehicle is provided. The vehicle recommendation device receives vehicle log data from a plurality of sensors associated with each of a first set of vehicles. The first set of vehicles are classified based on a set of vehicle types. The vehicle recommendation device generates vehicle trip data, associated with a plurality of trips of each of the first set of vehicles, based on the received vehicle log data. The vehicle recommendation device further determines a set of features, associated with the plurality of trips or with information about the first set of vehicles, based on the generated vehicle trip data. The vehicle recommendation device further generates a machine learning model which is trained based on the determined set of features to output a first type of vehicle from the set of vehicle types.

A system and method for power management of hybrid electric vehicles is provided. In some implementations, a plug-in series hybrid electric vehicle may include an engine, a motor/generator (MG), a traction motor, an energy storage device, and a controller. The controller is coupled to the engine and the MG to control operation of the engine and the MG such that a state-of-charge (SOC) of the energy storage device tracks a dynamic reference SOC profile during a trip and an average engine power (AEP) is maintained above a threshold. In some instances, maintaining AEP above a threshold supports emission control of the vehicle.

A system and method for power management of hybrid electric vehicles is provided. In some implementations, a plug-in series hybrid electric vehicle may include an engine, a motor/generator (MG), a traction motor, an energy storage device, and a controller. The controller is coupled to the engine and the MG to control operation of the engine and the MG such that a state-of-charge (SOC) of the energy storage device tracks a dynamic reference SOC profile during a trip and an average engine power (AEP) is maintained above a threshold. In some instances, maintaining AEP above a threshold supports emission control of the vehicle.

Aspects of the disclosure relate to generating assets for a simulation in order to test autonomous vehicle control software. For instance, data identifying a vehicle object, a polygon for the vehicle object, and a polygon for an open-door object associated with the vehicle object may be received. A rectangle may be fit to the polygon for the vehicle object. A polygon for the vehicle object without the open-door object may be determined based on the rectangle. A vehicle asset representing a vehicle may be adjusted based on the polygon for the vehicle object without the open-door object. A position and rotation of the rectangle may be used to position an open-door asset representing an open door for the simulation. The open-door asset is adjusted based on the adjustment to the vehicle asset. The simulation may be run with the positioned and adjusted open door asset in order to test the autonomous vehicle control software.

Aspects of the disclosure relate to generating assets for a simulation in order to test autonomous vehicle control software. For instance, data identifying a vehicle object, a polygon for the vehicle object, and a polygon for an open-door object associated with the vehicle object may be received. A rectangle may be fit to the polygon for the vehicle object. A polygon for the vehicle object without the open-door object may be determined based on the rectangle. A vehicle asset representing a vehicle may be adjusted based on the polygon for the vehicle object without the open-door object. A position and rotation of the rectangle may be used to position an open-door asset representing an open door for the simulation. The open-door asset is adjusted based on the adjustment to the vehicle asset. The simulation may be run with the positioned and adjusted open door asset in order to test the autonomous vehicle control software.

Methods and systems are provided for diagnostics and/or prognostics of a vehicle center bearing with integrated sensors. A method includes generating a degradation analysis of the center bearing from real-time vehicle operating data, the data including feedback from one or more of a temperature sensor, a displacement sensor, and an accelerometer wired to a battery integrally arranged in a bearing compartment of a vehicle.

Methods and systems are provided for diagnostics and/or prognostics of a vehicle center bearing with integrated sensors. A method includes generating a degradation analysis of the center bearing from real-time vehicle operating data, the data including feedback from one or more of a temperature sensor, a displacement sensor, and an accelerometer wired to a battery integrally arranged in a bearing compartment of a vehicle.