A temperature estimation system and method for an electric motor of a vehicle include a set of sensors configured to measure a set of operating parameters of the electric motor including at least (i) phase current, (ii) speed, and (iii) coolant temperature and a controller configured to access a trained artificial neural network (ANN) temperature estimation model, using the trained ANN temperature estimation model with the set of electric motor operating parameters as inputs, estimate temperatures of a stator and a rotor of the electric motor, and control operation of the electric motor based on the estimated stator and rotor temperatures.

A temperature estimation system and method for an electric motor of a vehicle include a set of sensors configured to measure a set of operating parameters of the electric motor including at least (i) phase current, (ii) speed, and (iii) coolant temperature and a controller configured to access a trained artificial neural network (ANN) temperature estimation model, using the trained ANN temperature estimation model with the set of electric motor operating parameters as inputs, estimate temperatures of a stator and a rotor of the electric motor, and control operation of the electric motor based on the estimated stator and rotor temperatures.

A reverse direction traveling detection apparatus including a microprocessor. The microprocessor is configured to perform acquiring an actually measured road surface profile of a road surface on which a vehicle is traveling, determining a travel direction of the vehicle based on position information of the vehicle, further determining whether a coincidence degree between the actually measured road surface profile and a first reference road surface profile in a first lane is equal to or greater than a predetermined value when it is determined that the travel direction of the vehicle is a first direction, and determining whether the vehicle travels in reverse direction based on the actually measured road surface profile and a second reference road surface profile in a second lane when it is determined that the coincidence degree is less than the predetermined value.

A system includes a first controller including a processor and a memory, and the memory stores instructions executable by the processor to receive first acceleration data from an accelerometer; upon determining that the first acceleration data satisfies a first criterion, transmit a wake-up instruction to a second controller; upon determining that the first acceleration data fails to satisfy the first criterion, instruct the accelerometer to send second acceleration data; and upon determining that the second acceleration data satisfies a second criterion, transmit the wake-up instruction to the second controller. The first criterion includes jerk exceeding a jerk threshold.

Disclosed are methods, apparatuses, and systems for controlling a valet mode of a vehicle. The method may include determining whether or not one or more vehicle takeover mode entry conditions are satisfied while a vehicle is in the valet mode, requesting one of a plurality of takeover controls for a vehicle takeover mode based on a determination that the one or more vehicle takeover mode entry conditions are satisfied and further based on a status of the vehicle, and performing the requested takeover control on the vehicle by adjusting a limitation that is applied, in the valet mode, to the vehicle.

Disclosed are methods, apparatuses, and systems for controlling a valet mode of a vehicle. The method may include determining whether or not one or more vehicle takeover mode entry conditions are satisfied while a vehicle is in the valet mode, requesting one of a plurality of takeover controls for a vehicle takeover mode based on a determination that the one or more vehicle takeover mode entry conditions are satisfied and further based on a status of the vehicle, and performing the requested takeover control on the vehicle by adjusting a limitation that is applied, in the valet mode, to the vehicle.

A distributed safety lockout system for a vehicle, such as a recreational vehicle (RV), that includes a plurality of networked devices communicatively coupled via a communications network, and that is configured to implement a method for controlling an electromechanical operation of an electromechanical device based on a safety lockout condition determined through use of a consensus protocol in which a safety lockout status is agreed upon by the networked devices. The safety lockout status may be escalated by a safety lockout condition detection device that detects a safety lockout condition, such as movement of the RV. The safety lockout status may be de-escalated through a network contention mode of the consensus protocol in which each of the networked devices agree to de-escalate the safety lockout status.

A method and device parameterize a driving dynamics controller of a vehicle, which intervenes in a controlling manner in a driving dynamics of the vehicle. The driving dynamics controller ascertains an action depending on a vehicle state. The method includes providing a model for predicting a vehicle state. The model configured to predict a subsequent vehicle state depending on the vehicle state and the action. At least one data tuple is ascertained including a sequence of vehicle states and respectively associated actions. The vehicle states are ascertained by the driving dynamics controller using the model depending on an ascertained action. The parameters of the driving dynamics controller are changed/adjusted such that a cost function which ascertains costs of the trajectory depending on the vehicle states and on the ascertained actions of the respectively associated vehicle states and is dependent on the parameters of the driving dynamics controller is minimized.

A method and device parameterize a driving dynamics controller of a vehicle, which intervenes in a controlling manner in a driving dynamics of the vehicle. The driving dynamics controller ascertains an action depending on a vehicle state. The method includes providing a model for predicting a vehicle state. The model configured to predict a subsequent vehicle state depending on the vehicle state and the action. At least one data tuple is ascertained including a sequence of vehicle states and respectively associated actions. The vehicle states are ascertained by the driving dynamics controller using the model depending on an ascertained action. The parameters of the driving dynamics controller are changed/adjusted such that a cost function which ascertains costs of the trajectory depending on the vehicle states and on the ascertained actions of the respectively associated vehicle states and is dependent on the parameters of the driving dynamics controller is minimized.

A vehicle driving assist apparatus of the invention starts a collision avoidance steering assist control to automatically steer the vehicle to avoid a collision of the vehicle with the obstacle in response to a driver performing a collision avoidance steering operation for avoiding the collision when there is a possibility that the vehicle collides with the obstacle. The vehicle driving assist apparatus cancels the collision avoidance steering assist control in response to the driver performing a counter collision avoidance steering operation against automatically steering the vehicle intended to be achieved by the collision avoidance steering assist control after a first predetermined time elapses from starting the collision avoidance steering assist control. The vehicle driving assist apparatus continues the collision avoidance steering assist control until the first predetermined time elapses from starting the collision avoidance steering assist control even when the driver performs the counter collision avoidance steering operation.