A controller is provided for operating a system under admissible states. The controller includes an interface configured to connect the system storing a set of measured system states, a set of reference inputs and a set of system parameters in a storage arranged inside or outside the system, a memory storing measured system states, admissible reference inputs and admissible parameter sets and computer-executable programs including a parameter estimator and an adaptive reference governor (ARG), a processor, in connection with the memory. The processor is configured to perform the ARG and the parameter estimator. The parameter estimator extracts a pair of a reference input and the system state and compute a system parameter estimate based on the reference input and system state. The ARG is configured to update the reference input and compute a parameter-robust constraint admissible set based on the updated reference input and the system states, wherein the ARG generates and transmits a reference input to the system based on the parameter-robust constraint admissible set.

Systems and methods for controlling a failover response of an autonomous vehicle are provided. In one example embodiment, a method includes determining, by one or more computing devices on-board an autonomous vehicle, an operational mode of the autonomous vehicle. The autonomous vehicle is configured to operate in at least a first operational mode in which a human driver is present in the autonomous vehicle and a second operational mode in which the human driver is not present in the autonomous vehicle. The method includes detecting a triggering event associated with the autonomous vehicle. The method includes determining actions to be performed by the autonomous vehicle in response to the triggering event based at least in part on the operational mode. The method includes providing one or more control signals to one or more of the systems on-board the autonomous vehicle to perform the one or more actions in response to the triggering event.

Provided are a vehicle control device and a computer program capable of simplifying design of state transition. An intermediate layer constituting an ECU divides a state of a lower-layer state machine for each function of a vehicle system in association with the state of the lower-layer state machine, and outputs the state to an upper-layer state machine, a state transition table of the upper-layer state machine includes, as a condition of state transition of the upper-layer state machine, a current state of a lower-layer state machine or a state to transition, and the upper-layer state machine receives the state of the lower-layer state machine input from the intermediate layer, refers to the state transition table, and outputs a signal for controlling the vehicle system.

A control system (208) for enabling deactivation of vehicle creep in a vehicle (10) with an engine (202), the control system (208) comprising one or more controllers (300), wherein the control system (208) is configured to: enable vehicle creep (402) so that wheel drive torque can reach a first value greater than zero without a driver load request and without a brake request, wherein enabling vehicle creep comprises the engine (202) being active while connected to a vehicle wheel (FL, FR); monitor for a vehicle creep deactivation signal (404); and in response to the vehicle creep deactivation signal (404), inhibit vehicle creep (412) so that wheel drive torque cannot reach the first value without a driver load request, and wherein inhibiting vehicle creep comprises causing disconnection (414) of the engine (202), at least in part, from the vehicle wheel (FL, FR).

A processing system for a motorized mobile system includes at least one sensor to measure one or more kinematic states of an object and at least one processor to use at least one state estimation filter and at least one object kinematic model to predict a first kinematic state estimate of the object and output the first predicted kinematic state estimate for use by at least one other process of the motorized mobile system. The processor uses the first predicted kinematic state estimate and the one or more measured kinematic states to determine a second kinematic state estimate of the object and uses the second kinematic state estimate as an input to the state estimation filter to predict another kinematic state estimate of the object.

An information processing device to be mounted on a vehicle includes a processor. The processor is configured to determine, in response to a request for the vehicle, one mode out of a plurality of modes defining behavior of the vehicle that is related to usage and operation of the vehicle, make transition of a status of the vehicle among a plurality of statuses that is based on a state and a sub-mode, and control the vehicle based on the status of the vehicle that has been achieved by the transition. The state and the sub-mode are permitted in the determined mode.

A state estimation system includes a first processor, and the first processor executes: a first state variable measurement process of measuring a first state variable of a monitored object; a second state variable estimation repetition process of repeating a second state variable estimation process of inputting a measurement value of the first state variable into a learned model and acquiring an estimate value of a second state variable outputted from the learned model; and an estimation accuracy decline information output process of outputting estimation accuracy decline information when a predetermined estimate value sudden change determination condition is met with respect to a first estimate value of the second state variable acquired in response to input of a first measurement value of the first state variable.

A method controls a motor vehicle including a plurality of sensors for acquiring raw data relative to the environment of the vehicle and a computational unit for receiving the raw data acquired by the sensors. The method includes: the computational unit receives the raw data and processes the raw data to deduce therefrom pieces of information relative to the environment of the vehicle and coefficients of probability of error in the deduction of each piece of information, and settings for controlling the vehicle are generated depending on the pieces of information and the probability coefficients. For at least one of the sensors, a quality coefficient relative to the quality of the raw data sent by this sensor is determined, the reliability of the control settings is estimated, and a decision is made to correct or not correct the control settings depending on the estimated reliability of the control settings.

An electric suspension apparatus that is provided in a vehicle includes an electric actuator and a control ECU. The electric actuator is configured to be supplied with a high voltage from a battery. The control ECU is configured to control the battery and the electric actuator. The control ECU includes a stop determination part and an output control part. The stop determination part is configured to determine whether the vehicle is in a stopped state. The output control part is configured to stop output of the high voltage to the electric actuator based on time that elapses after the vehicle stops.

A cooperative adaptive cruise control (CACC) system acquires a driving pattern of a target vehicle and variably provides an inter-vehicle distance and a responsible speed level of a subject vehicle that are followed by the CACC system based on the driving pattern. The CACC system includes a communication unit receiving vehicle information and road information of a region in which the subject vehicle travels; an information collection unit collecting driving information of a forward vehicle, vehicle information of the subject vehicle, and the road information; and a control unit controlling the inter-vehicle distance and the responsible speed level of the CACC system based on the driving pattern of the target vehicle according to generated control information.