A processing system for a motorized mobile system includes at least one sensor to measure one or more kinematic states of an object proximate to the motorized mobile system and at least one processor to use at least one state estimation filter and at least one object kinematic model. The processor uses the at least one state estimation filter and the at least one object kinematic model to predict a first kinematic state estimate of the object based on a prior knowledge of state for the object and output the predicted first kinematic state estimate of the object from the state estimation filter for use by at least one other process of the motorized mobile system, wherein the at least one other process causes one or more actions to be taken by the motorized mobile system based on the predicted first kinematic state estimate of the object. The processor further uses the at least one state estimation filter and the at least one object kinematic model to receive a measured kinematic state of the object observed by the sensor, use the predicted first kinematic state estimate of the object and the measured kinematic state of the object observed by the sensor to determine a second kinematic state estimate of the object, and use the second kinematic state estimate of the object as an input to the state estimation filter using the object kinematic model to predict another kinematic state estimate of the object.

A function monitor for a system, such as an advanced driver assistance system, comprising a first function monitor element and a second function monitor element, said first function monitor element configured to receive and collate sensor data from a plurality of sensors associated with the system and send a function warning signal to said second function monitor element when said sensor data from one or more of the plurality of sensors is indicative of a functional irregularity.

A control apparatus for controlling a vehicle is provided. The control apparatus includes a travel control unit capable of controlling travel of the vehicle in any of a travel states in accordance with a circumstance of the vehicle and an acquisition unit configured to acquire a movement of a driver of the vehicle. The travel states include a first travel state and a second travel state that is higher in a degree of automation or lower in a driver task than the first travel state. The movement of the driver includes a first movement prohibited in the first travel state and allowed in the second travel state. A condition for determining whether to execute travel control in the second travel state includes an execution state of the first movement or an operation state of the vehicle that relates to the first movement.

A system for controlling a vehicle a sensor to sense measurements indicative of a state of the vehicle and a memory to store a motion model of the vehicle, a measurement model of the vehicle, and a mean and a variance of a probabilistic distribution of a state of calibration of the sensor. The motion model of the vehicle defines the motion of the vehicle from a previous state to a current state subject to disturbance caused by an uncertainty of the state of calibration of the sensor in the motion of the vehicle. The measurement model relates the measurements of the sensor to the state of the vehicle using the state of calibration of the sensor. The system includes a processor to update the probabilistic distribution of the state of calibration based on a function of the sampled states of calibration weighted with weights determined based on a difference between the state of calibration sampled on a feasible space defined by the probabilistic distribution and the corresponding state of calibration estimated based on the measurements using the motion and the measurements models. The system includes a controller to control the vehicle using the measurements of the sensor adapted using the updated probabilistic distribution of the state of calibration of the sensor.

Method and apparatus are disclosed for a speed controller for a vehicle. An example disclosed vehicle includes a power train control unit and an autonomy unit. The example power train control unit controls a speed of the vehicle based on a control signal. The example autonomy unit (a) receives a speed profile based on a preview of traffic information from an profile generator on an external network, and (b) based on vehicle dynamic data and the speed profile, generate the control signal to control the speed of the vehicle according to the speed profile.

A system for controlling a vehicle a sensor to sense measurements indicative of a state of the vehicle and a memory to store a motion model of the vehicle, a measurement model of the vehicle, and a mean and a variance of a probabilistic distribution of a state of calibration of the sensor. The motion model of the vehicle defines the motion of the vehicle from a previous state to a current state subject to disturbance caused by an uncertainty of the state of calibration of the sensor in the motion of the vehicle. The measurement model relates the measurements of the sensor to the state of the vehicle using the state of calibration of the sensor. The system includes a processor to update the probabilistic distribution of the state of calibration based on a function of the sampled states of calibration weighted with weights determined based on a difference between the state of calibration sampled on a feasible space defined by the probabilistic distribution and the corresponding state of calibration estimated based on the measurements using the motion and the measurements models. The system includes a controller to control the vehicle using the measurements of the sensor adapted using the updated probabilistic distribution of the state of calibration of the sensor.

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.

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.

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.

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.