A control method is provided for controlling a lateral position of a motor vehicle. The control method includes calculating a sighting distance of a detector means embedded in the vehicle, calculating a first component of a steering angle setpoint of a steered wheels of the vehicle, and calculating a second component of the steering angle setpoint. The first component is an open loop component of a control system, while the second component is a closed loop component of the control system. The first component is weighted by a gain that is a decreasing function of the sighting distance.

Systems and methods are disclosed for reducing second order dynamics delays in a control subsystem (e.g. throttle, braking, or steering) in an autonomous driving vehicle (ADV). A control input is received from an ADV perception and planning system. The control input is translated in a control command to a control subsystem of the ADV. A reference actuation output is obtained from a storage of the ADV. The reference actuation output is a smoothed output that accounts for second order actuation dynamic delays attributable to the control subsystem actuator. Based on a difference between the control input and the reference actuation output, adaptive gains are determined and applied to the input control signal to reduce error between the control output and the reference actuation output.

A vehicular arbitration system includes: a main manager configured to receive one or more requests from a plurality of first application execution units and to determine a request for operating a predetermined on-vehicle device based on the received one or more requests and a predetermined rule; and a plurality of sub-managers respectively configured to arbitrate the request determined by the main manager and a request input from at least one second application execution unit that is different from the plurality of first application execution units and to control the on-vehicle device based on an arbitration result.

A control system for a hybrid vehicle which includes an internal combustion engine and an electric motor and whose drive mode is switchable between an electric vehicle mode and a hybrid vehicle mode includes: an on-board learning unit mounted on the hybrid vehicle and configured to perform a learning action; a position determination unit configured to determine whether the hybrid vehicle is located in a low emission area where operation of the internal combustion engine is supposed to be restricted; and a learning control unit configured to at least partially stop the learning action of the on-board learning unit when determination is made that the hybrid vehicle is located in the low emission area.

A vehicle includes a system method of operating the vehicle. The system includes a processor. The processor is configured to determine a machine-selected action for the vehicle in a current state and an actual next state for the vehicle resulting from the machine-selected action, determine, using a user model, a user-expected action for the vehicle in the first current state and a user-expected next state for the vehicle resulting from applying the machine-selected action, determine a gap value based on at least one of the user-expected action, the machine-selected action, the actual next state and the user-expected next state, and output a signal when the gap value meets a threshold.

The present invention relates to a method for estimating an accident risk of an autonomous driving unit. The method produces helpful results with less autonomous driving cycles. An autonomous-driving quantity, quantifying an autonomous-driving quality of the driving of the driving unit, is determined from driving values that have been determined from monitoring at least one driving parameter of the driving unit during autonomous driving. The autonomous-driving quantity is associated with a plurality of manual-driving quantities that have been determined from the same driving parameter during manual driving periods of different driving units. An autonomous-driving accident rate value is determined from accident rate values associated with those manual-driving quantities.

One embodiment describes an automation system including a sensor that determines operational parameters of the automation system; one or more actuators that perform control actions during operation of the automation system; and a control system communicatively coupled to the sensor and the one or more actuators. The control system includes memory that stores the operational parameters; determines occurrence of memory errors in data stored in the memory; determines error parameters that indicate characteristics of the memory errors; determines error-corrected data by correcting the memory errors based at least in part on the error parameters; adaptively adjusts a refresh rate used to refresh stored data in the memory based at least in part on the error parameters; and determines control commands instructing the one or more actuators to perform the control actions by processing the error-corrected data.

Methods and systems for adapting an advanced driver assistance driving system of a vehicle. One system includes an electronic processor of an advanced driver assistance driving system. The electronic processor is configured to control the vehicle using a control parameter of the advanced driver assistance driving system. The electronic processor is also configured to activate a learning mode for the advanced driver assistance driving system and receive feedback associated with the control of the vehicle. The electronic processor is also configured to adjust the control parameter of the advanced driver assistance driving system based on the feedback and control the vehicle using the adjusted control parameter.

An MG1 torque at a time of decreasing an engine speed of an engine is made larger when a turbocharging pressure by a turbocharger is higher than when the turbocharging pressure is lower. In this way, even if the losses of pumps of the engine differ due to the remaining turbocharging pressure during a transition of stopping the engine in turbocharging, it is possible to appropriately reduce the engine speed. Therefore, when the engine is being brought to a stop, it is possible to appropriately suppress vibration generated in the vehicle.

A computer-implemented method for training a machine learning system to generate driving profiles of a vehicle. The method includes first travel routes are selected from a first database having travel routes, a generator of the machine learning system receives the first travel routes and generates first driving profiles for each of the first travel routes, travel routes and associated driving profiles determined during vehicle operation are stored in a second database, second travel routes and respective associated second driving profiles determined during vehicle operation are selected from the second database, a discriminator of the machine learning system receives pairs made up of one of the first travel routes with the respective associated first generated driving profile and pairs made up of second travel routes with the respective associated second driving profile determined during vehicle operation, as input variables.