One example of a computer-implemented method of adjusting vehicle sensitivity comprises receiving, at a first vehicle, a vehicle profile of a second vehicle. The vehicle profile of the second vehicle correlates measured responses of one or more actuators on the second vehicle to measured user control inputs on the second vehicle. The method further comprises receiving a user control input for the first vehicle; and modifying a response of one or more actuators on the first vehicle to the received user control input based on the received vehicle profile of the second vehicle such that the one or more actuators on the first vehicle mimic the one or more actuators on the second vehicle.

A number of variations may include a method of training a neural network vehicle motion controller that more closely replicates how a human would drive a vehicle using seat of pants vehicle dynamics variables and look ahead parameters in order to determine how a motion controller should direct the steering angle, throttle and break inputs to the vehicle to navigate the vehicle.

A driver assistance system (10) for a vehicle includes a control unit (11), and a communication device (12) for receiving data from a server (30). The control unit (11) is configured to calculate a trajectory (T) for the vehicle on the basis of sensor data. The control unit (11) is also configured to replace the calculated trajectory (T) for the vehicle (20) with an offboard trajectory (T1) received from the server (30) under certain circumstances.

A vehicle control device stores learning data to correct a parameter used in a control program for controlling a vehicle, determines whether or not a part controlled by the parameter has been replaced; and resets the learning data when determining that the part has been replaced. The vehicle control device determines whether or not the part has been replaced by identifying individuality of the part based on characteristics of a sensor output from a sensor provided in the part.

Enclosed are embodiments for sampling driving scenarios for training machine learning models. In an embodiment, a method comprises: assigning, using at least one processor, a set of initial physical states to a set of objects in a map for a set of simulated driving scenarios, wherein the set of initial physical states are assigned according to one or more outputs of a random number generator; generating, using the at least one processor, the set of simulated driving scenarios in the map using the initial physical states of the objects in the set of objects; selecting, using the at least one processor, samples of the simulated driving scenarios; training, using the at least one processor, a machine learning model using the selected samples; and operating, using a control circuit, a vehicle in an environment using the trained machine learning model.

A training method includes: a simulation step outputting, by a simulator, a characteristic variable based on an input parameter set, the parameter set being input to the simulator and indicating that a specific component is presumed to have an abnormality in advance; and a training step updating, by a training device, mapping based on an input training data and an input teaching data, the input variables being input to the training device as the training data, the input variables including the characteristic variable output in the simulation step, the abnormality determination variable being input to the training device as the teaching data, the abnormality determination variable indicating that the specific component has the abnormality.

A control system for a vehicle may include a plurality of vehicle subsystems associated with respective different dynamic vehicle control functions and a plurality of subsystem controllers associated with respective ones of the vehicle subsystems. The subsystem controllers may be configured to control actuators associated with the respective different dynamic vehicle control functions to change an operational state of components of the vehicle subsystems based on respective configuration settings. The system further includes an attribute configuration module operably coupled to the subsystem controllers. The attribute configuration module may include processing circuitry configured to save current configuration settings as a saved configuration responsive to a first actuation of a configuration mode actuator and, after an on/off cycle of the vehicle, instruct the subsystem controllers to implement the saved configuration responsive to a second actuation of the configuration mode actuator.

A method is introduced to personalize a self-driving motor vehicle, which promises to provide an experience as if the self-driving motor vehicle is driven by the mind of a passenger the first time it operates on a freeway.

Data in a pre-dangerous state of a vehicle that is difficult to generate in an actual environment is collected efficiently. There is provided an information processing apparatus including a traveling instructor that instructs virtual traveling while providing traveling parameters to a traveling simulator that simulates traveling of at least one virtual peripheral vehicle and a virtual self-vehicle in a virtual environment, and a detector that detects occurrence of a dangerous state for the self-vehicle in the simulation by the traveling simulator.

High-performance road vehicle having: a plurality of replaceable or removable components; a control unit that supervises the operation of the road vehicle; at least one electronic identification device, which is fitted on a corresponding component, has a memory designed to contain at least one unique identifying code of the component and has a first transmission organ designed to send the data contained in the memory; and a second transmission organ designed to communicate with the first transmission organ and connected to the control unit to allow the control unit to interact with the electronic identification device.