Abstract:
A vehicle controller receives sensor outputs and identifies features in the sensor outputs. The controller determines a trajectory based on the features and generates control signals to vehicle actuators to follow the trajectory. Eccentricity of the control signals is evaluated and if it meets a threshold condition is met an intervention is performed such as discarding or modifying the control signal or initiating a safety procedure. Eccentricity may be determined using an unsupervised machine learning model. The threshold condition may be a dynamic threshold condition such as using the n-sigma approach or the Chebyshev inequality.
Abstract:
Information about a device may be emotively conveyed to a user of the device. Input indicative of an operating state of the device may be received. The input may be transformed into data representing a simulated emotional state. Data representing an avatar that expresses the simulated emotional state may be generated and displayed. A query from the user regarding the simulated emotional state expressed by the avatar may be received. The query may be responded to.
Abstract:
A controller includes a processor programmed to determine, for a vehicle, a first control input based on input data and first reference parameters. The processor is further programmed to operate the vehicle according to the first control input. Based on operating data of the vehicle for an operating condition, the processor determines a second control input for the vehicle. Operating the vehicle according to the second control input reduces a cost of operating the vehicle relative to operating the vehicle according to the first control input. The processor is further programmed to determine, based on the second control input, second reference parameters. The controller generates a third control input based on the second reference parameters and the input data. A cost of operating the vehicle according to the third control input is reduced relative to the cost of operating the vehicle based on the first control input.
Abstract:
The present disclosure relates to a connectivity system with: (a) a vehicle including an accelerator, brakes, steering, a cellular network antenna, a processor, and memory; (b) a connectivity program operatively coupled with the vehicle antenna and configured to: receive a connectivity map, sample connectivity, transmit the samples, anticipate connectivity based on the connectivity map, and generate an instruction for another program based on the anticipated connectivity.
Abstract:
A method according to an exemplary aspect of the present disclosure includes, among other things, scheduling charging of an energy storage device of an electrified vehicle based on a learned key-on pattern. The learned key-on pattern is derived by recursively updating the probability that a subsequent key-on event is likely to occur at any given time and day.
Abstract:
The present disclosure relates to a connectivity system with: (a) a vehicle including an accelerator, brakes, steering, a cellular network antenna, a processor, and memory; (b) a connectivity program operatively coupled with the vehicle antenna and configured to: receive a connectivity map, sample connectivity, transmit the samples, anticipate connectivity based on the connectivity map, and generate an instruction for another program based on the anticipated connectivity.
Abstract:
A user device is identified as being operated by a vehicle occupant. An operation being performed by the user device is identified. A road condition is determined. A vehicle suspension is adjusted based at least in part on the identified user device operation and the road condition.
Abstract:
Methods and systems are presented for improving performance of a vehicle operating in a cruise control mode where a controller adjusts torque output from a vehicle to maintain vehicle speed within a desired range. The methods and systems include adapting a vehicle dynamics model and a vehicle fuel consumption model that provide input to nonlinear model predictive controller.
Abstract:
A vehicle system includes at least one sensor that collects infrastructure information in real time and outputs sensor signals representing the collected infrastructure information. A processing device processes the sensor signals, predicts a future road characteristic based on the infrastructure information, and controls at least one vehicle subsystem in accordance with the predicted future road characteristic. A method includes receiving infrastructure information collected in real time, processing the infrastructure information, predicting a future road characteristic based on the infrastructure information, and controlling at least one vehicle subsystem in accordance with the predicted future road characteristic.
Abstract:
A system for generating power for an electric sub-assembly of a motor vehicle may include at least one reverse electrowetting energy harvesting element coupled to a tire of the motor vehicle. The system may further include at least one controller configured to accumulate electric energy generated by the at least one reverse electrowetting energy harvesting element and supply the accumulated electric energy to at least one sub-assembly mounted on the tire.