Abstract: An automatic speech recognition system for a vehicle includes a controller configured to select an acoustic model from a library of acoustic models based on ambient noise in a cabin of the vehicle and operating parameters of the vehicle. The controller is further configured to apply the selected acoustic model to noisy speech to improve recognition of the speech.

Abstract: A system includes a head and torso simulation (HATS) system configured to play back pre-recorded audio commands while simulating a driver head location as an output location. The system also includes a vehicle speaker system and a processor configured to engage a vehicle heating, ventilation and air-conditioning (HVAC) system. The processor is also configured to play back audio commands through the HATS system while playing back pre-recorded vehicle environment noises through the speaker system. The processor is further configured to determine if the audio command, recorded by a vehicle microphone, is recognizable in the presence of the environment noises and HVAC noises.

Abstract: A controller may be programmed to create a speech utterance set for speech recognition training by, in response to receiving data representing a neutral utterance and parameter values defining signal noise, generating data representing a Lombard effect version of the neutral utterance using a transfer function associated with the parameter values and defining distortion between neutral and Lombard effect versions of a same utterance due to the signal noise.

Abstract: Methods, devices, and systems pertaining to in-vehicle tutorials are described. A method may involve receiving a request for an in-vehicle tutorial of an operational feature of a vehicle from a user and simulating expected driving behavior corresponding to the operational feature in the vehicle. The method may further include monitoring operational behavior of the user, comparing the operational behavior with the expected driving behavior, and providing a feedback to the user based on the comparison.

Abstract: At least one spoken utterance and a stored vehicle acoustic impulse response can be provided to a computing device. The computing device is programmed to provide at least one speech file based at least in part on the spoken utterance and the vehicle acoustic impulse response.

Abstract: This disclosure generally relates to a system, apparatus, and method for achieving an adaptive vehicle state-based hands free noise reduction feature. A noise reduction tool is provided for adaptively applying a noise reduction strategy on a sound input that uses feedback speech quality measures and machine learning to develop future noise reduction strategies, where the noise reduction strategies include analyzing vehicle operational state information and external information that are predicted to contribute to cabin noise and selecting noise reducing pre-filter options based on the analysis.

Abstract: A method or system that receives a product definition that includes a feature family having data defining one or more product features. The product definition including one or more corresponding rules defining one or more relationships between one or more product features. The method or system receiving input selecting one or more feature families of interest. The method or system identifying the one or more rules that provide a relationship connecting the one or more feature families to the selected feature families of interest. The method or system converting the identified rules to one or more positive logic rule groups. The method or system generating one or more global representations of the product definition by interacting the one or more positive logic rule groups to produce a result that defines the relationship between the interacted positive logic rule groups and storing the results that are determined as being valid.

Abstract: One or more embodiments include a computer-implemented method or system for generating part volumes necessary to assemble all vehicles of a vehicle product line for a predetermined time period. The method or system being configured to receive a product definition representing valid configurations for a product. The products may include feature families with mutually exclusive features. The method or system also receives a feature forecast rate or sales forecast rate that may be an aggregated demand. The method or system may further receive a bill of material for the product. The method or system may generate a forecasted order that is a quantity of each configuration by interacting the feature forecast rate and the product definition. The method or system may further generate a part volume necessary to assemble the product by interacting the quantity of each configuration with a product bill of material.

Abstract: A vehicle interface module configured to communicate with a nomadic device and a vehicle. The vehicle interface module comprising a wireless transceiver configured to communicate with a nomadic device and a vehicle transceiver configured to communicate with a vehicle data bus. The vehicle interface module also includes a processor configured to receive a signal from the vehicle data bus using the vehicle transceiver, wherein the signal was initiated by a user input to a vehicle computer system. Furthermore, the processor is also configured to determine that the signal prompts activation of a voice recognition session on the nomadic device, and provide input to the nomadic device using the wireless transceiver, wherein the input initiates a voice recognition session of the nomadic device.

Abstract: A computer-implemented method or system that is capable of configuring a product. The computer-implemented method or system receiving a product configuration matrix from a computer storage medium. The computer-implemented method or system also receiving user input defining a user selection of at least one feature or component of the product. The computer-implemented method or system generating a binary representation of the user selection. The computer-implemented method or system deriving a resolution matrix. The method or system comparing the resolution matrix and the binary representation of the user selection to determine whether one or more of the selected product features or components in the user selection are valid for the product. The computer-implemented method or system also generating a validation result that identifies one or more valid product features or components within the user selection.

Abstract: A method or system that receives a product definition that includes a feature family having data defining one or more product features. The product definition including one or more corresponding rules defining one or more relationships between one or more product features. The method or system receiving input selecting one or more feature families of interest. The method or system identifying the one or more rules that provide a relationship connecting the one or more feature families to the selected feature families of interest. The method or system converting the identified rules to one or more positive logic rule groups.

Abstract: A method or system that receives a product definition that includes a feature family having data defining one or more product features. The product definition including one or more corresponding rules defining one or more relationships between one or more product features. The method or system receiving input selecting one or more feature families of interest. The method or system identifying the one or more rules that provide a relationship connecting the one or more feature families to the selected feature families of interest. The method or system converting the identified rules to one or more positive logic rule groups. The method or system generating one or more global representations of the product definition by interacting the one or more positive logic rule groups to produce a result that defines the relationship between the interacted positive logic rule groups and storing the results that are determined as being valid.

Abstract: A method or system that receives a product definition that includes a feature family having data defining one or more product features. The product definition including one or more corresponding rules defining one or more relationships between one or more product features. The method or system receiving input selecting one or more feature families of interest. The method or system identifying the one or more rules that provide a relationship connecting the one or more feature families to the selected feature families of interest. The method or system converting the identified rules to one or more positive logic rule groups.

Abstract: A computer-implemented method or system that is capable of configuring a product. The computer-implemented method or system receiving a product configuration matrix from a computer storage medium. The computer-implemented method or system also receiving user input defining a user selection of at least one feature or component of the product. The computer-implemented method or system generating a binary representation of the user selection. The computer-implemented method or system deriving a resolution matrix. The method or system comparing the resolution matrix and the binary representation of the user selection to determine whether one or more of the selected product features or components in the user selection are valid for the product. The computer-implemented method or system also generating a validation result that identifies one or more valid product features or components within the user selection.

Abstract: A data store includes, for a dealer of interest, a target inventory mix rate for an item having a feature. A computing device is configured to perform an optimization to obtain a recommended feature allocation of the item including the feature by minimizing a difference between a projected inventory mix rate for the feature and the target inventory mix rate for the feature.

Abstract: A computer-implemented method or system that is capable of configuring a product. The computer-implemented method or system receiving a product configuration matrix from a computer storage medium. The computer-implemented method or system also receiving user input defining a user selection of at least one feature or component of the product. The computer-implemented method or system generating a binary representation of the user selection. The computer-implemented method or system deriving a resolution matrix. The method or system comparing the resolution matrix and the binary representation of the user selection to determine whether one or more of the selected product features or components in the user selection are valid for the product. The computer-implemented method or system also generating a validation result that identifies one or more valid product features or components within the user selection.

Abstract: A method for calculating the probability that one or more automobiles will be sold by a future date includes performing a survival analysis based on historical days-on-lot data for one or more automobiles to generate a survival function. Based on the survival function, a probability that one or more automobiles will be sold by a future date is calculated. Days-on-lot data may include censored and geographic data. The survival analysis may additionally consider automobile content data and calculate sales impact values for various content items. The survival analysis may also consider incentive, automobile pricing, marketing and time-varying data. Data may be encoded into co-variate data for input into the survival analysis.

Abstract: A electronic engine control (EEC) module executes a neural network processing program to control the idle speed of an internal combustion engine by controlling the bypass air (throttle duty cycle) and the engine's ignition timing. The neural network is defined by a unitary data structure which defmes the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. To achieve idle speed control, the neural network processes input signals indicating the current operating state of the engine, including engine speed, the intake mass air flow rate, a desired engine speed, engine temperature, and other variables which influence engine speed, including loads imposed by power steering and air conditioning systems. The network definition data structure holds weight values which determine the manner in which network signals, including the input signals, are combined.

Abstract: An electronic engine control (EEC) module executes both open loop and closed loop neural network processes to control the air/fuel mixture ratio of a vehicle engine to hold the fuel mixture at stoichiometry. The open loop neural network provides transient air/fuel control to provide a base stoichiometric air/fuel mixture ratio signal in response to throttle position under current engine speed and load conditions. The base air/fuel mixture ratio signal from the open loop network is additively combined with a closed loop trimming signal which varies the air/fuel mixture ratio in response to variations in the sensed exhaust gas oxygen level. Each neural network function is defined by a unitary data structure which defines the network architecture, including the number of node layers, the number of nodes per layer, and the interconnections between nodes. In addition, the data structure holds weight values which determine the manner in which network signals are combined.

Abstract: A method of automating the calibration of lookup tables containing correction values to be used in an on-board vehicle system is disclosed. The method includes training a neural network to model engine behavior by outputting cylinder specific crankshaft acceleration correction values in response to any engine speed and load input conditions. The correction values generated are stored in a memory device. The training takes place off-board the vehicle, using a data set previously obtained from operating a representative engine under normal operating conditions.