Abstract: A method for increasing comfort of an occupant in a vehicle includes training a plurality of emotional state prediction machine learning models. The method further may include recording a plurality of sensor data using at least one vehicle sensor. The method further may include determining an occupant personality profile of the occupant. The method further may include selecting a selected one of the plurality of emotional state prediction machine learning models based at least in part on the occupant personality profile. The method further may include determining an occupant emotional state of the occupant based at least in part on the plurality of sensor data using the selected one of the plurality of emotional state prediction machine learning models. The method further may include adjusting an operation of a vehicle autonomous driving system of the vehicle based at least in part on the occupant emotional state.

Abstract: A system for predicting one or more specific cognitive states of an individual based on non-neural physiological data collected by one or more non-neural physiological sensors includes one or more controllers in electronic communication with the one or more non-neural physiological sensors. The one or more controllers include a physiological data based neural network, a neural data based neural network, and an encoder-decoder that learns a transformation between a hidden layer of the physiological data based neural network and a hidden layer of the neural data based neural network during a training phase of the system.

Abstract: A computer system for confirming whether user generated code meets defined coding standards, includes a conversion module, a violation module, and an alert module. The conversion module is configured to receive one or more text documents including defined coding standards and generate an executable computer program code based on the defined coding standards. The violation module is configured to receive the computer program code and user generated code and execute the computer program code to determine whether one or more violations of the defined coding standards are present in the user generated code. The alert module is configured to, in response to the one or more violations being present in the user generated code, generate a notification indicating the one or more violations of the defined coding standards in the user generated code. Other example computer system and methods are also disclosed.

Abstract: A system for predicting one or more specific cognitive states of an individual based on non-neural physiological data collected by one or more non-neural physiological sensors includes one or more controllers in electronic communication with the one or more non-neural physiological sensors. The one or more controllers include a physiological data based neural network, a neural data based neural network, and an encoder-decoder that learns a transformation between a hidden layer of the physiological data based neural network and a hidden layer of the neural data based neural network during a training phase of the system.

Abstract: A method for increasing comfort of an occupant in a vehicle includes training a plurality of emotional state prediction machine learning models. The method further may include recording a plurality of sensor data using at least one vehicle sensor. The method further may include determining an occupant personality profile of the occupant. The method further may include selecting a selected one of the plurality of emotional state prediction machine learning models based at least in part on the occupant personality profile. The method further may include determining an occupant emotional state of the occupant based at least in part on the plurality of sensor data using the selected one of the plurality of emotional state prediction machine learning models. The method further may include adjusting an operation of a vehicle autonomous driving system of the vehicle based at least in part on the occupant emotional state.

Abstract: A system for dynamically adjusting interactions between an ADAS equipped vehicle and occupants of the vehicle includes one or more physiological sensors disposed on the vehicle and one or more control modules having a processor, a memory, and input/output (I/O) ports in communication with the one or more physiological sensors. The control modules execute program code portions stored in the memory that: collect sensor data from the one or more physiological sensors; analyze the sensor data and select a subset of the sensor data corresponding to a subset of the one or more physiological sensors; predicts, based on the subset of the sensor data, that an occupant of the vehicle is experiencing an increase in stress level; and adapt an ADAS action of the vehicle to reduce an occupant stress level from a first level to a second level lower than the first level.

Abstract: A system for dynamically adjusting interactions between an ADAS equipped vehicle and occupants of the vehicle includes one or more physiological sensors disposed on the vehicle and one or more control modules having a processor, a memory, and input/output (I/O) ports in communication with the one or more physiological sensors. The control modules execute program code portions stored in the memory that: collect sensor data from the one or more physiological sensors; analyze the sensor data and select a subset of the sensor data corresponding to a subset of the one or more physiological sensors; predicts, based on the subset of the sensor data, that an occupant of the vehicle is experiencing an increase in stress level; and adapt an ADAS action of the vehicle to reduce an occupant stress level from a first level to a second level lower than the first level.

Abstract: A method for monitoring uncertainty for human-like behavioral modulation of trajectory planning includes: retrieving map and agent information of a current driving state of an autonomously operated host automobile vehicle; dividing uncertainty conditions affecting a trajectory of the host automobile vehicle into an expected uncertainty and an unexpected uncertainty; calculating the expected uncertainty in a first operation branch by forming attention zones according to identified portions of lanes which may potentially collide with a planned route of the host automobile vehicle; determining the unexpected uncertainty in a second operation branch by calculating an anomaly score for any other vehicles in a surrounding area of the host automobile vehicle positioned in the lanes which may potentially collide with the planned route of the host automobile vehicle; and modulating trajectory operation signals determined for the expected uncertainty if the unexpected uncertainty meets or exceeds a predetermined thresh