Abstract: Systems, methods, and other embodiments described herein relate to encouraging exploration within a computing application. In one embodiment, a system includes a processor and a memory storing machine-readable instructions. The instructions, when executed by the processor, cause the processor to identify when a user is stuck in a repetitive pattern within a computing application. When the user is stuck in the repetitive pattern the instructions, the instructions when executed by the processor, cause the processor to encourage the user to further explore the computing application by 1) intervening in user activity within the computing application and 2) presenting attention-capturing content that is targeted to the user and unrelated to a domain of content accessed by the user within the computing application.

Abstract: Systems and methods of adapting a driver interface for a vehicle are disclosed. Exemplary implementations may: obtain cognitive factor information corresponding to a driver of the vehicle; and adapt a driver interface of the vehicle based on the cognitive factor information. The adapting of the driver interface includes alerting the driver via an alert using the adaptive driver interface. Systems and methods of adapting control of an autonomous vehicle using cognitive factor information corresponding to a driver of the autonomous vehicle are also disclosed.

Abstract: In one embodiment, a computer-implemented method for driver training using zone of proximal learning (ZPL) includes receiving, by one or more processors, driving data with respect to a driver operating a vehicle, estimating, using a personal behavior model, a driver profile based on the driving data, estimating one or more zone of proximal development (ZPD) states based at least in part on the driver profile, and performing one or more vehicle actions to place the driver into the one or more ZPD states.

Abstract: Systems, methods, and other embodiments described herein relate to integrating human decision-making into a model-based system. In one embodiment, a method includes acquiring sensor data, including driver data about a driver of a vehicle and driving data about the vehicle and a surrounding environment of the vehicle. The method includes encoding, using a world encoder, the sensor data into a latent representation. The method includes determining human decision- making characteristics according to the latent representation. The method includes generating a control signal for providing shared control of the vehicle according to the human decision-making characteristics and the latent representation.

Abstract: A teaching curriculum method for generating teaching actions for drivers, includes obtaining driving data from a plurality of driving scenarios, the driving data comprises vehicle trajectory information and corresponding scene context information, the driving scenarios comprising instructed driving events and uninstructed driving events, encoding, with a behavior model, the driving data, wherein the encoded driving data comprises an indication that a corresponding one of the driving scenarios comprises one of the instructed driving event or the uninstructed driving event, determining, with a trajectory estimator processing the encoded driving data, one or more driving skill transitions based on a presence or an absence of the indication, and generating, with a teacher action model, a teaching action for one of the plurality of driving scenarios.

Abstract: Methods and systems disclosed are directed to a virtual simulation of chargers in a real-world environment having a wiring system using, a camera, an augmented reality (AR) interface, and a processor operable to generate a virtual charger representing a charger superimposed on the real-world environment, receive data responsive to a user interaction using the AR interface to operate the virtual charger, update the virtual charger superimposed on the real-world environment based on the operation, and wherein the virtual charger includes a connector and a charging cable including a first end and a second end, the first end operable to be connected to the connector, and the second end operable to be connected to the wiring system.

Abstract: Systems, methods, and other embodiments described herein relate to encouraging exploration within a computing application. In one embodiment, a system includes a processor and a memory storing machine-readable instructions. The instructions, when executed by the processor, cause the processor to identify when a user is stuck in a repetitive pattern within a computing application. When the user is stuck in the repetitive pattern the instructions, the instructions when executed by the processor, cause the processor to encourage the user to further explore the computing application by 1) intervening in user activity within the computing application and 2) presenting attention-capturing content that is targeted to the user and unrelated to a domain of content accessed by the user within the computing application.

Abstract: In accordance with one embodiment, a system includes a processor, a memory module communicatively coupled to the processor, an NLP module communicatively coupled to the processor, and a set of machine-readable instructions stored in the memory module. The machine-readable instructions, when executed by the processor, direct the processor to perform operations including receiving a text data, and receiving a training text data for training one or more models of the NLP module. The operations also include generating, with a novice model of the NLP module, a novice suggestion based on the text data and the training text data to present an idea related to the text data, generating, with an expert model of the NLP module, an expert suggestion based on the text data and the training text data to present an idea elaborating on the text data, and outputting the novice suggestion and/or the expert suggestion.

Abstract: Disclosed are systems and methods for training at least one policy using a framework for encoding human behaviors and preferences in a driving environment. In one example, the method includes the steps of setting parameters of rewards and a Markov Decision Process (MDP) of the at least one policy that models a simulated human driver of a simulated vehicle and an adaptive human-machine interface (HMI) system configured to interact with each other and training the at least one policy to maximize a total reward based on the parameters of the rewards of the at least one policy.

Abstract: Systems and methods for generating prototypes are disclosed. In one embodiment, a computer-implemented method of creating a prototype includes receiving one or more input design parameters, generating, using a first neural network, a plurality of prototypes based on the one or more input design parameters, generating, using a second neural network, one or more decoy prototypes, and presenting, by an electronic display, a report including at least a portion of the plurality of prototypes and at least one of the one or more decoy prototypes.

Abstract: Systems and methods for providing a design with consumer feedback are provided. The method may include receiving a design within a design environment, wherein the design comprises a plurality of attributes. The method may further include automatically generating, using a computer model, consumer-based feedback regarding at least one attribute of the plurality of attributes. The method may additionally include presenting the consumer-based feedback within the design environment in real-time.

Abstract: In accordance with one embodiment, a system includes a processor, a memory module communicatively coupled to the processor, an NLP module communicatively coupled to the processor, and a set of machine-readable instructions stored in the memory module. The machine-readable instructions, when executed by the processor, direct the processor to perform operations including receiving a text data, and receiving a training text data for training one or more models of the NLP module. The operations also include generating, with a novice model of the NLP module, a novice suggestion based on the text data and the training text data to present an idea related to the text data, generating, with an expert model of the NLP module, an expert suggestion based on the text data and the training text data to present an idea elaborating on the text data, and outputting the novice suggestion and/or the expert suggestion.

Abstract: Systems and methods for dynamically filtering choices include identifying, with an indecisiveness detector module, a state of a user, determining, with the indecisiveness detector module, whether the state of the user includes an indecisive behavior, identifying, with a choice identifier module, a state of an environment of the user, identifying, with the choice identifier module, a set of available choices from the state of the environment, receiving, with a processor, a set of past choices and a set of past user decisions relating to the set of past choices, and generating, with a decision making model, a predicted choice from the set of available choices based on the set of past choices and the set of past user decisions in response to determining that the state of the user includes an indecisive behavior.