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: 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: 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: A method for machine-assisted collaborative product design is described. The method includes training a neural network to simulate a plurality of stakeholder personas in a product review process to provide a plurality of stakeholder models. The method also includes simulating, using the plurality of stakeholder models, the plurality of stakeholder personas in the product review process of a potential product. The method further includes aggregating individual scores output from the plurality of stakeholder models corresponding to each of the plurality of stakeholder personas regarding the potential product; wherein each of the individual scores corresponds to a stakeholder persona and that stakeholder persona's reaction to the potential product. The method also includes displaying a summary providing an overview of the aggregated individual scores regarding the potential product to a user.

Abstract: A system and method for generating a predicted vehicle trajectory includes a generative adversarial network configured to receive a trajectory vector of a target vehicle and generate a set of latent state vectors using the received trajectory vector and an artificial neural network. The latent state vectors each comprise a high-level sub-vector, ZH. The GAN enforces ZH to be correlated to an annotation coding representing semantic categories of vehicle trajectories. The GAN selects a subset, from the set of latent state vectors, using farthest point sampling and generates a predicted vehicle trajectory based on the selected subset of latent state vectors.

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: A system and method for generating a predicted vehicle trajectory includes a generative adversarial network configured to receive a trajectory vector of a target vehicle and generate a set of latent state vectors using the received trajectory vector and an artificial neural network. The latent state vectors each comprise a high-level sub-vector, ZH. The GAN enforces ZH to be correlated to an annotation coding representing semantic categories of vehicle trajectories. The GAN selects a subset, from the set of latent state vectors, using farthest point sampling and generates a predicted vehicle trajectory based on the selected subset of latent state vectors.