Abstract: Systems and methods for a powered, robotic exoskeleton, or exosuit, for a user's limbs and body are provided. The exosuit may be equipped with airbag devices mounted at various locations on the suit. The exosuit may include on-board computing equipment that can sense, compute control commands in real-time, and actuate limbs and airbags to restore stability (fall prevention) and minimize injuries due to falls, should they happen (fall protection).

Abstract: Systems and methods for learning and managing robot user interfaces are disclosed herein. One embodiment generates, based on input data including information about past interactions of a particular user with a robot and with existing HMIs of the robot, a latent space using one or more encoder neural networks, wherein the latent space is a reduced-dimensionality representation of learned behavior and characteristics of the particular user, and uses the latent space as input to train a decoder neural network associated with (1) a new HMI distinct from the existing HMIs or (2) a particular HMI among the existing HMIs to alter operation of the particular HMI. The trained first decoder neural network is deployed in the robot to control, at least in part, operation of the new HMI or the particular HMI in accordance with the learned behavior and characteristics of the particular user.

Abstract: A method performed by an autonomous vehicle includes identifying a condition preventing the autonomous vehicle from proceeding along an intended route. The method also includes prompting a passenger of the autonomous vehicle to interact with a driver of a first vehicle in response to identifying the condition. The method further includes receiving, from the passenger, an input at an interface of the autonomous vehicle indicating a successful interaction or an unsuccessful interaction with the driver. The method also includes controlling the autonomous vehicle to proceed along the intended route based on the input indicating the successful interaction with the driver.

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: A method for certified control of a self-driving ego vehicle is described. The method includes analyzing a safety situation of the self-driving ego vehicle to determine a proposed vehicle control action using a main controller of the self-driving ego vehicle. The method also includes presenting, by the main controller, the proposed vehicle control action to an interlock controller, including a certificate of the proposed vehicle control action. The method further includes checking a safety certification evidence from the certificate by the interlock controller using a predefined safety argument to verify the safety certification evidence of the certificate. The method also includes directing, by a low-level controller, the self-driving ego vehicle to perform a certified vehicle control action.

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 personalized notification method for a mobility as a service (MaaS) vehicle includes receiving conditional personalized notification features from a passenger of the MaaS vehicle. The method also includes monitoring current driving environment of the MaaS vehicle to determine whether a condition of the conditional personalized notification features is satisfied. The method further includes notifying the passenger when the condition is satisfied via at least one localized output device in a compartment of the MaaS vehicle.

Abstract: A driver monitor system and method for predicting impairment of a user of a vehicle. The system includes video cameras, an input device for inputting a list of medications being taken by the driver. Processing circuitry predicts side effects of the medications based on the half-life of the medication, detecting eye gaze movement, eye lid position, and facial expression of the user using images from the video camera, predicting whether the user is transitioning into an impaired physical state that is a side effect of the medications, verifying the side effect of the medications, determining whether the user is fit to drive using the verified side effects of the medications, and outputting to the vehicle an instruction to operate the vehicle in a level of automation that makes up for the at least one side effect or to perform a safe pull over operation of the vehicle.

Abstract: Aspects of the disclosure provide a parking method and system for a vehicle. For example, the parking system can include a processor and an output circuit. As another example, the parking system can include the processor and a compelling unit. The processor can be configured to determine whether a first weather condition, such as snowing, raining, icing and hailing, at a first parking location meets a first criterion, and identify, when the first weather condition meets the first criterion, a second parking location at which a second weather condition meets a second criterion different from the first criterion. The output unit can be configured to output an alert to the vehicle parked at the first parking location indicating that the vehicle is to be moved to the second parking location. The compelling unit can be configured to compel the vehicle to move to the second parking location.

Abstract: A driver monitor system and method for predicting impairment of a user of a vehicle. The system includes video cameras, an input device for inputting a list of medications being taken by the driver. Processing circuitry predicts side effects of the medications based on the half-life of the medication, detecting eye gaze movement, eye lid position, and facial expression of the user using images from the video camera, predicting whether the user is transitioning into an impaired physical state that is a side effect of the medications, verifying the side effect of the medications, determining whether the user is fit to drive using the verified side effects of the medications, and outputting to the vehicle an instruction to operate the vehicle in a level of automation that makes up for the at least one side effect or to perform a safe pull over operation of the vehicle.

Abstract: Aspects of the disclosure provide a parking method and system for a vehicle. For example, the parking system can include a processor and an output circuit. As another example, the parking system can include the processor and a compelling unit. The processor can be configured to determine whether a first weather condition, such as snowing, raining, icing and hailing, at a first parking location meets a first criterion, and identify, when the first weather condition meets the first criterion, a second parking location at which a second weather condition meets a second criterion different from the first criterion. The output unit can be configured to output an alert to the vehicle parked at the first parking location indicating that the vehicle is to be moved to the second parking location. The compelling unit can be configured to compel the vehicle to move to the second parking location.

Abstract: A method for synthesizing parameters for control of a closed loop system based on a differentiable simulation model of the closed loop system includes determining requirements/specifications for the closed loop system in signal temporal logic (STL). The method also includes selecting a parametric control law having a differentiable parameter control function. The method also includes converting the requirements in signal temporal logic into differentiable computational graph. The method further includes building the differentiable simulation model as a differentiable computational graph. Furthermore, the method includes automatically learning values of parameters for the differentiable parameter control function of the closed loop system by backpropagating an error.

Abstract: An artificial intelligence perception system for detecting one or more objects includes one or more processors, at least one sensor, and a memory device. The memory device includes an image capture module, an object identifying module, and a logical scaffold module. The image capture module and the object identifying module cause the one or more processors to obtain sensor information of a field of view from a sensor, identify an object within the sensor information, and determine at least one property of the object. The logical scaffold module causes the one or more processors to determine, by a logical scaffold, when the at least one property of the object as determined by the object identifying module is one of a true condition or a false condition.

Abstract: Systems and methods for a powered, robotic exoskeleton, or exosuit, for a user's limbs and body are provided. The exosuit may be equipped with airbag devices mounted at various locations on the suit. The exosuit may include on-board computing equipment that can sense, compute control commands in real-time, and actuate limbs and airbags to restore stability (fall prevention) and minimize injuries due to falls, should they happen (fall protection).

Abstract: A method performed by an autonomous vehicle includes identifying a condition preventing the autonomous vehicle from proceeding along an intended route. The method also includes prompting a passenger of the autonomous vehicle to interact with a driver of a first vehicle in response to identifying the condition. The method further includes receiving, from the passenger, an input at an interface of the autonomous vehicle indicating a successful interaction or an unsuccessful interaction with the driver. The method also includes controlling the autonomous vehicle to proceed along the intended route based on the input indicating the successful interaction with the driver.

Abstract: Systems and methods for predicting a trajectory of a road agent are disclosed herein. One embodiment receives sensor data from one or more sensors; analyzes the sensor data to generate a predicted trajectory of the road agent, wherein the predicted trajectory includes a sequence of primitives, at least one primitive in the sequence of primitives having an associated duration that is determined in accordance with a dynamic timescale; and controls one or more aspects of the operation of an ego vehicle based, at least in part, on the predicted trajectory of the road agent.