The present invention relates to a method and system for accurately predicting future trajectories of observed objects in dense and ever-changing city environments. More particularly, the present invention relates to substantially continuously tracking and estimating the future movements of an observed object. As an example, an observed object may be a moving vehicle, for example along a path or road. Aspects and/or embodiments seek to provide an end to end method and system for substantially continuously tracking and predicting future movements of a newly observed object, such as a vehicle, using motion prior data extracted from map data.

A method for identifying distracted pedestrians. The method includes determining operating conditions of a vehicle using a plurality of vehicle controllers. Pedestrian parameters for a pedestrian in a vicinity of the vehicle are acquired using a plurality of vehicle sensors. The pedestrian parameters include at least one of face positions, body positions, gait and hand gestures. Information related to an environment surrounding the vehicle is acquired. Pedestrian awareness level is determined based on the acquired pedestrian parameters and based on the information related to the environment surrounding the vehicle. A determination is made whether the pedestrian awareness level is below a predefined threshold. The pedestrian is classified as distracted, in response to determining that the pedestrian awareness level is below the predefined threshold.

Various embodiments described herein relate to techniques for forecasting with state transitions and confidence factors. In this regard, a system is configured to segment data associated with one or more assets to determine a set of classifications for one or more attributes related to the one or more assets. The system is also configured to generate a state machine associated with a Markov chain model based on the set of classifications for the data. Furthermore, the system is configured to perform a machine learning process associated with the state machine to determine one or more behavior changes associated with the one or more attributes related to the one or more assets. The system is also configured to predict, based on the one or more behavior changes associated with the one or more attributes related to the one or more assets, a change in demand data for the one or more assets during a future interval of time.

A gesture operation method based on depth values and the system thereof are revealed. A stereoscopic-image camera module acquires a first stereoscopic image. Then an algorithm is performed to judge if the first stereoscopic image includes a triggering gesture. Then the stereoscopic-image camera module acquires a second stereoscopic image. Another algorithm is performed to judge if the second stereoscopic image includes a command gesture for performing the corresponding operation of the command gesture.

A model learning device provided with: an error-added movement locus generation unit for adding an error to movement locus data for action learning that represents the movement locus of a subject and to which is assigned an action label that is information representing the action of the subject, and thereby generating error-added movement locus data; and an action recognition model learning unit for learning a model, using at least the error-added movement locus data and learning data created on the basis of the action label, by which model the action of some subject can be recognized from the movement locus of the subject. Thus, it is possible to provide a model by which the action of a subject can be recognized with high accuracy on the basis of the movement locus of the subject estimated using a camera image.

The disclosure relates to systems and methods for determining blood vessel conditions. The method includes receiving a sequence of image patches along a blood vessel path acquired by an image acquisition device. The method also includes predicting a sequence of blood vessel condition parameters on the blood vessel path by applying a trained deep learning model to the acquired sequence of image patches on the blood vessel path. The deep learning model includes a data flow neural network, a recursive neural network and a conditional random field model connected in series. The method further includes determining the blood vessel condition based on the sequence of blood vessel condition parameters. The disclosed systems and methods improve the calculation of the sequence of blood vessel condition parameters through an end-to-end training model, including improving the calculation speed, reducing manual intervention for feature extraction, increasing accuracy, and the like.

One or more aspects of utility decomposition with deep corrections are described herein. An entity may be detected within an environment through which an autonomous vehicle is travelling. The entity may be associated with a current velocity and a current position. The autonomous vehicle may be associated with a current position and a current velocity. Additionally, the autonomous vehicle may have a target position or desired destination. A Partially Observable Markov Decision Process (POMDP) model may be built based on the current velocities and current positions of different entities and the autonomous vehicle. Utility decomposition may be performed to break tasks or problems down into sub-tasks or sub-problems. A correction term may be generated using multi-fidelity modeling. A driving parameter may be implemented for a component of the autonomous vehicle based on the POMDP model and the correction term to operate the autonomous vehicle autonomously.

A method for establishing sensorimotor programs includes specifying a concept relationship that relates a first concept to a second concept and establishes the second concept as higher-order than the first concept; training a first sensorimotor program to accomplish the first concept using a set of primitive actions; and training a second sensorimotor program to accomplish the second concept using the first sensorimotor program and the set of primitive actions.

The present invention relates to a method and system for accurately predicting future trajectories of observed objects in dense and ever-changing city environments. More particularly, the present invention relates to the use of prior trajectories extracted from mapping data to estimate the future movement of an observed object. As an example, an observed object may be a moving vehicle. Aspects and/or embodiments seek to provide a method and system for predicting future movements of a newly observed object, such as a vehicle, using motion prior data extracted from map data.

A threat assessment system and method of assessing errant threat detection. The method, in one implementation, involves receiving a detection estimation from a driver of the vehicle or an object detection sensor of the vehicle, obtaining an analysis environmental camera image from a camera on the vehicle, generating a predictive saliency distribution based on the analysis environmental camera image, comparing the detection estimation received from the driver of the vehicle or the object detection sensor of the vehicle with the predictive saliency distribution, and determining a deviation between the detection estimation and the predictive saliency distribution.