Abstract: Pedestrian activity recognition is embodied in a method, system, non-transitory computer-readable and vehicle. A Siamese neural network is trained to recognize a plurality of pedestrian activities by training it recordings of the same pedestrian activity from two or more separate training image capture devices. The Siamese neural network is deployed with continual data collection from an additional image capture device to create a dataset of clusters of similar activities in an unsupervised manner. A spatio-temporal intent prediction model is then trained that can be deployed to recognize and predict pedestrian activity. Based on the likelihood of a particular pedestrian activity occurring or currently being underway, an automatic vehicle maneuver can be executed to navigate the situation.

Abstract: Autonomous driving systems may be provided with one or more sensors configured to capture perception data, a model configured to be continually trained in the transportation vehicle and a scalable subset memory configured to store a subset of a dataset previously used to train a model. A processor may be provided for continually training the model in the transportation vehicle using captured perception data previously unseen and the subset and for generating a new subset of data to be stored so that the model avoids catastrophic forgetting.

Abstract: Pedestrian activity recognition is embodied in a method, system, non-transitory computer-readable and vehicle. A Siamese neural network is trained to recognize a plurality of pedestrian activities by training it recordings of the same pedestrian activity from two or more separate training image capture devices. The Siamese neural network is deployed with continual data collection from an additional image capture device to create a dataset of clusters of similar activities in an unsupervised manner. A spatio-temporal intent prediction model is then trained that can be deployed to recognize and predict pedestrian activity. Based on the likelihood of a particular pedestrian activity occurring or currently being underway, an automatic vehicle maneuver can be executed to navigate the situation.

Abstract: A system and methodologies for neuromorphic (NM) vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic (NM) vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.

Abstract: A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.

Abstract: A method comprises obtaining, by a computer, patient information associated with a patient and operator information associated with one or more biometric sensors; determining, by the computer, a protocol for obtaining patient data from the patient based on the patient information and the operator information; obtaining, by the one or more biometric sensors, the patient data from the patient based on the protocol.