Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving sensor data sensed from an environment associated with the vehicle; processing, by a processor, the sensor data to determine observation data, the observation data including differential features associated with an agent in the environment; determining, by the processor, a context associated with the agent based on the observation; selecting, by the processor, a first probability model associated with the context; processing, by the processor, the observation data with the selected first probability model to determine a set of predictions; processing, by the processor, the set of predictions with a second probability model to determine a final prediction of interaction behavior associated with the agent; and selectively controlling, by the processor, the vehicle based on the final prediction of interaction behavior associated with the agent.

Abstract: An automotive vehicle includes an actuator configured to control vehicle acceleration, steering, or braking. The vehicle additionally includes at least one sensor configured to detect an object in the vicinity of the vehicle. The vehicle further includes at least one controller configured to automatically control the actuator according to an automated driving system algorithm. The at least one controller is configured to define a first vehicle route, detect at least one object in the vicinity of the vehicle; calculate a probabilistic forecast of a future location of the at least one detected object, define a second vehicle route in response to the probabilistic forecast indicating proximity of the at least one detected object to the first route, and to control the actuator to execute the second vehicle route.

Abstract: In various embodiments, methods, systems, and autonomous vehicles are provided. In one exemplary embodiment, a method is provided that includes obtaining first sensor inputs pertaining to one or more actors in proximity to an autonomous vehicle; obtaining second sensor inputs pertaining to operation of the autonomous vehicle; obtaining, via a processor, first neural network outputs via a first neural network, using the first sensor inputs; and obtaining, via the processor, second neural network outputs via a second neural network, using the first network outputs and the second sensor inputs, the second neural network outputs providing one or more recommended actions for controlling the autonomous vehicle.

Abstract: A system and method is taught for data processing in an autonomous vehicle control system. Using information is acquired from the vehicle, network interface, and sensors mounted on the vehicle, the system can perceive situations around it with much less complexity in computation without losing crucial details, and then make navigation and control decisions. The system and method are operative to generate situation aware events, store them, and recall to predict situations for autonomous driving.

Abstract: Described is a system for cueing a specific memory in a waking state. The system sends an initiation signal to a memory recall controller to select a stored stimulation pattern previously associated with a specific memory of an event. The system signals to a memory recall controller to initiate delivery of the selected stimulation pattern to a brain in a waking state for a duration of the event via a brain stimulation system. Following completion of the event, the system signals for the memory recall controller to stop the brain stimulation system from delivering the selected stimulation pattern.

Abstract: The present application generally relates to methods and apparatus for evaluating driving performance under a plurality of driving scenarios and conditions. More specifically, the application teaches a method and apparatus for testing a driving scenario repetitively while altering a parametric variation, such as fog level, in order to evaluate driving system performance under changing conditions.

Abstract: The present application generally relates to methods and apparatus for evaluating driving performance under a plurality of driving scenarios and conditions. More specifically, the application teaches a method and apparatus for testing a driving scenario repetitively while altering a parametric variation, such as fog level, in order to evaluate driving system performance under changing conditions.

Abstract: The present application generally relates to methods and apparatus for evaluating and assigning a complexity metric to a driving scenario. More specifically, the application teaches a method and apparatus for breaking a scenario into subtasks, assigning each subtask a complexity value and generating an overall complexity metric in response to a weighted combination of the subtask complexities as well as human-perceived task complexity.

Abstract: The present application generally relates to methods and apparatus for evaluating and assigning a performance metric to a driver response to a driving scenario. More specifically, the application teaches a method and apparatus for breaking a scenario into features, assigning each feature a grade and generating an overall grade in response to a weighted combination of the grades.

Abstract: A system and method is taught for data processing in an autonomous vehicle control system. Using information is acquired from the vehicle, network interface, and sensors mounted on the vehicle, the system can perceive situations around it with much less complexity in computation without losing crucial details, and then make navigation and control decisions. The system and method are operative to generate situation aware events, store them, and recall to predict situations for autonomous driving.

Abstract: Described is a training system for training a vision-based object detector. The system is configured to run an object detector on an image of a cleared scene to detect objects in the cleared scene. The object detector includes a support vector machine (SVM) or similar classifier with a feature model to generate an SVM score for object features and a spatial bias threshold to generate augmented object scores. The system designated detected objects in the cleared scene as false detections and, based on that, updates at least one of the feature model and spatial bias threshold to designate the false detections as background. The updated feature model or updated spatial bias threshold are then stored for use in object detection.

Abstract: Described is a system for robot supervisory control. The system includes an operator device that receives camera imagery from a camera mounted on a robot and three-dimensional (3D) data from a 3D sensor mounted on the robot. The user interface displays a two-dimensional (2D) view of the scene from the camera and 3D sensor data. One or more object markers of objects in the scene are overlaid on the 2D view or 3D sensor data. Viewing the scene, the user can choose a robotic action from a library of actions, which generates a simulation of the robot performing the selected action. The simulation is then rendered and overlaid on top of the 3D sensor data. Moreover, the simulation shows the expected variability of the robot's action. Based on the simulation's outcome, the user can approve and trigger the execution of the action in the real robot.

Abstract: Systems, methods, and apparatus for neuro-robotic goal selection are disclosed. An example method to control a robot is described, including presenting a target object to a user, the target object corresponding to a goal to be effectuated by a robot, emphasizing a portion of the target object, identifying a first brain signal corresponding to a first mental response of the user to the emphasized portion, determining whether the first mental response corresponds to a selection of the emphasized portion by the user, and effectuating the robot with respect to the goal based on the emphasized portion.

Abstract: The present invention relates to a system for detecting an object of interest in a scene. The system operates by receiving an image frame of a scene and extracting features from the image frame, the features being descriptors. The descriptors are quantized to generate PHOW features. A sliding window protocol is implemented to slide a window over the image and analyze the PHOW features that fall inside the window. Finally, the system determines if the PHOW features represent the object of interest and, if so, then designates the window as a location in the image with a detected object of interest.

Abstract: The present invention relates to a system for deep packet inspection and intrusion detection. The system uses a pattern matching module receiving as an input a data stream in a neural network. Neurons are activated such that when active, the neuron fires to all connecting output neurons to form a neuron spike, each neuron spike from the assigned neuron to a connecting output neuron having a delay. A delay is associated with each input character in the pattern, such that a position of each input character relative to an end of the pattern is stored in an alphabet-pattern-delay matrix (APDFM). An activation matrix (AM) is used to match each input character with a stored pattern to generate a similarity match and determine if the string of characters is the stored pattern.

Abstract: A method and apparatus for processing sound from a sound source. The sound from the sound source is detected. Harmonics in the sound from the sound source are identified. A distance to the sound source is identified using the harmonics and a number of atmospheric conditions.

Abstract: Described is a recall system that uses spiking neuron networks to identify an unknown external stimulus. The system operates by receiving a first input signal (having spatial-temporal data) that originates from a known external stimulus. The spatial-temporal data is converted into a first spike train. A first set of polychronous groups (PCGs) are generated as a result of the first spike train. Thereafter, a second input signal originating from an unknown external stimulus is received. The spatial-temporal data of the second input signal is converted into a second spike train. A second set of PCGs are then generated as a result of the second spike train. Finally, the second set of PCGs is recognized as being sufficiently similar to the first set of PCGs to identify the unknown external stimulus as the known external stimulus.

Abstract: A three dimensional audio playback system in which the audio clips are determined by location. The audio playback system being located within a vehicle to aid in navigation or for entertainment or informational or safety purposes.

Abstract: Systems, methods, and apparatus for neuro-robotic tracking point selection are disclosed. A described example robot control system includes a feature and image presenter, a classifier, a visual-servo controller, and a robot interface. The feature and image presenter is to display an image of an object, emphasize one of more potential trackable features of the object, receive a selection of the emphasized feature, and determine an offset from the selected feature as a goal. The classifier is to classify a mental response to the emphasized features, and to determine that the mental response corresponds to the selection of one of the emphasized features. The visual-servo controller is to track the emphasized feature corresponding to an identified brain signal. The robot interface is to generate control information to effect a robot action based on the emphasized feature, the visual-servo controller to track the emphasized feature while the robot action is being effected.

Abstract: Described is a system for representing, storing, and reconstructing an input signal. The system constructs an index of unique polychronous groups (PCGs) from a spiking neuron network. Thereafter, a basis set of spike codes is generated from the unique PCGs. An input signal can then be received, with the input signal being spike encoded using the basis set of spike codes from the unique PCGs. The input signal can then be reconstructed by looking up in a reconstruction table, for each unique PCG in the basis set in temporal order according to firing times, anchor neurons. Using a neuron assignment table, an output location can be looked up for each anchor neuron to place a value based on the firing times of each unique PCG. Finally, the output locations of the anchor neurons can be compiled to reconstruct the input signal.