Abstract: Described is a system for detecting and correcting perception errors in a perception system. In operation, the system generates a list of detected objects from perception data of a scene, which allows for the generation of a list of background classes from backgrounds in the perception data associated with the list of detected objects. For each detected object in the list of detected objects, a closest background class is identified from the list of background classes. Vectors can then be used to determine a semantic feature, which is used to identify axioms. An optimal perception parameter is then generated, which is used to adjust perception parameters in the perception system to minimize perception errors.

Abstract: Described is a system for selecting among intelligence elements of a neural model. An intelligence element is selected from a set of intelligence elements which change group attack probability estimates and processed via multiple operations. A semantic memory component learns group probability distributions and rules based on the group probability distributions. The rules determine which intelligence element related to the groups to select. Given an environment of new probability distributions, the semantic memory component recalls which rule to select to receive a particular intelligence element. An episodic memory component recalls a utility value for each information element A procedural memory component recalls and selects the information element considered to have the highest utility. A list of intelligence elements is published to disambiguate likely attackers.

Abstract: Described is a system to evaluate and reduce perception error in object detection and recognition. The system includes a perception module that receives perception data (of an object(s)) from an environment proximate a mobile platform. Perception probes are generated that describe one or more characteristics of the objects. The perception probes are converted into probabilistic signal temporal logic (PSTL)-based constraints that provide axioms having statistical analysis of the perception probes. The axioms are evaluated to classify the perception probes as valid or erroneous. Optimal perception parameters are generated by solving an optimization problem based on the axioms, which allows the system to adjust the perception module based on the optimal perception parameters.

Abstract: Described is a system for an episodic memory used by an automated platform. The system acquires data from an episodic memory that comprises an event database, an event-sequence graph, and an episode list. Using the event-sequence graph, the system identifies a closest node to a current environment for the automated platform. Based on the closest node and using a hash function or key based on the hash function, the system retrieves from the event database an episode that corresponds to the closest node, the episode including a sequence of events. Behavior of the automated platform in the current environment is guided based on the data from the episodic memory.

Abstract: The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the method is operative to receive an input indicative of a training event, segmenting the driving episode into a plurality of time steps, generate a parse tree in response to each time step, and generate a most probable parse tree from a combination of the generated parse trees.

Abstract: An autonomous vehicle, system and method of operating the autonomous vehicle. The system includes a sensor, a reasoning engine and a navigation system. The sensor receives token data. The reasoning engine performs an abductive inference on a fact determined from the token data to estimate a backward condition, and a deductive inference to the estimated backward condition in to order to predict a forward condition. The navigation system operates the autonomous vehicle based on the predicted forward condition.

Abstract: An autonomous vehicle, system and method of operating the autonomous vehicle. The system includes an episodic memory, a hyper-association module and a navigation system. The episodic memory stores a plurality of episodes, recalls a plurality of candidate episodes in response to receiving a partial prefix and recalls a hypothesis episode in response to receiving an intermediate episode. The hyper-association module receives the plurality of candidate episodes from the episodic memory and obtains the intermediate episode from the plurality of candidate episodes. The navigation system navigates the autonomous vehicle using the hypothesis episode.

Abstract: Embodiments include methods, systems and computer readable storage medium for a method for behavioral path planning guidance for a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides path-planning guidance based on training data and one or more output trajectories generated from a plurality of predictive models and a plurality of input variables. The method includes determining, by a processor, a location of the vehicle on a map containing a road network and determining, by the processor, whether one or more objects exist within a predetermined range of the vehicle. The method includes selecting, by the processor, an output trajectory to traverse the road network based on the location of the vehicle on the map and the existence of one or more objects. The method includes controlling, by the processor, operation of the vehicle using the output trajectory.

Abstract: Embodiments include methods, systems and computer readable storage medium for a method for collision avoidance by a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides collision avoidance guidance based on training data using movement information from one or more agents and behaviors associated with one or more individuals associated with the one or more agents or the vehicle. The method further includes detecting, by a processor, a collision course between the vehicle and the one or more mobile agents and/or one or more stationary agents. The method further includes calculating, by the processor, one or more decisions that avoid a collision in response to detecting a collision course. The method further includes selecting, by the processor, a decision from the one or more decisions and controlling, by the processor, operation of the vehicle based on the selected decision.

Abstract: An autonomous vehicle, cognitive system for operating an autonomous vehicle and method of operating an autonomous vehicle. The cognitive system includes one or more hypothesizer modules, a hypothesis resolver, one or more decider modules, and a decision resolver. Data related to an agent is received at the cognitive system. The one or more hypothesizer modules create a plurality of hypotheses for a trajectory of the agent based on the received data. The hypothesis resolver selects a single hypothesis for the trajectory of the agent from the plurality of hypotheses based on a selection criteria. The one or more decider modules create a plurality of decisions for a trajectory of the autonomous vehicle based on the selected hypothesis for the agent. The decision resolver selects a trajectory for the autonomous vehicle from the plurality of decisions. The autonomous vehicle is operated based on the selected trajectory.

Abstract: Embodiments include methods, systems, and computer readable storage medium for a method for providing path-planning guidance by resolving multiple behavioral predictions associated with operating a vehicle is disclosed. The method includes installing a vehicle system into a vehicle, wherein the vehicle system provides path planning guidance based on training data using and fused hypotheses and/or decisions associated with the training data. The method further includes determining, by a processor, a location of the vehicle on a map containing a road network, and determining, by the processor, whether one or more agents exist within a predetermined range of the vehicle. The method further includes selecting, by the processor, an output trajectory to traverse the road network based on the location of the vehicle on the map and the existence of one or more agents. The method further includes controlling, by the processor, operation of the vehicle using the output trajectory.

Abstract: An autonomous vehicle, system and method for operating the autonomous vehicle. The system includes a plurality of solution modules, a state module, a hypothesis resolver and a navigation module. The plurality of solution modules each provide a solution for a future state of an agent. The state module that provides an environmental state. The hypothesis resolver receives the environmental state and the plurality of solutions, selects a solution from the plurality of solutions based on the environmental state and determines a reward for the solution, the reward indicating a confidence level of the solution for the environmental state. The navigation module navigates the autonomous vehicle based on the selected solution.

Abstract: Described is a system for personalizing a human-machine interface (HMI) device based on a mental and physical state of a user. During performance of a task in a simulation environment, the system extracts biometric features from data collected from body sensors, and extracts brain entropy features from electroencephalogram (EEG) signals. The brain entropy features are correlated with the biometric features to generate a mental-state model. The mental-state model is deployed in a HMI device during performance of the task in an operational environment for continuous adaptation of the HMI device to its user's mental and physical states.

Abstract: Described is a system for predicting multi-agent movements. A Radon Cumulative Distribution Transform (Radon-CDT) is applied to pairs of signature-formations representing agent movements. Canonical correlation analysis (CCA) components are identified for the pairs of signature-formations. Then, a relationship between the pairs of signature formations is learned using the CCA components. A counter signature-formation for a new dataset is predicted using the learned relationship and a new signature-formation. Control parameters of a device can be adjusted based on the predicted counter signature-formation.

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: Described is a system for neural decoding of neural activity. Using at least one neural feature extraction method, neural data that is correlated with a set of behavioral data is transformed into sparse neural representations. Semantic features are extracted from a set of semantic data. Using a combination of distinct classification modes, the set of semantic data is mapped to the sparse neural representations, and new input neural data can be interpreted.

Abstract: Described is a system for storing and retrieving a memory in context. A memory formed for a given context is encoded in a neural model of the entorhinal-hippocampal system, forming a context-appropriate memory. The context-appropriate memory is comprised of an association between presented environmental cues and presence of a rewarded event. The system is able to discriminate between environmental cues in an environment surrounding a vehicle and retrieve at least one encoded context-appropriate memory. Using the at least one retrieved encoded context-appropriate memory, the system determines whether to initiate a collision avoidance operation to cause the vehicle to proactively avoid a collision.

Abstract: Described is a system for proactive and reactive cognitive control using a neural module. The system calculates, for each hypothesis of a set of hypotheses, a probability that an event will occur. The neural module comprises a plurality of neurons and includes the PC module, a prefrontal cortex (PFC) module, an anterior cingulate cortex (ACC) module, a locus coeruleus (LC) module, and a basal forebrain (BF) module. The set of hypotheses are related to tasks to be performed by a plurality of groups, each group having a corresponding hypothesis. For each probability, the system calculates a conflict value across all hypotheses with the ACC module, compares each conflict value to a predetermined threshold using the BF and LC modules. A determination is made whether to directly output the calculated probability or perform an additional probability calculation and output an updated probability.

Abstract: Described is a system for prediction of adversary movements. In an aspect, the system includes one or more processors and a memory, the memory being a non-transitory computer-readable medium having executable instructions encoded thereon, such that upon execution of the instructions, the one or more processors perform operations of computing relative positions of multiple objects of interest, generating a feature representation by forming a matrix based on the relative positions, predicting movement of the multiple objects of interest by applying clustering to the feature representation and by performing canonical correlation analysis, and controlling a device based on the predicted movement of the multiple objects of interest.

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 a plurality of objects within the environment of the vehicle; processing, by the processor, the sensor data to determine feature data associated with each of the plurality of objects, wherein the feature data includes current data of each object, history data of each object, and interaction data between each object and at least two other objects; processing, by the processor, the feature data associated with a first object of the plurality of objects with a model to determine a future position of the first object; and controlling, by the processor, the vehicle based on the future position.