Abstract: A method of using perception-inspired event generation for situation awareness for a vehicle, including receiving perception input data from a sensor of the vehicle and processing the perception input data to classify and generate parameters related to an external entity in a vicinity of the vehicle. The method includes generating a hierarchical event structure that classifies and prioritizes the perception input data by classifying the external entity into an attention zone and prioritizing the external entity within the attention zone according to a risk level value for the external entity. A higher risk level value indicates a higher priority within the attention zone. The method further includes developing a behavior plan for the vehicle based on the hierarchical event structure.

Abstract: A method, autonomous vehicle and system for operating an autonomous vehicle. A sensor obtains data of an agent. A processor determines a measure of complexity of the environment in which the autonomous vehicle is operating from the sensor data, selects a control scheme for operating the autonomous vehicle based on the determined complexity, and operates the autonomous vehicle using the selected control scheme.

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: Described is a system for feature selection that extends supervised hierarchical clustering to neural activity signals. The system generates, using a hierarchical clustering process, a hierarchical dendrogram representing a set of neural activity data comprising individual neural data elements having neural activity patterns. The hierarchical dendrogram is searched for an optimal cluster parcellation using a stochastic supervised search process. An optimal cluster parcellation of the hierarchical dendrogram is determined that provides a classification of the set of neural activity data with respect to a supervised classifier, resulting in a reduced neural activity feature set. The set of neural activity data is classified using the reduced neural activity feature set, and the classified set of neural activity data is decoded.

Abstract: A method of path planning for a host vehicle includes: receiving host vehicle, environmental and obstacle information; calculating one or more projected host vehicle locations; computing a projected obstacle location for each obstacle; and determining a collision potential between each projected host vehicle location and each projected obstacle location. Until a maximum number of steps is reached, and while at least one projected host vehicle location has an associated collision potential below a collision threshold, the method further includes repeating the calculating, computing and determining steps.

Abstract: A method of predictive navigation control for an ego vehicle includes: comparing a cue node to each of a plurality of episodic memory nodes in an episodic memory structure, wherein the cue node represents a new event representing distances, speeds and headings of one or more newly observed objects about the ego vehicle, and wherein the episodic memory structure includes a network of nodes each representing a respective previously existing event and having a respective node risk and likelihood; determining which of the nodes has a smallest respective difference metric, thus defining a best matching node; consolidating the cue node with the best matching node if the smallest difference metric is less than a match tolerance, else adding a new node corresponding to the cue node to the episodic memory structure; and identifying a likeliest next node and/or a riskiest next node.

Abstract: An autonomous vehicle and a system and method of operating the autonomous vehicle. A maneuver classifier is trained at an offline processor to identify a driving maneuver for a driving context. An online processor is configured to receive the driving context, operate the maneuver classifier to identify the driving maneuver based on the driving context, perform the driving maneuver at the autonomous vehicle, grade the driving maneuver as it is being performed at the autonomous vehicle, and adjust a performance of the driving maneuver at the autonomous vehicle based on the grade.

Abstract: Described is a system for contrast and entropy-based perception adaption to optimize perception. The system is operable for receiving an input image of a scene with a camera system and detecting one or more objects (having perception data) in the input image. The perception data of the one or more objects is converted into probes, which are then converted into axioms using probabilistic signal temporal logic. The axioms are evaluated based on probe bounds. If the axioms are within the probe bounds, then results are provided; however, if the axioms are outside of the probe bounds, the system estimates optimal contrast bounds and entropy bounds as perception parameters. The contrast and entropy in the camera system are then adjusted based on the perception parameters.

Abstract: A system for interactive hypothesis estimation of multi-vehicle traffic for autonomous driving is provided. The system includes a sensor upon a host vehicle providing data regarding an operating environment of the host vehicle and a computerized device. The computerized device is operable to monitor the data from the sensor, identify a road surface based upon the data, and identify a neighborhood object based upon the data. The computerized device is further operable to determine a pressure score for the neighborhood object based upon a likelihood that the neighborhood object will conflict with the host vehicle based upon the road surface and the neighborhood object, selectively track the neighborhood object based upon the pressure score, and navigate the host vehicle based upon the tracking of the neighborhood object.

Abstract: A system ground surface projection for autonomous driving of a host vehicle is provided. The system includes a LIDAR device of the host vehicle and a computerized device. The computerized device is operable to monitor data from the LIDAR device including a total point cloud. The total point cloud describes an actual ground surface in the operating environment of the host vehicle. The device is further operable to segment the total point cloud into a plurality of local point cloud and, for each of the local point clouds, determine a local polygon estimating a portion of the actual ground surface. The device is further operable to assemble the local polygons into a total estimated ground surface and navigate the host vehicle based upon the total estimated ground surface.

Abstract: A vehicle and a system and a method of operating the vehicle. The system includes a reasoning engine, an episodic memory, a resolver and a controller. The reasoning engine infers a plurality of possible scenarios based on a current state of an environment of the vehicle. The episodic memory determines a historical likelihood for each of the plurality of possible scenarios. The resolver selects a scenario from the plurality of possible scenarios using the historical likelihoods. The controller operates the vehicle based on the selected scenario.

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: An ego vehicle includes decider modules and a grader module coupled to a resolver module. The decider modules generate trajectory decisions at a current time, generate a current two-dimensional slice of a flat space around the ego vehicle, generate future two-dimensional slices of the flat space by projecting the current two-dimensional slice of the flat space forward in time, and generate a three-dimensional state space by stacking the current two-dimensional slice and the future two-dimensional slices. The grader module generates rewards for the trajectory decisions based on a recent behavior of an ego vehicle. The resolver module selects a final trajectory decision for the ego vehicle from the trajectory decisions based on the three-dimensional state space and the rewards. The current two-dimensional slice includes a current ego vehicle location and current neighboring vehicle locations. The future two-dimensional slices include future ego vehicle locations and future neighboring vehicle locations.

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: A control system of the autonomous vehicle may generate multiple possible behavior control movements based on the driving goal and the assessment of the vehicle environment. In doing so, the method and system selects one of the best behavior control, among the multiple possible movements, and the selection is based on the quantitative grading of its driving behavior.

Abstract: A virtual lane estimation system includes a memory device, a sensor and a computer. The memory device is configured to store a road map that corresponds to a portion of a road ahead of a vehicle. The sensor is configured to observe a plurality of trajectories of a plurality of neighboring vehicles that traverse the portion of the road. The computer is configured to initialize a recursive self-organizing map as a plurality of points arranged as a two-dimensional grid aligned with the road map, train the points in the recursive self-organizing map in response to the trajectories, generate a directed graph that contains one or more virtual lanes through the road map in response to the points trained to the trajectories, and generate a control signal that controls navigation of the vehicle through the portion of the road in response to the virtual lanes in the directed graph.

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: Described is a system for analyzing time series data. A sequence of symbols is generated from a set of time series input data related to a moving vehicle using automatic segmentation. A grammar is extracted from the sequence of symbols, and the grammar is a subset of a probabilistic context-free grammar (PCFG). Using the grammar, time series input data can be analyzed, and a prediction of the vehicle's movement can be made. Vehicle operations for an autonomous vehicle are determined using the prediction.

Abstract: Described is a system for contrast and entropy-based perception adaption to optimize perception. The system is operable for receiving an input image of a scene with a camera system and detecting one or more objects (having perception data) in the input image. The perception data of the one or more objects is converted into probes, which are then converted into axioms using probabilistic signal temporal logic. The axioms are evaluated based on probe bounds. If the axioms are within the probe bounds, then results are provided; however, if the axioms are outside of the probe bounds, the system estimates optimal contrast bounds and entropy bounds as perception parameters. The contrast and entropy in the camera system are then adjusted based on the perception parameters.

Abstract: Described is a system for implicitly predicting movement of an object. 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 providing an image of a first trajectory to a predictive autoencoder, and using the predictive autoencoder, generating a predicted tactical response that comprises a second trajectory based on images of previous tactical responses that were used to train the predictive autoencoder, and controlling a device based on the predicted tactical response.