Abstract: A road sign interpretation system includes a front-facing camera mounted on or in a vehicle collecting image data of multiple road signs. A first convolutional neural network (CNN) receives the image data from the front-facing camera and yields a set of sign predictions including one or more sign text instances. A second CNN defining a text extractor receives the image data from the front-facing camera and extracts text candidates including the multiple sign text instances. Sign and sign data localization is provided in the second CNN to compute a text order from the multiple sign text instances. A sign text synthesizer module receives individual sign text instances from the first CNN and individual ones of the sign text instances in digitized forms from an optical character recognizer (OCR). A semantic encoding and interpretation module receives the sign text instances and identifies semantics of the multiple road signs.

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: 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 using unsupervised velocity prediction and correction for urban driving from sequences of noisy position estimates includes: performing a vehicle velocity prediction for one or more other vehicles in a vicinity of a host automobile vehicle; calculating a first heuristic based on a uniformity test; calculating a second heuristic based on a vehicle speed of the one or more other vehicles; combining the first heuristic and the second heuristic using a weighted sum; determining an uncertainty mask applying the combined first heuristic and the second heuristic and a heuristic threshold; and applying the uncertainty mask to identify a velocity correction for use by the host automobile vehicle.

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: A method for performing object detection during autonomous driving includes: performing 3D object detection in a 3D object detection segment; uploading an output of multiple sensors in communication with the 3D object detection segment to multiple point clouds; transferring point cloud data from the multiple point clouds to a Region Proposal Network (RPN); independently performing 2D object detection in a 2D object detector in parallel with the 3D object detection in the 3D object detection segment; and taking a given input image and simultaneously learning box coordinates and class label probabilities in a 2D object detection network operating to treat object detection as a regression problem.

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 using unsupervised velocity prediction and correction for urban driving from sequences of noisy position estimates includes: performing a vehicle velocity prediction for one or more other vehicles in a vicinity of a host automobile vehicle; calculating a first heuristic based on a uniformity test; calculating a second heuristic based on a vehicle speed of the one or more other vehicles; combining the first heuristic and the second heuristic using a weighted sum; determining an uncertainty mask applying the combined first heuristic and the second heuristic and a heuristic threshold; and applying the uncertainty mask to identify a velocity correction for use by the host automobile vehicle.

Abstract: A method for monitoring uncertainty for human-like behavioral modulation of trajectory planning includes: retrieving map and agent information of a current driving state of an autonomously operated host automobile vehicle; dividing uncertainty conditions affecting a trajectory of the host automobile vehicle into an expected uncertainty and an unexpected uncertainty; calculating the expected uncertainty in a first operation branch by forming attention zones according to identified portions of lanes which may potentially collide with a planned route of the host automobile vehicle; determining the unexpected uncertainty in a second operation branch by calculating an anomaly score for any other vehicles in a surrounding area of the host automobile vehicle positioned in the lanes which may potentially collide with the planned route of the host automobile vehicle; and modulating trajectory operation signals determined for the expected uncertainty if the unexpected uncertainty meets or exceeds a predetermined thresh

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 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: 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 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: 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: An autonomous vehicle, system and method of operating the autonomous vehicle. The system includes a performance evaluator, a decision module and a navigation system. The performance evaluator determines a performance grade for each of a plurality of decisions for operating the autonomous vehicle. The decision module selects a decision have a greatest performance grade. The navigation system operates the autonomous vehicle using the selected decision.

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: 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: 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: 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.