Hrl Laboratories, LLC

Gm Global Technolgoy Operations, Inc.

Raytheon Company

Hughes Electronics Corporation

Hughes Aircraft Company

The Boeing Company

Xlabs Holdings, LLC

Michael J Daily

Rajan Bhattacharyya

David W Payton

Michael D Howard

Hyukseong Kwon

Ronald Tadao Azuma

Howard Neely, Iii

Kevin R Martin

Craig A Lee

Kyungnam Kim

Gavin D Holland

Tiffany J Hwu

Deepak Khosla

Yang Chen

Heiko Hoffmann

Rashmi N Sundareswara

Jaehoon Choe

Terrell Nathan Mundhenk

Matthew E Phillips

Kenji Yamada

Youngkwan Cho

A Arturo Flores

Henry Lu

Michael O'Neal

Cheryl Hein

Nandan Thor

Dylan T Bergstedt

Yuri Owechko

Narayan Srinivasa

Swarup S Medasani

Praveen K Pilly

Qin Jiang

Karim El Defrawy

Jiejun Xu

Shinko Yuanhsien Cheng

Aruna Jammalamadaka

Alireza Esna Ashari Esfahani

Amit Agarwal

Richard Michael Kremer

Mike Howard

Jon Leonard

Jason Fox

Iman Mohammadrezazadeh

Rodolfo Valiente Romero

Robert Belvin

Jerry Isdale

Gerald B Isdale

Robert S Belvin

Ronald R Burns

Tamara Lacker

Divya P Kulkarni

Robert L Seliger

Darren Michael Chan

Joshua Lampkins

Alan Perry

Ronnie R Burns

Terrell N Mundhenk

Howard Neely Iii Neely

Howard E Neely

Jon N Leonard

Ronald T Azuma

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.

Scalable road sign interpretation system for autonomous driving

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.

3D object detection method using synergy of heterogeneous sensors for autonomous driving

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.

Virtual lane estimation using a recursive self-organizing map

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.

Methods for cognitive situation awareness using an attention-based event structure

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.

Unsupervised velocity prediction and correction for urban driving entities from sequence of noisy position estimates

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.

Navigation trajectory using reinforcement learning for an ego vehicle in a navigation network

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.

System, method and controller for graph-based path planning for a host vehicle

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.

Hyperassociation in episode memory

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.

System and method to cue specific memory recalls while awake

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.

Method and apparatus for autonomous system performance and grading

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.

Systems and methods for predicting entity behavior

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.

Efficient situational awareness by event generation and episodic memory recall for autonomous driving systems

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.

Method and apparatus for scenario generation and parametric sweeps for the development and evaluation of autonomous driving systems

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.

Training system for infield training of a vision-based object detector

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.

System and method for robot supervisory control with an augmented reality user interface

Growing community of inventors

Thousand Oaks, CA, United States of America

Michael J Daily