In one embodiment, a method of generating one or more coil geometries is provided. The method includes obtaining a set of input parameters to be used in conjunction with a set of basis functions. The method also includes performing an analysis of a set of coefficients of the set of basis functions based on the set of input parameters. The method further includes determining one or more coil geometries based on the analysis of the set of coefficients of the set of basis functions.
In one embodiment, a method is provided. The method includes storing a set of training data. One or more machine learning models are trained based on the set of training data. The method also includes receiving, from a computing device, a request to access the set of training data. The method further includes determining whether the computing device is allowed to access the set of training data. In response to determining that the computing device is allowed to access the set of training data, the method includes transmitting a training token to the computing device. The training token grants a training environment with access to the set of training data for a period of time.
G06K 9/62 - Methods or arrangements for recognition using electronic means
H04L 9/06 - Arrangements for secret or secure communications; Network security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
In one embodiment, a method includes obtaining candidate data generated by a vehicle. The candidate data comprises a subset of sensor data identified based on a set of neural network models executing on the vehicle. The method also includes determining whether the candidate data can be associated with one or more categories of a set of categories for training data based on a set of categorization models. The method further includes associating the candidate data with the first category in response to determining that the candidate data can be associated with at a first category of the set of categories. The method further includes determining whether the candidate data can be associated with a second category. The set of categories lacks the second category. The method further includes including the second category in the set of categories in response to determining that the candidate data can be associated with the second category. The method further includes associating the candidate data with the second category.
G06F 16/00 - Information retrieval; Database structures therefor; File system structures therefor
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06F 16/9035 - Filtering based on additional data, e.g. user or group profiles
G06F 16/901 - Indexing; Data structures therefor; Storage structures
Autonomous driving systems may be provided with one or more sensors configured to capture perception data, a model configured to be continually trained in the transportation vehicle and a scalable subset memory configured to store a subset of a dataset previously used to train a model. A processor may be provided for continually training the model in the transportation vehicle using captured perception data previously unseen and the subset and for generating a new subset of data to be stored so that the model avoids catastrophic forgetting.
Technologies and techniques for determining locations of a plurality of markers on a vehicle in three-dimensional space. The markers may be configured as optical or radio markers, where a sensor determines the locations of each of the plurality of markers, or a shape formed by the markers collectively. The locations of each of the plurality of markers or shape is then compared to a template to determine a match. The match identifies a vehicle having vehicle data that includes locations of vehicle components and/or vehicle performance characteristics. The vehicle data is then used to generate control signals for controlling a control device that may be associated with a robotic apparatus, a vehicle computer system and/or drone operating system.
G05D 1/02 - Control of position or course in two dimensions
G05D 1/00 - Control of position, course, altitude, or attitude of land, water, air, or space vehicles, e.g. automatic pilot
G06K 7/14 - Methods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
G07C 5/00 - Registering or indicating the working of vehicles
6.
Data science system for developing machine learning models
In one embodiment, a method is provided. The method includes receiving sensor data generated by a set of vehicles. The method also includes performing a first set of processing operations on the sensor data. The method further includes providing an exploration interface configured to allow one or more of browsing, searching, and visualization of the sensor data. The method further includes selecting a subset of the sensor data. The method further includes performing a second set of processing operations on the subset of the sensor data. The method further includes provisioning one or more of computational resources and storage resources for developing an autonomous vehicle (AV) model based on the subset of the sensor data.
A system and method are provided for observing conditions in an environment around a transportation vehicle and adjusting operation of the transportation vehicle in response to the observed conditions. The temperatures of features in the environment can be determined to compensate for varying conditions in the environment in operating the transportation vehicle.
A computing system comprises a data storage and at least one processor communicatively coupled to the data storage. The at least one processor is configured to execute program instructions to cause the system to perform the following steps. A deep neural network (“DNN”) model is trained using training data. Next, additional scenes are determined based on the DNN model and the training data. The determined scenes are generated, and then used to augment the training dataset. The DNN model is then retrained using the augmented training dataset and stored in a data storage for deployment.
H04W 36/14 - Reselecting a network or an air interface
G06V 20/58 - Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
A system, components, and methodologies are provided for image data processing and subsequent use to detect and/or identify objects and object movement in such image data to enable assistance, automation, control and/or documentation regarding transportation vehicle movement. An affine contour filter provides the ability to extract precise sub-pixel roots of contours that represent boundaries of blobs in an image that undergoes small affine changes such as translation, rotation and scale. Thereby lateral contour tracking may be performed wherein movement of an object may be tracked within the field of view of a camera by aligning the contours associated with the object in space-time. As a result, the size and shape of the object to be tracked need not be specified ahead of time.
G01B 11/16 - Measuring arrangements characterised by the use of optical techniques for measuring the deformation in a solid, e.g. optical strain gauge
C12Q 1/02 - Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving viable microorganisms
G01N 33/483 - Physical analysis of biological material
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
G06K 9/46 - Extraction of features or characteristics of the image
G06K 9/48 - Extraction of features or characteristics of the image by coding the contour of the pattern
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G06T 7/536 - Depth or shape recovery from perspective effects, e.g. by using vanishing points
H04N 5/232 - Devices for controlling television cameras, e.g. remote control
G06T 3/60 - Rotation of a whole image or part thereof
G06T 7/33 - Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
B60R 1/00 - Optical viewing arrangements; Real-time viewing arrangements for drivers or passengers using optical image capturing systems, e.g. cameras or video systems specially adapted for use in or on vehicles
G05D 1/02 - Control of position or course in two dimensions
A vehicle attachment for guiding a vehicle includes a magnetic tape sensor, at least one sensor, a controller, at least one coupling device for attachment to the vehicle, and a vehicle interface system. The magnetic tape sensor is configured to detect a path. The at least one sensor can detect whether there are any obstructions along the path. The controller is configured to steer the vehicle along the predefined path and to adjust the speed of the vehicle to avoid coming in contact with any obstructions. The vehicle interface system is configured to communicatively couple the controller to at least one electronic control unit of the vehicle.
A system, methodologies and components utilizing a random access scanning LiDAR sensor for object detection for use in autonomous and driver assisted transportation vehicles, wherein calculated expected potential range changes determined based on emission of range determining laser pulses are used to schedule subsequent emission of range determining laser pulses such that laser pulse generation and ray sample generation for individual rays with a larger expected potential range change is performed more frequently than laser pulse generation and ray sample generation for individual rays with a smaller expected potential change so as to provide compressive active range scanning.
Devices, systems, and methodologies for providing a comfort system of a transportation vehicle include capturing and assessing movement data of a user. The movement data can include movement data captured upon the user's approach to the transportation vehicle.
B60N 2/02 - Seats specially adapted for vehicles; Arrangement or mounting of seats in vehicles the seat or part thereof being movable, e.g. adjustable
B60R 16/037 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric for occupant comfort
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
13.
Optimized differential evolution for radio frequency trilateration in complex environments
A system and method are provided for controlling traffic signals using virtual induction loops. The system allows bidirectional communication between a traffic signal controller and a vehicle so that the controller can send map data to the vehicle and the vehicle can send a recall message to the controller, requesting to be served by the controller at an approaching traffic signal.
A system and methodologies for controlling the content of a heads up display to provide increased visibility of naturally occurring contours and surfaces in the environment is provided. Depth information is provided along with predefined lighting conditions in order to output an image by the HUD with enhanced feature contours.
Autonomous driving systems may be provided with one or more sensors configured to capture perception data, a model configured to be continually trained in the transportation vehicle and a scalable subset memory configured to store a subset of a dataset previously used to train a model. A processor may be provided for continually training the model in the transportation vehicle using captured perception data previously unseen and the subset and for generating a new subset of data to be stored so that the model avoids catastrophic forgetting.
A system and method are provided for controlling traffic signals using virtual induction loops. The system allows bidirectional communication between a traffic signal controller and a vehicle so that the controller can send map data to the vehicle and the vehicle can send a recall message to the controller, requesting to be served by the controller at an approaching traffic signal.
G08G 1/13 - Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles to a central station the indicator being in the form of a map
G08G 1/01 - Detecting movement of traffic to be counted or controlled
18.
Vector engine and methodologies using digital neuromorphic (NM) data
A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.
Systems, components, and methodologies are provided for improvements in operation of automotive vehicles by enabling monitoring analysis and reaction to subtle sources of information that aid in prediction and response of vehicle control systems across a range of automation levels. Such systems, components, and methodologies include wheel-turn detection equipment for detecting a wheel angle of another vehicle to trigger a vehicle control system to perform an operation based on the detected wheel angle of the other vehicle.
B60R 16/02 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric
H02K 9/00 - Arrangements for cooling or ventilating
H02H 7/08 - Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from norm for dynamo-electric motors
H02H 6/00 - Emergency protective circuit arrangements responsive to undesired changes from normal non-electric working conditions using simulators of the apparatus being protected, e.g. using thermal images
B60L 3/00 - Electric devices on electrically-propelled vehicles for safety purposes; Monitoring operating variables, e.g. speed, deceleration or energy consumption
B60L 58/26 - Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries for controlling the temperature of batteries by cooling
B60L 50/64 - Constructional details of batteries specially adapted for electric vehicles
20.
System and methodologies for occupant monitoring utilizing digital neuromorphic (NM) data and fovea tracking
A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking so as to detect and predict movement of a vehicle occupant.
Systems, devices and methodologies for generating a vehicle identification hash value and verifying the integrity of the vehicle. The vehicle identification hash value is generated based on hashes provided by each vehicle component. The generated overall vehicle identification hash value may be dynamic and reflects changes that occur to the vehicle at the component level.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
H04L 9/32 - Arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system
H04W 4/48 - Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for in-vehicle communication
H04L 29/06 - Communication control; Communication processing characterised by a protocol
B60R 16/023 - Electric or fluid circuits specially adapted for vehicles and not otherwise provided for; Arrangement of elements of electric or fluid circuits specially adapted for vehicles and not otherwise provided for electric for transmission of signals between vehicle parts or subsystems
H04L 29/08 - Transmission control procedure, e.g. data link level control procedure
H04L 29/12 - Arrangements, apparatus, circuits or systems, not covered by a single one of groups characterised by the data terminal
Systems, components, and methodologies are provided for improvements in operation of automotive vehicles by enabling emulation of traffic signal operation by genertic algorithms, providing tunable solutions for efficient and safe operation.
G01S 1/02 - Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith using radio waves
G08G 1/096 - Arrangements for giving variable traffic instructions provided with indicators in which a mark progresses showing the time elapsed, e.g. of green phase
A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.
A system and methodologies for neuromorphic (NM) vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.
G06K 9/00 - Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
G05D 1/02 - Control of position or course in two dimensions
G06T 7/246 - Analysis of motion using feature-based methods, e.g. the tracking of corners or segments
B60W 30/00 - Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.
A system and methodologies for neuromorphic vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.
A system and methodologies for neuromorphic (NM) vision simulate conventional analog NM system functionality and generate digital NM image data that facilitate improved object detection, classification, and tracking.
patents
