Abstract: Provided herein is technology relating to connected and automated highway systems and particularly, but not exclusively, to systems and methods for providing localized self-evolving artificial intelligence for intelligent road infrastructure systems.

Abstract: This invention provides a system-oriented and fully-controlled connected automated vehicle highway system for various levels of connected and automated vehicles and highways. The system comprises one or more of: 1) a hierarchical traffic control network of Traffic Control Centers (TCC's), local traffic controller units (TCUs), 2) A RSU (Road Side Unit) network (with integrated functionalities of vehicle sensors, I2V communication to deliver control instructions), 3) OBU (On-Board Unit with sensor and V2I communication units) network embedded in connected and automated vehicles, and 4) wireless communication and security system with local and global connectivity. This system provides a safer, more reliable and more cost-effective solution by redistributing vehicle driving tasks to the hierarchical traffic control network and RSU network.

Abstract: The present technology relates to an intelligent road infrastructure system and, more particularly, to systems and methods for a heterogeneous connected automated vehicle highway (CAVH) network in which the road network has various RSU and TCU/TCC coverages and functionalities. The heterogeneous CAVH network facilitates control and operations for vehicles of various automation level and other road users by implementing various levels of coordinated control among CAVH system entities and providing individual road users with detailed customized information and time-sensitive control instructions, and operations and maintenance services.

Abstract: This invention provides a system-oriented and fully-controlled connected automated vehicle highway system for various levels of connected and automated vehicles and highways. The system comprises one or more of: 1) a hierarchical traffic control network of Traffic Control Centers (TCC's), local traffic controller units (TCUs), 2) A RSU (Road Side Unit) network (with integrated functionalities of vehicle sensors, I2V communication to deliver control instructions), 3) OBU (On-Board Unit with sensor and V2I communication units) network embedded in connected and automated vehicles, and 4) wireless communication and security system with local and global connectivity. This system provides a safer, more reliable and more cost-effective solution by redistributing vehicle driving tasks to the hierarchical traffic control network and RSU network.

Abstract: This invention provides a system-oriented and fully-controlled connected automated vehicle highway system for various levels of connected and automated vehicles and highways. The system comprises one or more of: 1) a hierarchical traffic control network of Traffic Control Centers (TCC's), local traffic controller units (TCUs), 2) A RSU (Road Side Unit) network (with integrated functionalities of vehicle sensors, I2V communication to deliver control instructions), 3) OBU (On-Board Unit with sensor and V2I communication units) network embedded in connected and automated vehicles, and 4) wireless communication and security system with local and global connectivity. This system provides a safer, more reliable and more cost-effective solution by redistributing vehicle driving tasks to the hierarchical traffic control network and RSU network.

Abstract: Provided herein is a technology for an Autonomous Vehicle Intelligent System (AVIS), which facilitates vehicle operations and control for connected automated vehicle highway (CAVH) systems. The AVIS and related methods provide vehicles with vehicle-specific information for a vehicle to perform driving tasks such as car following, lane changing, and route guidance. The AVIS comprises: (1) a communication module communicating with one or more of: (a) other autonomous vehicles (AV), (b) a roadside unit (RSU), (c) a cloud platform, or (d) a traffic control center/traffic control unit (TCC/TCU); and (2) an onboard unit (OBU) of an AV. The AVIS implements one or more of the following functions: sensing, prediction, decision-making, and vehicle control using onboard information and vehicle-specific information received from other AVs, the RSU, the cloud platform, and/or the TCC/TCU.

Abstract: The invention provides systems and methods for an Intelligent Road Infrastructure System (IRIS), which facilitates vehicle operations and control for connected automated vehicle highway (CAVH) systems. IRIS systems and methods provide vehicles with individually customized information and real-time control instructions for vehicle to fulfill the driving tasks such as car following, lane changing, and route guidance. IRIS systems and methods also manage transportation operations and management services for both freeways and urban arterials. In some embodiments, the IRIS comprises or consists of one of more of the following physical subsystems: (1) Roadside unit (RSU) network, (2) Traffic Control Unit (TCU) and Traffic Control Center (TCC) network, (3) vehicle onboard unit (OBU), (4) traffic operations centers (TOCs), and (5) cloud information and computing services.

Abstract: Provided herein is a technology for an autonomous vehicle cloud system (AVCS). The AVCS provides sensing, prediction, decision-making, and/or control instructions for specific vehicles at a microscopic level using data from one or more of other vehicles, a roadside unit, cloud-based platform, or traffic control center/traffic control unit. Specifically, the autonomous vehicles are effectively and efficiently operated and controlled by the AVCS. The AVCS provides individual vehicles with detailed time-sensitive control instructions for vehicles to fulfill driving tasks. The AVCS is configured to predict individual vehicle behavior and provide planning and decision-making at a microscopic level to improve AV operations. In addition, the AVCS is configured to provide one or more of virtual traffic light management, travel demand assignment, traffic state estimation, and platoon control.

Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. A sparsity-inducing regularization optimization process is performed on a machine learning model to generate a compressed machine learning model. A machine learning model is trained using a first set of training data. A sparsity-inducing regularization optimization process is executed on the machine learning model. Based on the sparsity-inducing regularization optimization process, a compressed machine learning model is received. The compressed machine learning model is executed to generate one or more outputs.

Abstract: The invention provides systems and methods for an Intelligent Road Infrastructure System (IRIS), which facilitates vehicle operations and control for connected automated vehicle highway (CAVH) systems. IRIS systems and methods provide vehicles with individually customized information and real-time control instructions for vehicle to fulfill the driving tasks such as car following, lane changing, and route guidance. IRIS systems and methods also manage transportation operations and management services for both freeways and urban arterials. In some embodiments, the IRIS comprises or consists of one of more of the following physical subsystems: (1) Roadside unit (RSU) network, (2) Traffic Control Unit (TCU) and Traffic Control Center (TCC) network, (3) vehicle onboard unit (OBU), (4) traffic operations centers (TOCs), and (5) cloud information and computing services.

Abstract: Provided herein is technology related to a distributed driving system (DDS) that provides transportation management and operations and vehicle control for connected and automated vehicles (CAV) and intelligent road infrastructure systems (IRIS) and particularly, but not exclusively, to methods and systems for sending individual vehicles with customized, detailed, and time-sensitive control instructions and traffic information for automated vehicle driving, such as vehicle following, lane changing, route guidance, and other related information

Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. A sparsity-inducing regularization optimization process is performed on a machine learning model to generate a compressed machine learning model. A machine learning model is trained using a first set of training data. A sparsity-inducing regularization optimization process is executed on the machine learning model. Based on the sparsity-inducing regularization optimization process, a compressed machine learning model is received. The compressed machine learning model is executed to generate one or more outputs.

Abstract: Provided herein is technology related to a distributed driving system (DDS) that provides transportation management and operations and vehicle control for connected and automated vehicles (CAV) and intelligent road infrastructure systems (IRIS) and particularly, but not exclusively, to methods and systems for sending individual vehicles with customized, detailed, and time-sensitive control instructions and traffic information for automated vehicle driving, such as vehicle following, lane changing, route guidance, and other related information.

Abstract: The technology provides systems and methods for a system providing customized and route-specific operations, control, and services for connected and automated vehicles (CAVs) according to user origin and destination requests, based on connected automated vehicle highway (CAVH) systems which includes an intelligent road infrastructure system providing transportation management and operations and individual vehicle control for CAV.

Abstract: The invention provides systems and methods for an autonomous vehicle (AV) and cloud control system, which comprises an AV control system comprising an onboard unit (OBU), a roadside unit (RSU) network, and a cloud-based platform. This integrated vehicle-road-cloud system provides traffic state estimation, prediction, computing, and control instructions for specific vehicles by the cloud-based platform. The cloud-based platform also provides safety and efficiency measures for vehicle operations and control under adverse weather conditions, and provides security, redundancy, and resiliency measures to improve system reliability. In addition, the RSU network provides high-resolution maps for specific vehicles.

Abstract: As described herein, a N-Gram index may be created and the search may be conducted using the index, which will lead to faster search results. The N-Gram index may also include partial N-Gram components to capture more relevant data. Moreover, as described herein, the search may also take into account recent log data that has not yet been indexed. Techniques for building an index store using log data and efficiently searching the index store and log data to process search requests are described herein.

Abstract: Techniques for executing show commands are described herein. A plurality of navigation steps is utilized, each navigation step corresponding to a different layer in a database structure and each navigation step including an operator to fetch items from a metadata database up to respective bounded limits. Dependency information is also fetched for objects of the specified object type in the show command. After a set of objects from the last layer are processed, memory for the navigation steps is flushed and the next set of objects are processed.

Abstract: Non-limiting examples of systems, methods, and devices for automatic triaging of diagnostic failures for heterogeneous groups of tenants of a Software-as-a-Service, multi-tenant environment are disclosed herein. In an implementation, telemetry data for the heterogeneous groups of tenants is analyzed to classify individual tenant failures and detect the health status of the individual tenant. Tenant failures and/or tenant health statuses are filtered according to a threshold level. Anomalies having metrics that meet or exceed the threshold level are further analyzed to determine their priority (e.g., to a specific tenant). If the anomalies are known, then an existing entry for the anomaly is tagged and its priority may be changed. If the anomalies are unknown, then an entry is generated for the anomaly and prioritized. Tenants may be notified of a detected anomaly and may provide feedback. The feedback may be used to update triaging models.

Abstract: Systems and methods for performing spatial line grouping on digital ink stokes. The system includes an electronic processor configured to access a set of hypothetical lines in an electronic document and determine a set of hypothetical line pairings. The electronic processor is also configured to determine, via a gradient boosting tree model, a merge confidence score for each hypothetical line pairing and compare a first merge confidence score with a merge threshold. The first merge confidence score is associated with a first hypothetical line and a first neighboring hypothetical line. The electronic processor is also configured to, in response to the first merge confidence score satisfying the merge threshold, merge the first hypothetical line and the first neighboring hypothetical line to form a first line grouping. The electronic processor is also configured to perform a digital ink stroke analysis on the electronic document based on the first line grouping.

Abstract: Techniques for executing show commands are described herein. A plurality of navigation steps is utilized, each navigation step corresponding to a different layer in a database structure and each navigation step including an operator to fetch items from a metadata database up to respective bounded limits. Dependency information is also fetched for objects of the specified object type in the show command. After a set of objects from the last layer are processed, memory for the navigation steps is flushed and the next set of objects are processed.