Abstract: The Intelligent Information Conversion System (IICS) facilitates real-time dynamic information exchange among connected and automated vehicle (CAV), roadside intelligent unit (RIU), and cloud platform. The system comprises a codebook, coding module, connector module, and supporting system. The codebook provides a standardized format for information exchange, using a sequence of integers corresponding to various categories such as vehicle automation level, vehicle type, and road category. The coding module encodes and decodes information to enable seamless communication among CAV, RIU, and cloud platform, optimizing data transmission and service levels for autonomous driving. The system supports sorting, encoding, and decoding information into a codebook string, improving real-time interaction and information flow across connected environments. It enhances vehicle automation and supports dynamic, context sensitive data exchanges between different entities in the autonomous ecosystem.

Abstract: Provided herein is technology relating to aspects of a Distributed Driving System (DDS) for managing Connected and Automated Vehicles (CAV) and particularly, but not exclusively, to systems, designs, and methods for a Device Allocation System (DAS) configured to allocate and distribute resources to devices of a Distributed Driving Systems (DDS).

Abstract: Provided herein is a technology for an Autonomous Vehicle Cloud System (AVCS). This AVCS provides sensing, data fusion, prediction, decision-making, and/or control instructions for specific vehicles at a microscopic level based on data from one or more of other vehicles, roadside unit (RSU), cloud-based platform, and traffic control center/traffic control unit (TCC/TCU). Specifically, the AVs can be effectively and efficiently operated and controlled by the AVCS. The AVCS provides individual vehicles with detailed time-sensitive control instructions for fulfilling driving tasks, including car following, lane changing, route guidance, and other related information. The AVCS is configured to predict individual vehicle behavior and provide planning and decision-making at a microscopic level. 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: Provided herein is a technology for an Autonomous Vehicle Intelligent System (AVIS), which facilitates vehicle operations and control for autonomous driving. 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 an onboard unit (OBU), wherein the OBU comprises a communication module communicating with one or more of other autonomous vehicles (AV), a roadside unit (RSU), a cloud platform, and a traffic control center/traffic control unit (TCC/TCU). 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 a computing power allocation system for autonomous driving (CPAS-AD), which is a component of an Intelligent Road Infrastructure System (IRIS). The CPAS-AD incorporates advanced computing capabilities that effectively allocate computational power for sensing, prediction, planning, decision-making, and control functions to enable end-to-end driving functions. In addition to the vehicle, the CPAS-AD can acquire additional computation resources from one or more of: (a) a roadside unit (RSU) network, (b) a cloud platform, (c) a traffic control center/traffic control unit (TCC/TCU), and (d) a traffic operations center (TOC). Additionally, tailored to different traffic scenarios, the CPAS-AD can allocate data and computation resources (including but not limited to CPU and GPU) for vehicle sensing, prediction, planning, decision-making, and control functions, thereby enabling safe and efficient autonomous driving.

Abstract: The invention provides systems and methods for a function-based computing power allocation system (FCPAS), which is a component of an Intelligent Road Infrastructure System (IRIS). The FCPAS incorporates advanced computing capabilities that effectively allocate computational power for prediction, planning, and decision making functions. Specifically, through the FCPAS, an AV can acquire additional computational resources for vehicle prediction, planning, and decision-making functions, thereby enabling safe and efficient autonomous driving. Additionally, tailored to different traffic scenarios, the FCPAS can allocate data and computational resources (including but not limited to CPU and GPU) for vehicle automation.

Abstract: The technology provided herein relates to a roadside infrastructure sensing system for Intelligent Road Infrastructure Systems (IRIS) and, in particular, to devices, systems, and methods for data fusion and communication that provide proactive sensing support to connected and automated vehicle highway (CAVH) systems.

Abstract: Provided herein is technology relating to automated driving and particularly, but not exclusively, to an intelligent information conversion system and related methods for providing collaborative automatic driving to intelligent transportation systems, vehicle networking systems, collaborative management control systems, vehicle-road collaborative systems, automated driving systems, and the like.

Abstract: The disclosure relates to an antenna for a glass roof of a vehicle. The antenna can comprise a radiator for radiating electromagnetic waves. The radiator can be configured to be disposed between two glass layers of the glass roof. The radiator can be configured for feeding a signal to a receiver. The antenna can be substantially flat.

Abstract: The invention provides a roadside computing system (RCS), or an edge computing system, for an autonomous vehicle. The RCS comprises a hierarchy of roadside unit (RSU) and an onboard unit (OBU) in an individual vehicle. The RSU comprises a data processing module and a communication module, and is capable of generating guidance information and targeted instructions for individual vehicle. The data processing module of the RSU comprises two processors: an External Object Calculating Module (EOCM) and an AI processing unit. Thus, the RCS utilizes roadside edge computing power and AI models to support autonomous driving for the vehicle. The OBU comprises a data processing module, a communication module, and a vehicle control module, and is capable of generating vehicle-specific targeted instruction for the vehicle based on guidance information and targeted instructions received from RSUs, and controlling the vehicle based on vehicle-specific targeted instruction.

Abstract: This invention presents a function allocation system for an autonomous vehicle (AV). During the operations of the AV, some or all of its automated driving capabilities or functions could be downgraded due to long-tail events or malfunctioning. The roadside intelligent infrastructure, or the cloud platform, could supplement some or all of AV's automated driving functions, including sensing, prediction and decision-making, and/or control functions. The function allocation system dynamically allocates these functions between AV and intelligent infrastructure, achieving a higher system intelligence level S than the downgraded vehicle intelligence level V. In addition, a function allocation system could dynamically allocate sensing, prediction and decision-making, and/or control functions between AV and a cloud platform. This invention also presents a function integration system or a fusion system for an AV.

Abstract: This invention presents an automated driving system with distributed computing (ADS-DC). During the operation of a connected automated vehicle (CAV), some or all of its automated driving capabilities for sensing, prediction, planning, decision-making, or control may be downgraded due to long-tail events or malfunctions. The intelligent roadside toolbox (IRT) functions as an edge server or a cloud, and can supplement CAV's sensing functions, prediction and management functions, planning and decision-making functions, and vehicle control functions by providing customized, on-demand, and dynamic computing resources and functions to the CAV. In addition, the IRT computing functions provide the computation support for sensing, prediction, planning, decision-making, and/or control functions of said CAV. Namely, the IRT functions as an edge server or a cloud to provide processing, training or optimization of CAV driving models as well as facilitate the implementation of the driving models in the CAV.

Abstract: This technology relates to autonomous vehicle (AV) control systems tailored for critical points of a partially instrumented infrastructure. The first system integrates an onboard unit (OBU) and communication modules to interact with roadside units (RSUs) or a cloud platform, delivering time-sensitive control instructions to vehicles at critical points. The OBUs execute these instructions for driving tasks. The second system combines an OBU with a cloud platform, leveraging cloud services for enhanced functionality like storage and computing. It adapts to diverse critical point scenarios, employing proactive incident prediction and rapid detection methods for optimized performance.

Abstract: The invention provides a vehicle AI computing system (VACS) that supports autonomous driving through an Onboard Unit (OBU) for vehicle-based computing and distributed computing based on vehicle road-cloud. The vehicle-based computing can effectively complete various computational tasks by using onboard computing resources. The distributed computing allows the vehicle to work in collaboration with roadside units (RSUs) and/or the cloud to effectively complete various computational tasks. The VACS features an OBU with a sensing module, a communication module, and a data processing module that integrates data from vehicle sensors, RSUs, and the cloud. The OBU also includes a vehicle control module that helps control the vehicle based on the data of RSU and cloud. The VACS leverages high performance computation resources to implement end to end driving tasks including sensing, prediction, planning and decision making, and control.

Abstract: The invention provides an autonomous vehicle (AV) system with an artificial intelligence (AI) system for automated vehicle control and traffic operations. This AI system comprises a computation component configured to provide sensing, behavior prediction and management, decision making, and vehicle control for the vehicle. This AI system is configured to receive local knowledge, information, data, and models from a roadside unit (RSU) or a cloud to improve performance and efficiency of the vehicle. The AI system is configured to train models with heuristic parameters obtained from a local traffic control center/traffic control unit (TCC/TCU) or the cloud to provide an improved model. The AI system is configured to provide intelligence coordination to distribute intelligence among vehicles, RSUs and cloud. The system also provides localized self-evolving artificial intelligence.

Abstract: Provided herein is technology related to a distributed driving system (DDS) by using flexible, on-demand, and customized resources and functions from an intelligent roadside toolbox (IRT). These resources comprise computational resources, cloud resources, system security resources, backup and redundancy resources. The functions comprise sensing, transportation behavior prediction and management, planning and decision-making, and vehicle control functions. The DDS and IRT technologies described herein are vehicle oriented, modular, and customizable for each vehicle to meet the specific needs of each individual vehicle as an on-demand and dynamic service. The DDS is configured to provide customized, on-demand, and dynamic IRT resources and functions to individual CAVs to supplement the CAV's sensing, transportation behavior prediction and management, planning and decision-making, and/or vehicle control.

Abstract: The technology described herein provides Automated Driving System (ADS) methods and systems for coordinating and/or fusing intelligence and functions between Connected Automated Vehicles (CAV) and ADS infrastructure to provide target levels of automated driving. The technology provides systems and methods for function allocation comprising sensing allocation, prediction and decision-making allocation, and control allocation. The ADS operates across various intelligence levels, identified during vehicle operation. The function allocation system dynamically allocates essential functions based on the intelligence levels of both vehicles and infrastructures, ensuring that ADS achieves a system intelligence that surpasses that of individual components. This methodical function distribution enables ADS to manage both vehicles and infrastructures in a manner that enhances vehicular operations and control.

Abstract: The invention provides systems and methods for an autonomous vehicle and cloud control system comprising an autonomous vehicle (AV) control system and a cloud-based platform, which are two components of an Intelligent Road Infrastructure System (IRIS). This integrated vehicle-cloud system provides sensing, prediction, decision-making, and control instructions for specific vehicles at a microscopic level. Specifically, through the system, AVs can be effectively and efficiently controlled by AV itself and/or by the cloud. The AV and cloud control system provides individual vehicles with detailed time-sensitive control instructions for vehicles to fulfill driving tasks. In addition, the cloud-based platform is configured to predict behavior of individual vehicles and provide planning and decision making at a microscopic level from 1 to 10 milliseconds, which is critical for AV operations.

Abstract: The invention provides systems and methods for an autonomous vehicle (AV) control system comprising an onboard unit (OBU) and a roadside unit (RSU), which are two components of an Intelligent Road Infrastructure System (IRIS). This integrated vehicle-road system provides sensing, prediction, decision-making, and control instructions for specific vehicles at a microscopic level. Specifically, through the AV control system, an AV can be effectively and efficiently controlled by the AV itself and/or by the RSU. The AV control system provides individual vehicles with detailed time-sensitive control instructions for vehicles to fulfill driving tasks. In addition, the RSU conducts behavior prediction for individual vehicles at a microscopic level from 1 to 10 milliseconds, which is critical for connected and automated vehicle (CAV) operations.

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.