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 present invention provides a model training system, a model training method, a training device, and a training node. The system includes: n training devices, in which a total of M training nodes are deployed. Each training device stores a global index list and a dataset corresponding to a training node deployed in the training device, and the global index list indicates indexes of data in datasets respectively corresponding to the M training nodes. Each training device is configured to: shuffle the global index list by using a random number seed, and query, based on a shuffled list, the dataset stored in the training device, to obtain exchange data of the training node deployed in the training device, where the exchange data is hit data obtained through data query.

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: A road network balanced drainage method aimed at reducing urban waterlogging is disclosed. According to the drainage demand, the existing intersection elevation is changed or the intersection elevation that meets the drainage requirement is designed and implemented in new construction so that the intersection drainage is distributed according to the desired proportion; the water flowing to the water accumulation position is transferred to reduce the waterlogging degree. Through the analysis of the depth of water accumulation in the whole road network, the method of water flow distribution at the intersection is used to make the precipitation flow evenly distributed in the whole road network. The method can be used for solving serious water accumulation of a certain road section, the balanced drainage of the whole road network, the technical transformation of existing intersections, and for the planned and designed roads. The method can effectively eliminate and prevent urban waterlogging.

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: Provided herein is an artificial intelligence-based mobile roadside intelligent unit (MRIU) for providing, supplementing, and/or enhancing the control and operation of autonomous vehicles in normal and long-tail scenarios. The MRIU comprises a computing module configured to provide supplemental computation capability for autonomous driving. The MRIU comprises a communication module to communicate and exchange data with a vehicle or a cloud. The MRIU provides prediction, decision-making, and/or control functions for autonomous driving. The MRIU provides edge computing capability for autonomous vehicles to train and operate artificial intelligence-based intelligent driving models in a distributed fashion. Specifically, an edge computing unit conducts data fusion and data feature extraction, provides prediction, formulates control strategies, generates vehicle control instructions, and/or distributes vehicle control information and/or instructions for an autonomous vehicle.

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: 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: Provided herein is technology relating to automated driving and particularly, but not exclusively, to a mobile intelligent road infrastructure technology configured to serve automated driving systems by providing, supplementing, and/or enhancing autonomous driving functions for connected automated vehicles under common and unusual driving scenarios.

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 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: A multilayer structure comprising regions of higher aluminum (Al) composition as compared to adjacent layers, in combination with an undulating active region and controlled buffer layer crystal quality, promotes radiative recombination and improves the performance and efficiency of ultraviolet (UV) or far-UV light-emitting diodes (LEDs), laser diode (LDs), or other light emitting devices.

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: Provided herein is technology relating to automated driving and particularly, but not exclusively, to a connected reference marker technology configured to serve automated driving systems by providing, supplementing, and/or enhancing autonomous driving functions for connected automated vehicles under normal and abnormal driving scenarios.

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.