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: The invention provides an autonomous vehicle and intelligent control (AVIC) system with distributed AI computing, which is a component of a Connected Automated Vehicle Highway (CAVH) system. This AVIC system is configured to realize training and application for distributed AI computing by providing automated vehicles (AVs) and/or connected automated vehicles (CAVs) with vehicle-specific control instructions comprising instructions for vehicle longitudinal and lateral position, speed, and steering and control. Detailed and time-sensitive vehicle-specific control instructions comprise control instructions for speed, spacing, lane designation, vehicle following, lane changing, and/or route guidance. Specifically, through the system, CAVs can be effectively and efficiently controlled by the AVIC system.

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 is a communication method. The method includes: receiving, by a first station (STA), a block acknowledgement (BA) frame, wherein the BA frame carries first information and/or protected privacy-related information, the first information being configured to identify the protected privacy-related information.

Abstract: Provided herein is technology relating to intelligent transportation systems and automated vehicles and particularly, but not exclusively, to function allocation systems and methods for a connected automated vehicle highway system that provides transportation management and operations and vehicle control for connected and automated vehicles.

Abstract: Provided is a communication method. The method includes: receiving, by a first station (STA), a control frame carrying first information and/or second information, wherein the first information is configured to identify the second information, and the second information comprises protected information.

Abstract: A polyethylene glycol-drug conjugate and a use thereof, specifically relating to a polyethylene glycol-drug conjugate as shown in formula A, a stereoisomer thereof or a pharmaceutically acceptable salt thereof; an intermediate for the preparation of the polyethylene glycol-drug conjugate, the stereoisomer thereof or the pharmaceutically acceptable salt thereof; a pharmaceutical composition containing the polyethylene glycol-drug conjugate, the stereoisomer thereof or the pharmaceutically acceptable salt thereof; and a use of the polyethylene glycol-drug conjugate, the stereoisomer thereof or the pharmaceutically acceptable salt thereof in the preparation of a drug.

Abstract: This technology provides an Autonomous Vehicle (AV) Intelligent Control System (ICS) that integrates a sensing module for collecting driving environment data and an onboard unit (OBU) for vehicle control. The OBU includes a vehicle control module and communication modules for interacting with Traffic Control Centers (TCC/TCU) and Roadside Units (RSUs). These modules exchange information at the control level, such as vehicle positioning, speed, and environmental conditions, enabling the AV to operate safely and efficiently. The system also features redundancy through dual-security mechanisms, enhancing safety and reliability. The AV ICS is designed to distribute driving tasks and functions, utilizing both TCC/TCU and RSU data for vehicle control and monitoring, with wireless communication capabilities for seamless information exchange.

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: 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: Provided is a remote control device based on computer vision technology, the remote control device includes a main body and a stand base connected with the main body. The main body includes a control unit, an output unit, a power supply unit, and a switch at least partially enclosed by a case. The control unit includes a visual sensor that collects image information and a microcontroller that outputs a control instruction to a controlled device via the output unit according to the image information collected by the visual sensor. The power supply unit is configured to supply power to the control unit and the output unit. The switch is configured to control the working state of the remote control device.

Abstract: Aspects of the present invention allow for real-time people monitoring method and system for estimation of the size and flow density of a given group of people located in a given area of space. The method and system may be used to monitor live or recorded camera input, foreground segmentation, human tracking, height estimation, and dwell estimation. In particular, human detection may be achieved using a spatio-temporal variance analysis calculation methodology for moving target detection.

Abstract: Method and system for objects surveillance and real-time activity recognition is based on analysis of spatio-temporal images of individuals under surveillance where a spatio-temporal volume occupied by each individual is decomposed by crossing the same at specific heights to form 2-dimensional slices, each containing representation of trajectory of the motion of corresponding portions of the individual body. The symmetry of the trajectories (Gait DNA) is analyzed and classified to generate data indicative of a type of activity of the individual based on the symmetry or asymmetry of the Gait DNA in each 2-dimensional slice. An effective occlusion handling ability is implemented which permits to restore the occluded silhouette of an individual.

Abstract: Aspects of the present invention allow for real-time people monitoring method and system for estimation of the size and flow density of a given group of people located in a given area of space. The method and system may be used to monitor live or recorded camera input, foreground segmentation, human tracking, height estimation, and dwell estimation. In particular, human detection may be achieved using a spatio-temporal variance analysis calculation methodology for moving target detection.