Abstract: The present technology relates to systems and methods that allocate, arrange, and distribute certain types of functions and intelligence for autonomous driving systems (ADS) to facilitate vehicle operations and controls; to improve the safety of an autonomous vehicle (AV); and to ensure the efficiency, intelligence, reliability, and resilience of ADS. The present technology also provides methods to define ADS system intelligence and its levels, which are based on two dimensions: the vehicle intelligence and the cloud intelligence. The cloud intelligence supports and complements the vehicle intelligence when a vehicle is impaired or handicapped for certain functions (sensing, prediction, decision, and/or control) during operations in abnormal conditions. Distribution of driving intelligence between the AV and the cloud facilitates safe operations of the AV for long-tail corner cases.

Abstract: This technology provides several methods for operating an Autonomous Vehicle Intelligent Control System that integrates a sensing module for collecting driving environment data and an onboard unit (OBU) for vehicle control. The OBU features a vehicle control module and communication modules for guidance-level interaction with Traffic Control Centers/Traffic Control Units (TCC/TCU). The system receives targeted guidance instructions and information, such as vehicle maneuvering, safety maintenance, traffic control, and special conditions, to enhance driving tasks. The system also incorporates weather or special information in vehicle operation or control. The OBU receives vehicle-specific targeted weather information and/or special information from the TCC/TCU. The special information comprises activity, accidents, and hazards/obstacles information.

Abstract: Provided herein is technology relating to a distributed driving system with the support of an intelligent roadside toolbox (IRT) to address long-tail cases. The IRT comprises a cloud and provides fused data from multiple sensors and data sources. A connected automated vehicle (CAV) uses the fused data to supplement its sensing data and maintain its automation level when its components have failure. Moreover, the IRT predicts the movement and behavior of surrounding objects, pedestrians, and bicycles; and provides such vulnerable road user information to the CAV to help the CAV maintain its safe driving and desired autonomous driving level in complex urban environments. For emergency situations or function failure, the IRT provides emergency services and failure safety measures for the CAV.

Abstract: This technology relates to autonomous vehicle (AV) control systems tailored for critical points with information support from a cloud platform or other vehicles. A first system integrates an onboard unit (OBU) and communication modules to interact with a cloud platform, delivering time-sensitive instructions to vehicles at critical points. The OBUs execute these instructions for driving tasks. A second system enables an AV to operate through critical points with information from other vehicles, leveraging integrated information services for enhanced functionality and safety. A third system combines an OBU with a cloud platform, leveraging cloud services for enhanced functionality, such as computing. The technology adapts to diverse critical point scenarios, employing proactive incident prediction and rapid detection methods for optimized performance.

Abstract: The disclosure relates to the field of medical equipment, and discloses a closed-loop transcranial ultrasound brain stimulation device, the device includes a head ring, where the outer wall of the head ring is fixedly connected with a vertically symmetrical connecting belt, the outer wall of the head ring is fixedly connected with a protective shell, the inside of the protective shell is rotatably connected with a knob I, the outer wall of the knob I is fixedly connected with a gear, the lower surface of the connecting belt is fixedly connected with a rack, and the rack meshes with the gear; the upper surface of the head ring is fixedly connected with a connecting rod, the upper surface of the connecting rod is fixedly connected with a fixed ring, the outer wall of the fixed ring is fixedly connected with a toothed ring.

Abstract: This technology provides an Autonomous Vehicle (AV) Control System that includes a sensing module for gathering driving environment data and an onboard unit (OBU) for vehicle control. The OBU fuses data from the vehicle and data from a Traffic Control Center/Traffic Control Unit (TCC/TCU), a Roadside Unit (RSU), or another vehicle. The OBU comprises a vehicle control module and communication modules for information exchange with TCC/TCU, RSU, or another vehicle. The AV control system receives targeted guidance instructions and information, such as vehicle maneuvering, safety maintenance, traffic control, and special conditions, to enhance driving tasks. The AV control system collaborates with TCC/TCU, RSU, or another AV, which provides redundancy and a fail-safe mechanism for increased safety and reliability. The system is designed with wireless communication capabilities for efficient information exchange and is applicable to autonomous vehicles (AVs).

Abstract: This invention provides an Autonomous Vehicle (AV) Control System comprising a sensing module for gathering driving environment data and an onboard unit (OBU) for vehicle control. The OBU comprises a vehicle control module and communication modules for information exchange with a Traffic Control Center/Traffic Control Unit (TCC/TCU) or Roadside Units (RSUs). The AV Control System receives targeted guidance instructions and information, such as vehicle maneuvering, safety maintenance, traffic control, and special conditions, to enhance driving tasks. The AV Control System collaborates with a TCC/TCU or RSUs, providing redundancy and a fail-safe mechanism for increased safety and reliability. The system is designed with wireless communication capabilities for efficient information exchange and is applicable to autonomous vehicles (AVs).

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: The invention provides systems and methods for an autonomous vehicle and center control system (AVCCS), which is a component of a Connected Automated Vehicle Highway (CAVH) system. The AVCCS is configured to realize drone/tele driving or digital twins by providing automated vehicles (AVs) or connected automated vehicles (CAVs) with vehicle-specific control instructions comprising instructions for vehicle longitudinal and lateral position, speed, and steering and control. Specifically, through the system, AVs or CAVs can be effectively and efficiently controlled by the AVCCS. In addition, the AVCCS is configured to provide vehicle-specific control instructions to AVs or CAVs and control them through a hierarchy of traffic control centers/units (TCCs/TCUs) or the combination of TCCs/TCUs and the onboard units (OBUs) with a vehicle control module, wherein said TCCs/TCUs are configured to fulfill vehicle maneuver tasks, monitor safety maintenance tasks, and take over if the system fails.

Abstract: The present invention relates to systems and methods that allocate, arrange, and distribute certain types of functions and intelligence, for connected automated vehicle highway (CAVH) systems, to facilitate vehicle operations and controls, to improve the general safety of the whole transportation system, and to ensure the efficiency, intelligence, reliability, and resilience of CAVH systems. The present invention also provides methods to define CAVH system intelligence and its levels, which are based on two dimensions: the vehicle intelligence and infrastructure intelligence.

Abstract: A spatial-temporal storage method, system, and non-transitory computer readable medium include dynamically managing a plurality of region servers for querying spatiotemporal data in noSQL databases.

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: Provided herein is technology relating to roadway design and traffic control systems and methods for connected and automated vehicle and highway (CAVH) systems, and particularly, but not exclusively, to systems and methods for controlling switching of vehicles between automated mode and human-driven mode, systems and methods for vehicle merging, diverging, and overtaking on automated lanes of multiple lane highways, systems and methods for emergency management and roadside assistance on automated lanes, and/or systems and methods for managing automated vehicle lanes on urban major and minor expressways.

Abstract: This technology provides an Autonomous Vehicle (AV) Intelligent Driving System (IDS) that uses weather information or special information to provide vehicle operation or control. The Onboard Unit (OBU) uses a traffic control center/traffic control unit (TCC/TCU) communication module or a roadside unit (RSU) communication module to receive vehicle-specific targeted weather information and/or special information at the Guidance Level of vehicle driving tasks from the TCC/TCU or the RSU. The special information comprises activity, accidents, and hazards/obstacles information. The vehicle control module enhances driving safety by integrating vehicle-specific weather, pavement condition, activity, accidents, and hazards/obstacles information.

Abstract: This technology provides an Autonomous Vehicle (AV) Intelligent Driving System (IDS) that includes a sensing module for gathering driving environment data and an onboard unit (OBU) for vehicle control. The OBU features a vehicle control module and communication modules for guidance-level interaction with Traffic Control Centers (TCC/TCU) and Roadside Units (RSUs). The system receives targeted guidance instructions and information, such as vehicle maneuvering, safety maintenance, traffic control, and special conditions, to enhance driving tasks. The IDS collaborates with TCC/TCU and RSUs, providing redundancy and a fail-safe mechanism for increased safety and reliability. The system is designed with wireless communication capabilities for efficient information exchange and is applicable to connected and automated vehicles (CAVs).

Abstract: This technology provides an autonomous vehicle (AV) system that integrates a localized generative artificial intelligence (AI) system with a world model for automated vehicle control and traffic operations. The AI system comprises a machine learning component that uses historical and real-time environmental or road data to improve models and algorithms for identifying vehicles and objects and predicting vehicle movements. The AI system features an environment prediction component configured to generate road and environmental condition forecasts based on both historical and real-time information. The AI system is configured to generate numerous long-tail cases that are challenging or impractical to be collected directly from real-world scenarios, such as traffic accidents, adverse weather conditions, natural hazards, pavement breakdown, traffic events, and/or communication malfunction.

Abstract: Provided herein is an intelligent system for microscopic motion control of autonomous vehicles. The autonomous vehicle intelligent system comprises an onboard unit (OBU) in a vehicle. The OBU comprises a sensing module, a communication module, a data fusion module, and/or a prediction module. The OBU is capable of generating guidance information and targeted instructions for individual vehicles. The sensing module is designed to detect the surrounding environment. The communication module enables seamless connectivity with nearby autonomous vehicles (AVs), roadside units (RSUs), cloud platform, and traffic control center or traffic control unit (TCC/TCUs). The data fusion module integrates multi-source information collected from onboard sensors and the cloud. The prediction module delivers advanced forecasting capabilities.

Abstract: The technology described herein provides an Intelligent Driving System for Adverse Weather Conditions (IDS-AWC) to enhance the safety and efficiency of autonomous vehicles (AVs). The system comprises an onboard unit (OBU) and/or a cloud platform, which integrate multi-source weather and environmental information from vehicle sensors, AVs, roadside units (RSUs), cloud platforms, and/or traffic control centers/traffic control units (TCC/TCU). The OBU processes data using learning-based, statistical, and empirical models to optimize vehicle control. The IDS-AWC improves situational awareness with high-definition maps for lane and road geometry recognition in low visibility and applies weather-adaptive control strategies, such as speed adjustments on slippery or icy roads. The cloud platform provides vehicle-specific weather forecasts and planning outputs to enhance decision-making.

Abstract: The technology relates to a systematic intelligent system (SIS) configured to train and optimize trip profile models through dynamic distribution of computing resources and model parameters across vehicle intelligent units (VIUs) and roadside intelligent units (RIUs). The SIS comprises a system intelligent unit (SIU) configured to generate and maintain a trip profile model using aggregated historical trip data. Based on this model, the SIU coordinates distributed training, schedules model updates, allocates computing resources, and issues task assignments to VIUs and RIUs provided by multiple automated driving system service providers. The system further supports pre-trip planning, en-route updates, and post-trip feedback to continuously refine training processes and optimize deployment. Communication among SIS components is enabled through unified data interfaces and formats to ensure cross-system coordination, efficient model calibration, and adaptive resource management.

Abstract: Provided herein is technology relating to automated driving and particularly, but not exclusively, to a modular Vehicle Intelligence Unit (VIU) comprising a sensing and perception fusion module, a collaborative decision-making module, and an intelligent control/assistance module. The VIU is designed to dynamically upgrade or adaptively restore autonomous driving levels based on vehicle conditions and driving conditions. The VIU enables progressive autonomy restoration or elevation from Level 1 to Level 2, 3, 4, or 5; from Level 2 to Level 3, 4, or 5; or from Level 3 to Level 4 or 5. The system features a dynamic recovery mechanism for downgraded systems. When adverse conditions trigger temporary downgrades (e.g., from Level 4 to Level 2), the VIU autonomously restores the original autonomy level through multi-module coordination and improvement.