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: 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: Provided herein is technology relating to automated driving, particularly, but not exclusively, to an enterprise active safety system (EASS) that is configured to improve safety or minimize losses from safety events. More specifically, the EASS is configured to provide system-based safety functions by integrating one or more of the vehicle components, road components, cloud components, pedestrian components, and map components. These safety functions include one or more of crash detection, crash prediction, crash warning, crash avoidance, crash impact reduction, and emergency response functions.

Abstract: The technology provides designs and methods for the transit management system, which facilitates transit vehicle operations and control for connected automated transit vehicles (CATVs) systems. The transit management system provides transit vehicles with customized/non-customized information and time-sensitive control instructions for transit vehicle to fulfill the driving tasks such as vehicle routing, lane changing, turning. The transit management system also realizes transit vehicle lane design, transportation operations and management services for transit vehicle. The transit management system 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), (5) Cloud platform.

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 presents a cloud-based model deployment and control system (CMDCS) for providing automated driving services. The CMDCS comprises a cloud-based platform, an onboard unit (OBU), and a Vehicle-to-System component. The cloud-based platform comprises a localization-enhancement subsystem and a cloud computing module. The CMDCS is configured to collect detectable data and undetectable data from vehicles, road, and cloud. Then, the CMDCS deploys a set of end-to-end AI models and methods for automated driving services, comprising sensing, prediction, planning, and control services. The AI models and methods are trained and optimized to process collected data for providing operating parameters for vehicles. Then, the vehicles can be effectively and efficiently controlled and operated by the CMDCS. In addition, the CMDCS is configured to generate and provide detailed time-sensitive vehicle specific control instructions.

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: 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: Provided herein is technology relating to automated driving and particularly, but not exclusively, to a communication-based Connected Reference Marker (C-CRM) system for precise, real-time vehicle localization in connected and automated driving environments. The system comprises CRMs, Wireless Signal Units, an Onboard Module, and/or a Central Operations Unit, which uses two-dimensional and three-dimensional triangular position identification methods for level grade roads and for upgrade or down grade roads, respectively. This method enhances localization accuracy in conditions where GPS or onboard sensors fail. The system also enables virtual roadway configuration, allowing vehicles to maintain lane position without relying on high definition (HD) maps. Importantly, the C-CRM system improves detection and positioning of Vulnerable Road Users, including pedestrians and bikes, offering critical support in urban driving scenarios.

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.