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: 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: 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 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: 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: 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: State data of vehicles in a control area is collected, then time of starting control and selection of controlled vehicles are decided according to a position of a ramp merging vehicle, information is transmitted into a traffic control module by means of a data transmission module, and an artificial intelligence based ramp merging multi-objective control model ensures efficient, safe, and energy-saving operation of overall traffic of a road while completing ramp merging by means of a vehicle traveling track in the cooperative ramp control area. Compared with a traditional method, the present invention greatly promotes merging of the ramp vehicles, and different from other methods having the defect of transforming the problem of ramp merging into the problem of vehicle sequencing, the present invention greatly improves efficiency of ramp merging.

Abstract: The technology described herein provides systems and methods for an Autonomous Vehicle Cloud Control System (AVCCS) with a World Model. The AVCCS comprises a cloud-based platform, a communication module, and/or an onboard unit (OBU). The AVCCS leverages generative models, predictive models, and reinforcement learning methods to generate and synthesize comprehensive information at real-time, short-term, and long-term scales for sensing, transportation behavior prediction and management, planning and decision-making, and/or vehicle control. The comprehensive information generated from the World Model comprises vehicle surrounding information, weather information, vehicle attribute data, traffic state information, road information, and incident information.

Abstract: The technology described herein provides systems and methods for an Automated Driving Cloud System (ADCS) for long-tail corner cases. The ADCS for long-tail corner cases comprises a cloud-based platform, a communication module, and/or an onboard unit (OBU). The ADCS leverages world models to provide automated driving functions including sensing, prediction, planning, decision making, and control at microscopic, mesoscopic, and/or macroscopic levels. The system is specifically designed to address long-tail corner cases, which include work zones, special events, reduced speed zones, incident detection, buffer spaces, and adverse weather conditions. Additionally, the ADCS is configured to provide safety and efficiency measures for vehicle operations and control at various special scales that require additional system coverage, including construction zones, special event zones, and special weather conditions. The ADCS enables adaptive and reliable automated driving in highly uncertain and dynamic environments.

Abstract: The technology described herein provides a cloud-based learning system (CLS) for end to end and/or sequential models for autonomous driving. The CLS provides high-performance computation capability that allocates computation power for sensing, prediction, planning and decision making, and control at a microscopic level, a mesoscopic level, and/or a macroscopic level. The CLS can acquire computation resources from a cloud system and from one or more of a roadside unit network, a network of vehicles comprising onboard units, a traffic control center/traffic control unit, or a traffic operations center. Additionally, the CLS is configured to optimize and generate detailed customized information and time-sensitive control instructions for vehicles by processing data through learning models to fulfill driving tasks and provide operations and maintenance services for vehicles.

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: 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 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.