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: 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 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 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: 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 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: 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) 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.