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: This technology described herein provides embodiments of a cloud-based mobility service system for a Connected Automated Vehicle Highway (CAVH). In some embodiments, the technology provides a cloud-based mobility service system to provide the services and functionalities of different components of a CAVH system including, for example, user, vehicle, infrastructure, system, roadside, and CAVH traffic control layers. Detailed cloud-based data interfaces and services are described for each component, e.g., regarding their data needs to and from the cloud system. Cloud functionalities including the communication, computational, and analytic needs are described for each system component. The CAVH cloud services also provide integrated CAVH functionalities including planning, control, sensing, prediction, and analytics at macroscopic, mesoscopic, and microscopic levels of CAVH systems.

Abstract: Systems and methods allow for incident data collection and management system based on unmanned aerial vehicles (UAVs), that is, drones to help accelerate the data collection and analytics, information dissemination, and decision support at incident sites. The system architecture may include onsite, server, and offline components including flight planning subsystem, flight execution and mission control subsystem, information dissemination subsystem to travelers and traveler information services, the interface with traffic management center, and the data analytic, visualization, and training subsystems. Other embodiments include the video-based 3D incident site reconstruction methods, site positioning and scaling methods with pre-collected static background infrastructure data, data management and user charging methods, and training methods with the generated 3D model.

Abstract: This technology describes one type of CAVH services focusing on fixed-route trips such as commuting, shopping, school, and other trips that users previously travel recurrently and frequently. The technology describes the system architecture of the proposed fixed-route services. The technology includes methods of calibrating, providing, and optimizing the functionalities of such fixed-route services. The detailed methods are proposed for pre-trip, enroute, trip chaining, and post-trip operations, the cyber-physical security, and privacy protection for the users and participating vehicles.

Abstract: Systems and methods allow for incident data collection and management system based on unmanned aerial vehicles (UAVs), that is, drones to help accelerate the data collection and analytics, information dissemination, and decision support at incident sites. The system architecture may include onsite, server, and offline components including flight planning subsystem, flight execution and mission control subsystem, information dissemination subsystem to travelers and traveler information services, the interface with traffic management center, and the data analytic, visualization, and training subsystems. Other embodiments include the video-based 3D incident site reconstruction methods, site positioning and scaling methods with pre-collected static background infrastructure data, data management and user charging methods, and training methods with the generated 3D model.

Abstract: Systems and methods allow for incident data collection and management system based on unmanned aerial vehicles (UAVs), that is, drones to help accelerate the data collection and analytics, information dissemination, and decision support at incident sites. The system architecture may include onsite, server, and offline components including flight planning subsystem, flight execution and mission control subsystem, information dissemination subsystem to travelers and traveler information services, the interface with traffic management center, and the data analytic, visualization, and training subsystems. Other embodiments include the video-based 3D incident site reconstruction methods, site positioning and scaling methods with pre-collected static background infrastructure data, data management and user charging methods, and training methods with the generated 3D model.

Abstract: Systems and methods allow for incident data collection and management system based on unmanned aerial vehicles (UAVs), that is, drones to help accelerate the data collection and analytics, information dissemination, and decision support at incident sites. The system architecture may include onsite, server, and offline components including flight planning subsystem, flight execution and mission control subsystem, information dissemination subsystem to travelers and traveler information services, the interface with traffic management center, and the data analytic, visualization, and training subsystems. Other embodiments include the video-based 3D incident site reconstruction methods, site positioning and scaling methods with pre-collected static background infrastructure data, data management and user charging methods, and training methods with the generated 3D model.

Abstract: This technology describes one type of CAVH services focusing on fixed-route trips such as commuting, shopping, school, and other trips that users previously travel recurrently and frequently. The technology describes the system architecture of the proposed fixed-route services. The technology includes methods of calibrating, providing, and optimizing the functionalities of such fixed-route services. The detailed methods are proposed for pre-trip, enroute, trip chaining, and post-trip operations, the cyber-physical security, and privacy protection for the users and participating vehicles.

Abstract: This technology described herein provides embodiments of a cloud-based mobility service system for a Connected Automated Vehicle Highway (CAVH). In some embodiments, the technology provides a cloud-based mobility service system to provide the services and functionalities of different components of a CAVH system including, for example, user, vehicle, infrastructure, system, roadside, and CAVH traffic control layers. Detailed cloud-based data interfaces and services are described for each component, e.g., regarding their data needs to and from the cloud system. Cloud functionalities including the communication, computational, and analytic needs are described for each system component. The CAVH cloud services also provide integrated CAVH functionalities including planning, control, sensing, prediction, and analytics at macroscopic, mesoscopic, and microscopic levels of CAVH systems.

Abstract: Systems and methods allow for incident data collection and management system based on unmanned aerial vehicles (UAVs), that is, drones to help accelerate the data collection and analytics, information dissemination, and decision support at incident sites. The system architecture may include onsite, server, and offline components including flight planning subsystem, flight execution and mission control subsystem, information dissemination subsystem to travelers and traveler information services, the interface with traffic management center, and the data analytic, visualization, and training subsystems. Other embodiments include the video-based 3D incident site reconstruction methods, site positioning and scaling methods with pre-collected static background infrastructure data, data management and user charging methods, and training methods with the generated 3D model.

Abstract: Systems and methods allow for incident data collection and management system based on unmanned aerial vehicles (UAVs), that is, drones to help accelerate the data collection and analytics, information dissemination, and decision support at incident sites. The system architecture may include onsite, server, and offline components including flight planning subsystem, flight execution and mission control subsystem, information dissemination subsystem to travelers and traveler information services, the interface with traffic management center, and the data analytic, visualization, and training subsystems. Other embodiments include the video-based 3D incident site reconstruction methods, site positioning and scaling methods with pre-collected static background infrastructure data, data management and user charging methods, and training methods with the generated 3D model.