Abstract: One embodiment discloses an automobile having multiple distributed subsystems configured to provide communication via a network system. The automobile includes an outward facing camera (“OFC”) subsystem and a vehicle onboard computer (“VOC”). The OFC subsystem, having at least one OFC, OFC processor, and OFC database, is configured to recognize a predefined exterior object from a set of exterior images captured by the OFC based on an OFC query. The VOC includes a VOC central processing unit, VOC database, and network manager, wherein the network manager includes an internal network circuit and an external network circuit. The internal network circuit is used for communicating with the OFC subsystem while the external network circuit is used to interface with a cloud system. In one aspect, the VOC provides a data stream representing a recognized event in accordance with a query retrieved from the VOC database.

Abstract: One embodiment of the present invention is able to provide an interactive parking management (“IPM”) in accordance with information obtained from interior and exterior sensors, vehicle onboard computer (“VOC”), and cloud data. The process, in one embodiment, is capable of acknowledging a parking activity initiated by a vehicle traveling in a geographic location via a communications network. Upon providing parking information to the vehicle based on the data obtained from the current parking status, historical parking status, and big data for facilitating the parking activity, the movement of the vehicle is monitored in accordance with the current parking status. After recording a physical location where the vehicle is parked and parking duration, a parking invoice is generated in response to information relating to the recorded information such as physical location and parking duration.

Abstract: One embodiment of the present invention discloses a process of providing a report predicting potential risks relating to an operator driving a vehicle using information obtained from various interior and exterior sensors, vehicle onboard computer (“VOC”), and cloud network. After activating interior and exterior sensors mounted on a vehicle operated by a driver for obtaining data relating to external surroundings and internal environment, the data is forwarded to VOC for generating a current fingerprint associated with the driver. The current fingerprint represents current driving status in accordance with the collected real-time data. Upon uploading the current fingerprint to the cloud via a communications network, a historical fingerprint which represents historical driving information associated with the driver is retrieved.

Abstract: One embodiment of the present invention predicts a vehicular event relating to machinal performance using information obtained from interior and exterior sensors, vehicle onboard computer (“VOC”), and cloud data. The process of predication is able to activate interior and exterior sensors mounted on a vehicle operated by a driver for obtaining current data relating to external surroundings, interior settings, and internal mechanical conditions of the vehicle. After forwarding the current data to VOC to generate a current vehicle status representing real-time vehicle performance in accordance with the current data, retrieving a historical data associated with the vehicle including mechanical condition is retrieved. In one aspect, a normal condition signal is issued when the current vehicle status does not satisfy with the optimal condition based on the historical data. Alternatively, a race car condition is issued when the current vehicle status meets with the optimal condition.

Abstract: A method and/or system is able to provide driver fingerprint via metadata extraction managed by a driver rating (“DR”) model trained by a machine learning center (“MLC”) coupled to a cloud based network (“CBN”). In one embodiment, a DR system includes a set of outward facing cameras, a set of inward facing cameras, and a vehicle onboard computer (“VOC”). The set of outward facing cameras mounted on a vehicle is used to collect external images representing a surrounding environment in which the vehicle operates. The set of inward facing cameras mounted in the vehicle is used to collect internal images including operator body expression representing at least operator's attention. The VOC is configured to determine the identity of operator and current operating style in response to the collected internal images, the collected external images, and historical stored data.

Abstract: A method or system is able to refine Global Positioning System (“GPS”) information for guiding a vehicle via extracted metadata using a GPS refinement (“GR”) model managed by a virtuous cycle containing sensors, machine learning center (“MLC”), and a cloud based network (“CBN”). The GR system includes a set of outward facing cameras, a vehicle onboard computer (“VOC”), and GR model. The outward facing cameras mounted on a vehicle are capable of collecting external images representing a surrounding environment in which the vehicle operates. The VOC is configured to generate a positional vehicle location with respect to the surrounding environment in accordance with the external images and historical stored data obtained from CBN. The GR model is configured to generate a driving guidance based on combined information between the positional vehicle location and GPS data.

Abstract: A method and/or system is able to improve vehicle safety via metadata extraction by an inferred attentional (“IA”) model trained by a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the system includes a set of outward facing camera, inward facing cameras, and vehicle onboard computer (“VOC”). The outward facing cameras collect external images representing a surrounding environment in which the vehicle operates. The inward facing cameras collect internal images including operator facial expression representing at least operator's attention. The VOC is configured to identify operator's attention in response to the collected internal images and the collected external images.

Abstract: A method or system is able to adjust an exterior mirror of a vehicle via an automatic mirror-setting (“AM”) model managed by a virtuous cycle containing a cloud based network (“CBN”). The system includes a set of mirrors, a set of inward facing cameras, a vehicle onboard computer (“VOC”), and AM module. In one embodiment, the mirrors, attached to a vehicle, are configured to capture at least a portion of external environment in which the vehicle operates. The inward facing cameras, mounted in the vehicle, are configured to collect internal images including operator facial features showing operator visual characteristics. VOC, which is coupled to CBN, is configured to determine operator vision metadata based on the internal images, operator visual characteristics, and historical stored data. The AM module is able to adaptively set a mirror to an optimal orientation so that area of external blind spot is minimized.

Abstract: A method or system is capable of detecting operator behavior (“OB”) utilizing a virtuous cycle containing sensors, machine learning center (“MLC”), and cloud based network (“CBN”). In one aspect, the process monitors operator body language captured by interior sensors and captures surrounding information observed by exterior sensors onboard a vehicle as the vehicle is in motion. After selectively recording the captured data in accordance with an OB model generated by MLC, an abnormal OB (“AOB”) is detected in accordance with vehicular status signals received by the OB model. Upon rewinding recorded operator body language and the surrounding information leading up to detection of AOB, labeled data associated with AOB is generated. The labeled data is subsequently uploaded to CBN for facilitating OB model training at MLC via a virtuous cycle.

Abstract: A method or system capable of managing automobile parking space (“APS”) using containerized sensors, machine learning center, and cloud based network is disclosed. A process, in one aspect, monitors the surrounding information observed by a set of onboard sensors of a vehicle as the vehicle is in motion. After selectively recording the surrounding information in accordance with instructions from a containerized APS model which is received from a machine learning center, an APS and APS surrounding information are detected when the vehicle is in a parked condition. Upon rewinding recorded surrounding information leading up to the detection of APS, labeled data associated with APS is generated based on APS and the recorded surrounding information. The process subsequently uploads the labeled data to the cloud based network for facilitating APS model training at the machine learning center via a virtuous cycle.

Abstract: An active debugging environment for debugging a virtual application that contains program language code from multiple compiled and/or interpreted programming languages. The active debugging environment is language neutral and host neutral, where the host is a standard content centric script host with language engines for each of the multiple compiled and/or interpreted programming languages represented in the virtual application. The active debugging environment user interface can be of any debug tool interface design. The language neutral and host neutral active debugging environment is facilitated by a process debug manager that catalogs and manages application specific components, and a machine debug manager that catalogs and manages the various applications that comprise a virtual application being run by the script host. The process debug manager and the machine debug manager act as an interface between the language engine specific programming language details and the debug user interface.

Abstract: An active debugging environment for debugging a virtual application that contains program language code from multiple compiled and/or interpreted programming languages. The active debugging environment is language neutral and host neutral, where the host is a standard content centric script host with language engines for each of the multiple compiled and/or interpreted programming languages represented in the virtual application. The active debugging environment user interface can be of any debug tool interface design. The language neutral and host neutral active debugging environment is facilitated by a process debug manager that catalogs and manages application specific components, and a machine debug manager that catalogs and manages the various applications that comprise a virtual application being run by the script host. The process debug manager and the machine debug manager act as an interface between the language engine specific programming language details and the debug user interface.

Abstract: The scripting engine interface provides the capability to interconnect any suitably written application or server with any scripting language. The implementation of the scripting engine itself (language, syntax, persistent format, execution model, and the like) is left entirely to the vendor, and the scripting engine need not come from the same vendor as the host application. The scripting engine interface implements this capability using a set of interfaces, the two most important ones being IActiveScriptSite (implemented by the host application) and IActiveScript (implemented by the language engine.) Together, these interfaces allow a host application to inform a scripting engine about the functionality that can be scripted and to define the scripts that the scripting engine should execute, either immediately or in response to user actions such as mouse clicks.

Abstract: An active debugging environment for debugging a virtual application that contains program language code from multiple compiled and/or interpreted programming languages. The active debugging environment is language neutral and host neutral, where the host is a standard content centric script host with language engines for each of the multiple compiled and/or interpreted programming languages represented in the virtual application. The active debugging environment user interface can be of any debug tool interface design. The language neutral and host neutral active debugging environment is facilitated by a process debug manager that catalogs and manages application specific components, and a machine debug manager that catalogs and manages the various applications that comprise a virtual application being run by the script host. The process debug manager and the machine debug manager act as an interface between the language engine specific programming language details and the debug user interface.