Abstract: Autonomous driving includes evaluating information about an environment surrounding a vehicle, generating, based on the evaluation of the information about the environment surrounding the vehicle, a driving plan for performing a driving maneuver, and operating vehicle systems in the vehicle to perform the driving maneuver according to the driving plan. The autonomous driving further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles. Under the driving plan, a driving behavior of the vehicle matches the predominating driving behavior of the like population of reference vehicles.

Abstract: Providing user assistance in a vehicle includes evaluating information about the manual operation of the vehicle and information about an environment surrounding the vehicle, and identifying the driving behavior of the vehicle based on the evaluation of the information about the manual operation of the vehicle and the information about the environment surrounding the vehicle. The user assistance further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles, and issuing both prospective instructions and remedial instructions to a user on how to make the driving behavior of the vehicle match the predominating driving behavior of the like population of reference vehicles.

Abstract: Providing user assistance in a vehicle includes evaluating information about the manual operation of the vehicle and information about an environment surrounding the vehicle, and identifying the driving behavior of the vehicle based on the evaluation of the information about the manual operation of the vehicle and the information about the environment surrounding the vehicle. The user assistance further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles, and issuing both prospective instructions and remedial instructions to a user on how to make the driving behavior of the vehicle match the predominating driving behavior of the like population of reference vehicles.

Abstract: Providing user assistance in a vehicle includes evaluating information about the manual operation of the vehicle and information about an environment surrounding the vehicle, and identifying the driving behavior of the vehicle based on the evaluation of the information about the manual operation of the vehicle and the information about the environment surrounding the vehicle. The user assistance further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles, and issuing both prospective instructions and remedial instructions to a user on how to make the driving behavior of the vehicle match the predominating driving behavior of the like population of reference vehicles.

Abstract: Autonomous driving includes evaluating information about an environment surrounding a vehicle, generating, based on the evaluation of the information about the environment surrounding the vehicle, a driving plan for performing a driving maneuver, and operating vehicle systems in the vehicle to perform the driving maneuver according to the driving plan. The autonomous driving further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles. Under the driving plan, a driving behavior of the vehicle matches the predominating driving behavior of the like population of reference vehicles.

Abstract: The present application discloses systems and methods for inventorying objects. In one embodiment, a robot detects an object and sends identification data and location data associated with the detected object to a cloud computing system. The identification data may include an image of the object and/or information from a tag, code, or beacon associated with the object. In response to receiving the identification data and the location data, the cloud computing system identifies the object. The cloud computing system may also determine or create a first map associated with the identified object and a second map associated with the identified object. The first map may be associated with the current location of the object and the second map may correspond to a past location of the object. The cloud computing server may compare the first and second maps, and then send instructions to the robot based on the comparison.

Abstract: Methods and systems for proactively preventing hazardous or other situations in a robot-cloud interaction are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices. The task logs may include information associated with tasks performed by the plurality of robotic devices. The method may also include a computing system determining information associated with hazardous situations based on the information associated with the task logs. For example, the hazardous situations may comprise situations associated with failures of one or more components of the plurality of robotic devices. According to the method, information associated with a contextual situation of a first robotic device may be determined, and when the information associated with the contextual situation is consistent with information associated with the one or more hazardous situations, an alert indicating a potential failure of the first robotic device may be provided.

Abstract: This disclosure describes various embodiments for resource optimization in a vehicle. In an embodiment, a system for resource optimization in a vehicle is described. The system may comprise a memory; a processor coupled to the memory; and a resource optimization module. The resource optimization module may be configured to: monitor usage of local computing resources of the vehicle, the local computing resources comprising the processor and available bandwidth of a transmission medium; determine an availability of the local computing resources; evaluate data captured by one or more sensors of the vehicle; and determine whether to process the data locally or remotely based, at least in part, on the availability of the local computing resources and the data captured by the one or more sensors.

Abstract: Autonomous driving includes evaluating information about an environment surrounding a vehicle, generating, based on the evaluation of the information about the environment surrounding the vehicle, a driving plan for performing a driving maneuver, and operating vehicle systems in the vehicle to perform the driving maneuver according to the driving plan. The autonomous driving further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles. Under the driving plan, a driving behavior of the vehicle matches the predominating driving behavior of the like population of reference vehicles.

Abstract: Providing user assistance in a vehicle includes evaluating information about the manual operation of the vehicle and information about an environment surrounding the vehicle, and identifying the driving behavior of the vehicle based on the evaluation of the information about the manual operation of the vehicle and the information about the environment surrounding the vehicle. The user assistance further includes receiving a traffic behavior model that describes a predominating driving behavior of a like population of reference vehicles, and issuing both prospective instructions and remedial instructions to a user on how to make the driving behavior of the vehicle match the predominating driving behavior of the like population of reference vehicles.

Abstract: Providing user assistance in a vehicle includes evaluating information about an environment surrounding the vehicle, including identifying an object in the environment surrounding the vehicle, and predicting, based on the evaluation of the information about the environment surrounding the vehicle, the future maneuvering of the object. The user assistance further includes receiving a traffic behavior model that describes a predominating traffic behavior of a like population of reference objects. The prediction includes switching from extrapolating the predominating traffic behavior of the like population of reference objects, to, in response to identifying a traffic behavior of the object, extrapolating the traffic behavior of the object.

Abstract: Methods and systems for proactively preventing hazardous or other situations in a robot-cloud interaction are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices. The task logs may include information associated with tasks performed by the plurality of robotic devices. The method may also include a computing system determining information associated with hazardous situations based on the information associated with the task logs. For example, the hazardous situations may comprise situations associated with failures of one or more components of the plurality of robotic devices. According to the method, information associated with a contextual situation of a first robotic device may be determined, and when the information associated with the contextual situation is consistent with information associated with the one or more hazardous situations, an alert indicating a potential failure of the first robotic device may be provided.

Abstract: System, methods, and other embodiments described herein relate to detecting markers on a roadway. In one embodiment, a method includes controlling a radar to transmit a scanning signal with defined characteristics. The radar is integrated with a vehicle that is traveling on the roadway. The method includes, in response to receiving a reflected signal resulting from the scanning signal interacting with the roadway, identifying the marker from the reflected signal according to an electromagnetic signature of the marker embodied in the reflected signal. The electromagnetic signature is a response induced within the defined characteristics of the scanning signal that is embodied within the reflected signal.

Abstract: System, methods, and other embodiments described herein relate to acquiring embedded information from a roadway. In one embodiment, a method includes, in response to receiving a reflected signal resulting from a scanning signal interacting with the roadway, analyze the reflected signal to detect a roadway signature embedded within the roadway. The method includes computing an identifier of the roadway signature as a function of features associated with the roadway signature that are embodied within the reflected signal. The method includes providing the embedded information about the roadway according to the identifier.

Abstract: System, methods, and other embodiments described herein relate to localizing a vehicle on a roadway. In one embodiment, a method includes, in response to detecting one or more indicators of a roadway signature within a reflected signal from the roadway, acquiring a fix on the roadway signature as a function of the one or more indicators that identify at least a segment of the roadway signature. The method includes localizing the vehicle on the roadway by correlating the roadway signature with a signature mapping that identifies a location for the segment of the roadway signature on the roadway.

Abstract: System, methods, and other embodiments described herein relate to detecting markers on a roadway. In one embodiment, a method includes controlling a radar to transmit a scanning signal with defined characteristics. The radar is integrated with a vehicle that is traveling on the roadway. The method includes, in response to receiving a reflected signal resulting from the scanning signal interacting with the roadway, identifying the marker from the reflected signal according to an electromagnetic signature of the marker embodied in the reflected signal. The electromagnetic signature is a response induced within the defined characteristics of the scanning signal that is embodied within the reflected signal.

Abstract: System, methods, and other embodiments described herein relate to improving vigilance and readiness of an operator in a vehicle that includes an augmented reality (AR) display. In one embodiment, a method includes computing an engagement level of the operator to characterize an extent of vigilance decrement and readiness presently exhibited by the operator relative to operating characteristics of the vehicle including an external environment around the vehicle and at least semi-autonomous operation of the vehicle. The method includes dynamically rendering, on the AR display, graphical elements by varying visual characteristics of the at least one graphical element as a function of the engagement level and based, at least in part, on sensor data about the external environment. Dynamically rendering the at least one graphical element improves the at least semi-autonomous operation of the vehicle through inducing vigilance and readiness within the operator with respect to the operating characteristics.

Abstract: A method and system for video encoding assets for swivel/360-degree spinners is disclosed. Still images of a 3D object from different perspectives about the 3D object may be stacked and then video encoded to generate video frames of the object from the different perspectives. The video-encoded assets may be stored on a server or other network-connected device, and later retrieved by a connected client device for display processing by a swivel/360-degree spinner on the client device. The swivel/360-degree spinner may utilize native video processing capabilities of the client device and/or of a browser running on the client device to display video motion of the object moving through different angular orientations in response to movement of an interactive cursor.

Abstract: Methods and systems for allocating tasks to robotic devices are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices and in a computing system configured to access a processor and memory, determining information associated with a health level for the plurality of robotic devices based on the information associated with the task logs. A health level for a given robotic device may be proportional to a current level of ability to perform a function, which may change over a lifespan of the given robotic device. Information associated with a plurality of tasks to be performed by one or more or the robotic devices may also be determined. The computing system may optimize an allocation of the plurality of tasks such that a high precision task may be allocated to a robotic device having a greater current health level than another robotic device.

Abstract: Methods and systems for proactively preventing hazardous or other situations in a robot-cloud interaction are provided. An example method includes receiving information associated with task logs for a plurality of robotic devices. The task logs may include information associated with tasks performed by the plurality of robotic devices. The method may also include a computing system determining information associated with hazardous situations based on the information associated with the task logs. For example, the hazardous situations may comprise situations associated with failures of one or more components of the plurality of robotic devices. According to the method, information associated with a contextual situation of a first robotic device may be determined, and when the information associated with the contextual situation is consistent with information associated with the one or more hazardous situations, an alert indicating a potential failure of the first robotic device may be provided.