Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can receive one or more electronic notifications broadcasted by a vehicle. In various aspects, the system can determine, via parsing, whether the one or more electronic notifications indicate that a vehicular collision not involving the vehicle has occurred in a vicinity of the vehicle. In various instances, the system can initiate, in response to a determination that the one or more electronic notifications indicate that the vehicular collision has occurred, one or more electronic actions based on the vehicular collision.

Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can be onboard a first vehicle. In various aspects, the system can capture, via one or more first cameras or one or more first microphones of the first vehicle, vicinity data associated with a first vicinity of the first vehicle. In various instances, the system can determine, via execution of a deep learning neural network on the vicinity data, whether a vehicular collision not involving the first vehicle has occurred in the first vicinity of the first vehicle. In various cases, the system can record, in response to a determination that the vehicular collision has occurred and via the one or more first cameras or the one or more first microphones, first post-collision evidence associated with the first vicinity of the first vehicle.

Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can be onboard a vehicle and can capture, via one or more cameras or microphones of the vehicle, vicinity data associated with a vicinity of the vehicle. In various instances, the system can generate, via execution of a deep learning neural network on the vicinity data, a classification label indicating whether a vehicular collision not involving the vehicle has occurred in the vicinity of the vehicle. In various cases, the system can record, in response to the classification label indicating that the vehicular collision has occurred and via the one or more cameras or the one or more microphones, post-collision evidence associated with the vicinity of the vehicle. In various aspects, the system can broadcast the classification label and the post-collision evidence to an emergency service computing device.

Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can be onboard a vehicle. In various aspects, the system can capture, via one or more cameras or one or more microphones of the vehicle, vicinity data associated with a vicinity of the vehicle. In various instances, the system can determine, via execution of a deep learning neural network on the vicinity data, whether a vehicular collision not involving the vehicle has occurred in the vicinity of the vehicle. In various cases, the system can broadcast, in response to a determination that the vehicular collision has occurred, via the one or more cameras or the one or more microphones, and to an emergency service computing device, a post-collision live stream associated with the vicinity of the vehicle.

Abstract: Systems/techniques that facilitate artificially intelligent provision of post-vehicular-collision evidence are provided. In various embodiments, a system can be onboard a vehicle. In various aspects, the system can capture, via one or more cameras or one or more microphones of the vehicle, vicinity data associated with a vicinity of the vehicle. In various instances, the system can determine, via execution of a deep learning neural network on the vicinity data, whether a vehicular collision not involving the vehicle has occurred in the vicinity of the vehicle. In various cases, the system can broadcast, in response to a determination that the vehicular collision has occurred and via the one or more cameras or the one or more microphones, one or more electronic notifications.

Abstract: Embodiments are directed towards controlling uncontrolled release of ammonia from an engine of a vehicle. An estimated status of the engine is determined prior to an event, such as an estimated load on the engine prior to the vehicle going up a hill. A predictive model of uncontrolled ammonia release is generated for the estimated status. At least one engine-related countermeasure is selected based on the predictive model. If the predictive model of uncontrolled ammonia release with the selected countermeasures satisfies a threshold condition, then the selected engine-related countermeasure is employed.

Abstract: Embodiments are directed towards controlling uncontrolled release of ammonia from an engine of a vehicle. An estimated status of the engine is determined prior to an event, such as an estimated load on the engine prior to the vehicle going up a hill. A predictive model of uncontrolled ammonia release is generated for the estimated status. At least one engine-related countermeasure is selected based on the predictive model. If the predictive model of uncontrolled ammonia release with the selected countermeasures satisfies a threshold condition, then the selected engine-related countermeasure is employed.

Abstract: Embodiments are directed towards controlling uncontrolled release of ammonia from an engine of a vehicle. An estimated status of the engine is determined prior to an event, such as an estimated load on the engine prior to the vehicle going up a hill. A predictive model of uncontrolled ammonia release is generated for the estimated status. At least one engine-related countermeasure is selected based on the predictive model. If the predictive model of uncontrolled ammonia release with the selected countermeasures satisfies a threshold condition, then the selected engine-related countermeasure is employed.

Abstract: Embodiments are directed towards controlling uncontrolled release of ammonia from an engine of a vehicle. An estimated status of the engine is determined prior to an event, such as an estimated load on the engine prior to the vehicle going up a hill. A predictive model of uncontrolled ammonia release is generated for the estimated status. At least one engine-related countermeasure is selected based on the predictive model. If the predictive model of uncontrolled ammonia release with the selected countermeasures satisfies a threshold condition, then the selected engine-related countermeasure is employed.

Abstract: A V2X communication device (41, 42, 43) receives service information (402, 403, 404) in a first frequency spectrum. The service information indicates availability of at least one V2X communication service in a second frequency spectrum. Based on the received service information (402, 403, 404), the V2X communication device (41, 42, 43) controls utilization of one or more V2X communication services by the V2X communication device (41, 42, 43), e.g., whether to start utilizing the at least one V2X service and/or whether to stop utilizing another V2X service.

Abstract: In some embodiments of the present disclosure, sensors mounted on a vehicle can allow opportunities for coasting to be predicted based on environmental conditions, route planning information, and/or vehicle-to-vehicle or vehicle-to-infrastructure signaling. In some embodiments of the present disclosure, these sensors can also predict a need for power and/or an end of a coast opportunity. These predictions can allow the vehicle to automatically enter a coasting state, and can predictively re-engage the engine and/or powertrain in order to make power available with no delay when desired by the operator.

Abstract: Systems and methods are disclosed for rendering a surface in a synthetic scene using gutter space padding for texture atlases. Gutter space adjacent to UV shells may be padded with texels from the UV shells used for neighboring surfaces, rather than merely extending the interior texels. The result is that distracting artifacts, caused by mesh downsampling that produces sub-optimal polygon shapes and vertex placement may be mitigated. The resulting rendering, manifesting lessened artifacts, may appear to be less impacted by downsampling, and thus improve the experience for the viewer or other synthetics data consumer. The improvement may be used in rendering for human users and also training neural networks in computer vision tasks, such as object detection and recognition.

Abstract: In some embodiments of the present disclosure, sensors mounted on a vehicle can allow opportunities for coasting to be predicted based on environmental conditions, route planning information, and/or vehicle-to-vehicle or vehicle-to-infrastructure signaling. In some embodiments of the present disclosure, these sensors can also predict a need for power and/or an end of a coast opportunity. These predictions can allow the vehicle to automatically enter a coasting state, and can predictively re-engage the engine and/or powertrain in order to make power available with no delay when desired by the operator.

Abstract: In some embodiments of the present disclosure, sensors mounted on a vehicle can allow opportunities for coasting to be predicted based on environmental conditions, route planning information, and/or vehicle-to-vehicle or vehicle-to-infrastructure signaling. In some embodiments of the present disclosure, these sensors can also predict a need for power and/or an end of a coast opportunity. These predictions can allow the vehicle to automatically enter a coasting state, and can predictively re-engage the engine and/or powertrain in order to make power available with no delay when desired by the operator.

Abstract: Systems and methods are disclosed for rendering a surface in a synthetic scene using gutter space padding for texture atlases. Gutter space adjacent to UV shells may be padded with texels from the UV shells used for neighboring surfaces, rather than merely extending the interior texels. The result is that distracting artifacts, caused by mesh downsampling that produces sub-optimal polygon shapes and vertex placement may be mitigated. The resulting rendering, manifesting lessened artifacts, may appear to be less impacted by downsampling, and thus improve the experience for the viewer or other synthetics data consumer. The improvement may be used in rendering for human users and also training neural networks in computer vision tasks, such as object detection and recognition.

Abstract: In some embodiments of the present disclosure, sensors mounted on a vehicle can allow opportunities for coasting to be predicted based on environmental conditions, route planning information, and/or vehicle-to-vehicle or vehicle-to-infrastructure signaling. In some embodiments of the present disclosure, these sensors can also predict a need for power and/or an end of a coast opportunity. These predictions can allow the vehicle to automatically enter a coasting state, and can predictively re-engage the engine and/or powertrain in order to make power available with no delay when desired by the operator.

Abstract: A mobile computing device presents a set of user interface (UI) elements and receives user input corresponding to a set of vehicle specification values (e.g., engine type, transmission type, axle ratio) via the set of UI elements. The mobile computing device obtains (e.g., from a remote computer system hosted by a vehicle manufacturer) a vehicle performance value (e.g., a fuel economy value or a greenhouse gas emission value), an optimization value for the vehicle performance value, and vehicle specification analysis information based at least in part on the set of vehicle specification values. The mobile computing device presents the vehicle performance value, the optimization value, and vehicle specification feedback to a user. The vehicle specification feedback is based at least in part on the optimization value and the vehicle specification analysis information.

Abstract: A system for training a neural network generates a plurality of training meshes based on an input mesh. The plurality of training meshes include at least one mesh perceptually similar to the input mesh and one arbitrarily selected mesh perceptually dissimilar to the input mesh. The neural network is trained using the input mesh and the plurality of training meshes by tuning output of the neural network to identify similar non-identical meshes. The trained neural network is robust in identifying meshes similar to an unknown mesh input to the trained neural network.

Abstract: The present invention relates to the use of aleglitazar in the treatment or prevention of diabetic kidney disease in a patient having a linear decline in GFR.

Abstract: In some embodiments of the present disclosure, sensors mounted on a vehicle can allow opportunities for coasting to be predicted based on environmental conditions, route planning information, and/or vehicle-to-vehicle or vehicle-to-infrastructure signaling. In some embodiments of the present disclosure, these sensors can also predict a need for power and/or an end of a coast opportunity. These predictions can allow the vehicle to automatically enter a coasting state, and can predictively re-engage the engine and/or powertrain in order to make power available with no delay when desired by the operator.