Abstract: A system including a computing device may receive base map information, including attribute information associated with a plurality of road segments, and trip request information. Based on this information, a route for the user to travel may be determined. The system might further calculate a risk score for each road segment forming the route, and generate a risk map based on the risk score and the route. The risk map may then be displayed to a user. The risk map may include markers or other objects depicting potential risks along the route the driver may face. Also, the risk map may be updated based on information collected from a sensor coupled to the vehicle or located at the road segment to reflect actual, real-time risk scores calculated using an equation for providing a risk score for a particular driver driving a particular vehicle on a particular road segment.

Abstract: Systems and methods for deployment of a protective component, generation of a customized design for the protective component, or combinations thereof are associated with a structure comprising a portion, a neural network model, processor(s), and memory storing machine readable instructions. When executed for deployment, the neural network model predicts the likelihood of the occurrence of the natural event in the geographic area within the time frame as high as defined by when the likelihood is above a threshold, and deploys the protective component for protecting the portion of the structure when the likelihood is high. For customized design, the neural network model is used to access dimension and weather data associated with a structure and weather data to generate the customized design of the protective component for the structure.

Abstract: Aspects of the disclosure relate to controlling autonomous vehicles to optimize traffic characteristics. A computing platform may receive vehicle guidance data from autonomous vehicle control systems of vehicles. Subsequently, the computing platform may identify a number of the vehicles currently operating in an autonomous mode based on the vehicle guidance data. Thereafter, the computing platform may identify a target number of the vehicles to be operated in an autonomous mode in order to optimize traffic characteristics. Then, the computing platform may generate messages directing selected vehicles to switch into autonomous mode in order to achieve the target number. Subsequently, the computing platform may send the messages directing the selected vehicles to switch into autonomous mode in order to receive incentives. Thereafter, the computing platform may award the incentives to the selected vehicles that switch into the autonomous mode as directed by the messages.

Abstract: A system including a computing device may receive base map information, including attribute information associated with a plurality of road segments, and trip request information. Based on this information, a route for the user to travel may be determined. The system might further calculate a risk score for each road segment forming the route, and generate a risk map based on the risk score and the route. The risk map may then be displayed to a user. The risk map may include markers or other objects depicting potential risks along the route the driver may face. Also, the risk map may be updated based on information collected from a sensor coupled to the vehicle or located at the road segment to reflect actual, real-time risk scores calculated using an equation for providing a risk score for a particular driver driving a particular vehicle on a particular road segment.

Abstract: Systems and methods for deployment of a protective component, generation of a customized design for the protective component, or combinations thereof are associated with a structure comprising a portion, a neural network model, processor(s), and memory storing machine readable instructions. When executed for deployment, the neural network model predicts the likelihood of the occurrence of the natural event in the geographic area within the time frame as high as defined by when the likelihood is above a threshold, and deploys the protective component for protecting the portion of the structure when the likelihood is high. For customized design, the neural network model is used to access dimension and weather data associated with a structure and weather data to generate the customized design of the protective component for the structure.

Abstract: A system including a computing device may receive base map information, including attribute information associated with a plurality of road segments, and trip request information. Based on this information, a route for the user to travel may be determined. The system might further calculate a risk score for each road segment forming the route, and generate a risk map based on the risk score and the route. The risk map may then be displayed to a user. The risk map may include markers or other objects depicting potential risks along the route the driver may face. Also, the risk map may be updated based on information collected from a sensor coupled to the vehicle or located at the road segment to reflect actual, real-time risk scores calculated using an equation for providing a risk score for a particular driver driving a particular vehicle on a particular road segment.

Abstract: A method, medium, and apparatus for educating and reducing risk to inexperienced drivers using vehicles with autonomous navigation systems. Data regarding a driver's past experience with vehicles and operating environments may be used to proactively warn the driver about a potential danger detected or predicted by the vehicle. An autonomous vehicle may prevent the driver from operating the vehicle under unfamiliar circumstances or from causing a collision. Data regarding a driver's past experience with vehicles and the safety features thereof may be used to mitigate risk of injury or property damage by selectively activating safety features in a new vehicle which the driver has not previously driven. Data regarding a driver's past experience with vehicles and safety features thereof may be used to determine a decreased or increased rental rate for a particular vehicle.

Abstract: Systems and apparatuses for receiving data from a plurality of sensors and using the data, as well as other data, to generate a parking recommendation for a first vehicle are provided. Data may be received from sensors associated with a first vehicle for which a parking recommendation may be generated. Data may also be received from sensors associated with other vehicles and/or from one or more structures or other non-vehicle devices. In some examples, historical parking data may be extracted from a database. The collected and extracted data may be used to generate a parking recommendation for the first vehicle. In some examples, pre-stored user preferences, may be used in generating the parking recommendation as well. The parking recommendation may then be transmitted to a computing device within the first vehicle and may be displayed on the computing device.

Abstract: A method, medium, and apparatus for educating and reducing risk to inexperienced drivers using vehicles with autonomous navigation systems. Data regarding a driver's past experience with vehicles and operating environments may be used to proactively warn the driver about a potential danger detected or predicted by the vehicle. An autonomous vehicle may prevent the driver from operating the vehicle under unfamiliar circumstances or from causing a collision. Data regarding a driver's past experience with vehicles and the safety features thereof may be used to mitigate risk of injury or property damage by selectively activating safety features in a new vehicle which the driver has not previously driven. Data regarding a driver's past experience with vehicles and safety features thereof may be used to determine a decreased or increased rental rate for a particular vehicle.

Abstract: Systems including one or more sensors, coupled to a vehicle, may detect sensor information and provide the sensor information to another computing device for processing. The processing may include analyzing the sensor information to identify one or more risk objects, such as animals, pedestrians, potholes, etc. The processing may further include generating a three-dimensional (3D) risk map, which may be displayed to a passenger in the vehicle. The 3D risk map may illustrate the one or more risk objects as one or more point clouds, respectively, within a virtual world representation of the vehicle's surroundings. Such a display may be used to alert drivers to possible risks while driving. In case of an accident, the 3D risk map may also be leveraged by insurance providers to process insurance claims and to notify customers that an insurance claim has been established.

Abstract: Systems and apparatuses for receiving data from a plurality of sensors and using the data, as well as other data, to generate a parking recommendation for a first vehicle are provided. Data may be received from sensors associated with a first vehicle for which a parking recommendation may be generated. Data may also be received from sensors associated with other vehicles and/or from one or more structures or other non-vehicle devices. In some examples, historical parking data may be extracted from a database. The collected and extracted data may be used to generate a parking recommendation for the first vehicle. In some examples, pre-stored user preferences, may be used in generating the parking recommendation as well. The parking recommendation may then be transmitted to a computing device within the first vehicle and may be displayed on the computing device.

Abstract: Systems including one or more sensors, coupled to a vehicle, may detect sensor information and provide the sensor information to another computing device for processing. The processing may include analyzing the sensor information to identify one or more risk objects, such as animals, pedestrians, potholes, etc. The processing may further include generating a three-dimensional (3D) risk map, which may be displayed to a passenger in the vehicle. The 3D risk map may illustrate the one or more risk objects as one or more point clouds, respectively, within a virtual world representation of the vehicle's surroundings. Such a display may be used to alert drivers to possible risks while driving. In case of an accident, the 3D risk map may also be leveraged by insurance providers to process insurance claims and to notify customers that an insurance claim has been established.

Abstract: The disclosed embodiments relate to recognition of navigation and/or guidance related information within an expressed conversational narrative between a provider of the narrative and a receiver thereof. The recognized navigation and/or guidance related information is then converted into navigation data which may be used to present a navigation route and/or guidance information related thereto to the receiver.

Abstract: A method, medium, and apparatus for educating and reducing risk to inexperienced drivers using vehicles with autonomous navigation systems. Data regarding a driver's past experience with vehicles and operating environments may be used to proactively warn the driver about a potential danger detected or predicted by the vehicle. An autonomous vehicle may prevent the driver from operating the vehicle under unfamiliar circumstances or from causing a collision. Data regarding a driver's past experience with vehicles and the safety features thereof may be used to mitigate risk of injury or property damage by selectively activating safety features in a new vehicle which the driver has not previously driven. Data regarding a driver's past experience with vehicles and safety features thereof may be used to determine a decreased or increased rental rate for a particular vehicle.

Abstract: A system including a computing device may receive base map information, including attribute information associated with a plurality of road segments, and trip request information. Based on this information, a route for the user to travel may be determined. The system might further calculate a risk score for each road segment forming the route, and generate a risk map based on the risk score and the route. The risk map may then be displayed to a user. The risk map may include markers or other objects depicting potential risks along the route the driver may face. Also, the risk map may be updated based on information collected from a sensor coupled to the vehicle or located at the road segment to reflect actual, real-time risk scores calculated using an equation for providing a risk score for a particular driver driving a particular vehicle on a particular road segment.

Abstract: Aspects of the disclosure relate to controlling autonomous vehicles to optimize traffic characteristics. A computing platform may receive vehicle guidance data from autonomous vehicle control systems of vehicles. Subsequently, the computing platform may identify a number of the vehicles currently operating in an autonomous mode based on the vehicle guidance data. Thereafter, the computing platform may identify a target number of the vehicles to be operated in an autonomous mode in order to optimize traffic characteristics. Then, the computing platform may generate messages directing selected vehicles to switch into autonomous mode in order to achieve the target number. Subsequently, the computing platform may send the messages directing the selected vehicles to switch into autonomous mode in order to receive incentives. Thereafter, the computing platform may award the incentives to the selected vehicles that switch into the autonomous mode as directed by the messages.

Abstract: Aspects of the disclosure relate to controlling autonomous vehicles to optimize traffic characteristics. A computing platform may receive vehicle guidance data from autonomous vehicle control systems of vehicles. Subsequently, the computing platform may identify a number of the vehicles currently operating in an autonomous mode based on the vehicle guidance data. Thereafter, the computing platform may identify a target number of the vehicles to be operated in an autonomous mode in order to optimize traffic characteristics. Then, the computing platform may generate messages directing selected vehicles to switch into autonomous mode in order to achieve the target number. Subsequently, the computing platform may send the messages directing the selected vehicles to switch into autonomous mode in order to receive incentives. Thereafter, the computing platform may award the incentives to the selected vehicles that switch into the autonomous mode as directed by the messages.

Abstract: Methods, computer-readable media, systems and apparatuses for determining and implementing risk unit usage-based insurance policies. In some arrangements, sensor data associated with vehicle operation data, driving data, and the like, may be received and analyzed to determine a consumption rate of risk units in the risk unit usage-based insurance policy. In some examples, one or more driving behaviors may also be identified (e.g., from the sensor data, historical data, and the like). In some examples, one or more user interfaces may be generated displaying the determined consumption rate and/or driving behaviors. In some arrangements, additional information, such as one or more recommendations for improving (e.g., reducing) consumption rate may be generated and provided via the generated user interface.

Abstract: A fuel analysis system is described configured to assist vehicle drivers/users in preventing damage to their vehicles caused by bad fuel. Bad fuel can leave a driver and passengers stranded on the road in need of emergency road side service, and in many instances, results in permanent damage to the vehicle. The disclosed fuel analysis system describes an enhanced bad fuel sensor system that measures a delta in vehicle operation data to identify and in many instances, pre-emptively alert, a user of a vehicle of bad fuel. The fuel analysis system may use crowdsourcing through aggregation of refueling event profile records from a plurality of vehicles' telematics devices to increase the accuracy with which bad fuel is detected.

Abstract: A system including a computing device may receive base map information, including attribute information associated with a plurality of road segments, and trip request information. Based on this information, a route for the user to travel may be determined. The system might further calculate a risk score for each road segment forming the route, and generate a risk map based on the risk score and the route. The risk map may then be displayed to a user. The risk map may include markers or other objects depicting potential risks along the route the driver may face. Also, the risk map may be updated based on information collected from a sensor coupled to the vehicle or located at the road segment to reflect actual, real-time risk scores calculated using an equation for providing a risk score for a particular driver driving a particular vehicle on a particular road segment.