Abstract: Systems, methods, apparatuses, and computer-readable media for receiving data from one or more sensors associated with one or more home devices of a user, such as appliances, home systems, etc., receiving data from one or more sensors associated with a vehicle of the user, and/or receiving data associated with a lifestyle of the user are presented. In some examples, the data may aggregated and analyzed to assess risk associated with the user in order to determine or adjust an insurance rate, premium, and/or incentive.

Abstract: Methods, computer-readable media, software, and apparatuses may determine whether a human driver, or an autonomous driving system, should be in control of a vehicle in response to a detection of an unexpected event. A decision may be made to pass control from the autonomous driving system to the human driver, if it is determined that the human driver can handle the unexpected event more safely than the autonomous vehicle.

Abstract: Aspects of the disclosure relate to a dynamic distance estimation output platform that utilizes improved computer vision and perspective transformation techniques to determine vehicle proximities from video footage. A computing platform may receive, from a visible light camera located in a first vehicle, a video output showing a second vehicle that is in front of the first vehicle. The computing platform may determine a longitudinal distance between the first vehicle and the second vehicle by determining an orthogonal distance between a center-of-projection corresponding to the visible light camera, and an intersection of a backside plane of the second vehicle and ground below the second vehicle. The computing platform may send, to an autonomous vehicle control system, a distance estimation output corresponding to the longitudinal distance, which may cause the autonomous vehicle control system to perform vehicle control actions.

Abstract: Aspects of the disclosure relate to dynamic driving metric output platforms that utilize improved computer vision methods to determine vehicle metrics from video footage. A computing platform may receive video footage from a vehicle camera. The computing platform may determine that a reference marker in the video footage has reached a beginning and an end of a road marker based on brightness transitions, and may insert time stamps into the video accordingly. Based on the time stamps, the computing platform may determine an amount of time during which the reference marker covered the road marking. Based on a known length of the road marking and the amount of time during which the reference marker covered the road marking, the computing platform may determine a vehicle speed. The computing platform may generate driving metric output information, based on the vehicle speed, which may be displayed by an accident analysis platform.

Abstract: Systems and methods are provided for automatically changing or updating insurance coverage using a computer device. An insurance analyzer may receive data associated with an insurance user. The insurance analyzer may determine types of insurance coverage available and coverage amounts associated with the received user data. Based on the received data the insurance analyzer may automatically activate insurance coverage based on determined types of insurance available and coverage amounts. The insurance analyzer may also deactivate insurance coverage or change insurance coverage amounts based on determined user activity.

Abstract: Methods, computer-readable media, systems, and/or apparatuses for providing offer and output generation functions are provided. For instance, a request for an output may be received by a device. In response, user physical trait data, pre-stored data, location data, and the like, may be extracted and received from a plurality of sources. Machine learning may be used to analyze the data to predict one or more factors associated with the user and the output or offer may be generated based on the predicted factors.

Abstract: Aspects of the disclosure relate to an autonomous vehicle evaluation system that performs continuous evaluation of the actions, strategies, preferences, margins, and responses of an autonomous driving control system. A computing platform may receive sensor data from one or more autonomous vehicle sensors, manufacturer computing platform, or V2X computing platform. Based on this sensor data, the computing platform may determine one or more driving patterns. Based on a primary context corresponding to the one or more driving patterns, the computing platform may group the one or more driving patterns. The computing platform may determine a driving pattern degradation output indicating degradation corresponding to the one or more grouped driving patterns, and the computing platform may send the driving pattern degradation output to an autonomous driving system, which may cause the autonomous driving system to take corrective action accordingly.

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: Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to utilizing partially available data associated with driver and/or driving behaviors to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

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: Methods, computer-readable media, software, and apparatuses may determine whether a human driver, or an autonomous driving system, should be in control of a vehicle in response to a detection of an unexpected event. A decision may be made to pass control from the autonomous driving system to the human driver, if it is determined that the human driver can handle the unexpected event more safely than the autonomous 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 an autonomous vehicle and instruct the autonomous vehicle to travel to the recommended parking location are provided. Data may be received from a plurality of sensors within a first autonomous vehicle, as well as from other vehicles and/or structures. Historical parking data associated with the first autonomous vehicle may also be extracted. In some examples, an expected future trip of the first autonomous vehicle may be determined. The system may then evaluate the data to generate a parking recommendation for the first autonomous vehicle. The system may generate and transmit instructions for traveling from a current location to the recommended parking location and may cause the first autonomous vehicle to travel to the recommended parking location.

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: Methods, computer-readable media, systems, and apparatuses for determining a safest road segment for traveling between two locations are provided. In some cases, a road segment safety rating may be determined for a plurality of road segments between the two locations based on historical data associated with the road segments and/or based on driving behavior data of an operator of the vehicle. An indication of the road segment determined as being the safest may be provided to the operator of the vehicle. An actual route of travel of the vehicle may be compared to the safest route. If the actual route of travel is different from the determined safest route, the operator may be notified that an award may be earned when the determined safest route is traveled. If the actual route of travel is determined to be safer than the determined safest route, an award may be earned.

Abstract: Exhaustive driving analytical methods, systems, are apparatuses are described. The methods, systems, are apparatuses relate to utilizing partially available data associated with driver and/or driving behaviors to determine safety factors, identify times to react to events, and contextual information regarding the events. The methods, systems, and apparatuses described herein may determine, based on a systematic model, reactions and reaction times, compare the vehicle behavior (or lack thereof) to the modeled reactions and reaction times, and determine safety factors and instructions based on the comparison.

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 an autonomous vehicle and instruct the autonomous vehicle to travel to the recommended parking location are provided. Data may be received from a plurality of sensors within a first autonomous vehicle, as well as from other vehicles and/or structures. Historical parking data associated with the first autonomous vehicle may also be extracted. In some examples, an expected future trip of the first autonomous vehicle may be determined. The system may then evaluate the data to generate a parking recommendation for the first autonomous vehicle. The system may generate and transmit instructions for traveling from a current location to the recommended parking location and may cause the first autonomous vehicle to travel to the recommended parking location.

Abstract: Methods, computer-readable media, software, and apparatuses provide a tool for use by drivers and/or coaches throughout the pre-license stage of obtaining a driver’s license. A pre-license program may control a computing device to collect drive data while a driver is driving a vehicle. This drive data may be used to detect a drive event. Then, the computing device may present coaching information associated with the detected drive event. The coaching information may provide a passenger, such as a coach or parent, with real-time advice for instructing the driver how to improve his/her driving skills. Moreover, the drive data collected may be used to prepare reports providing feedback to the drivers and coaches. Further, the pre-license program may determine an expected track for a driver to follow to prepare for a driver’s license test and may indicate whether the driver is on or off the track.

Abstract: Methods, computer-readable media, systems, and apparatuses for determining a safest road segment for traveling between two locations are provided. In some cases, a road segment safety rating may be determined for a plurality of road segments between the two locations based on historical data associated with the road segments and/or based on driving behavior data of an operator of the vehicle. An indication of the road segment determined as being the safest may be provided to the operator of the vehicle. An actual route of travel of the vehicle may be compared to the safest route. If the actual route of travel is different from the determined safest route, the operator may be notified that an award may be earned when the determined safest route is traveled. If the actual route of travel is determined to be safer than the determined safest route, an award may be earned.

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.