Allstate Insurance Company

Here Global B.v.

Arity International Limited

Navteq B.v.

Mark V Slusar

Surender Kumar

Timothy W Gibson

Regina Madigan

Howard Hayes

Joseph Alan Kleinhenz

Eric D Huls

Clint J Marlow

Chris Wiklund

Sunil Kumar Chintakindi

Ryan Mark Briggs

Ken Prchal

Julie A Jordan Peters

Jason Brian Bogovich

William Polisson

Younuskhan Mohamed Iynoolkhan

Nilesh Malpekar

Dana Ferguson

Grady Irey

Caleb Brian Slaughter Gore

Colton J Reif

Robert Andrew Kreek

Patrick O'Reilly

Robert Andrew Nendorf

Meg G Walters

Anna Reifman

William F Polisson, Jr

Gabriel Federico Carballo Nunez

Patrick O'REILLY

Event-based and subscription-based connected vehicle control and response systems, methods, and apparatus are disclosed. An example method comprises receiving first data acquired by a first plurality of vehicle operation sensors of a first vehicle from a first computing device associated with a first subscriber, identifying an occurrence of an event involving the first vehicle, requesting second data acquired by one or more responsive object sensors in a vicinity of the event, determining a causation of the event, generating a fault distribution, and transmitting the fault distribution to the computing device of the first subscriber.

Subscription-based and event-based connected vehicle control and response systems

Systems and methods are disclosed for determining the efficacy of an autonomous vehicle driving system in exhibiting safe driving behavior during test track scenarios. Testing parameters eliciting autonomous vehicle driving system behavior may be chosen for the test track scenario. The autonomous vehicle driving system may be tested in the test track and autonomous vehicle driving system behavior may be observed in response to the presentations of testing parameters. A safe driving score may be calculated for the autonomous vehicle driving system based on measured autonomous vehicle performance and operational data generated responsive to the presentation of testing parameters in the test track. A benchmark insurance premium may be calculated for an autonomous vehicle driving system based on the calculated safe driving score.

Testing autonomous cars

Systems and methods are disclosed for determining that an adverse driving event is likely to occur and utilizing accident calculus algorithms to determine and cause vehicle driving actions necessary to mitigate consequences of the adverse driving event. After determining that an adverse driving event is likely to occur, a computing device my forecast consequences of the driving event. The computing device may determine potential evasive maneuvers that may be taken responsive to the adverse driving event. Additionally, the computing device may determine consequences associated with the potential evasive maneuvers and assign a weight relative to the consequence. The computing device may compare the potential driving maneuvers based on the weighted consequences to determine a driving maneuver to take.

Accident prediction and consequence mitigation calculus

A system may be configured to detect an emergency condition at a premises; dispatch one or more autonomous drones to a location associated with the emergency condition; receive from the one or more autonomous drones, sensor data associated with the emergency condition; generate, based on the sensor data, a plan identifying an evacuation route for safely evacuating the premises; and transmit an instruction that causes the one or more autonomous drones to indicate, to one or more persons in a vicinity of the emergency condition, the evacuation route. The system may further detect, based on the sensor data, one or more safe areas in the premises, and the evacuation route may pass through at least one of the one or more safe areas.

Emergency incident detection, response, and mitigation using autonomous drones

System and methods are disclosed for determining, through vehicle-to-vehicle communication, whether vehicles are involved in autonomous droning. Vehicle driving data and other information may be used to calculate an autonomous droning reward amount. In addition, vehicle involved in a drafting relationship in addition to, or apart from, an autonomous droning relationship may be financially rewarded. Moreover, aspects of the disclosure related to determining ruminative rewards and/or aspects of vehicle insurance procurement/underwriting.

Reward system related to a vehicle-to-vehicle communication system

One or more driving analysis computing devices in a driving analysis system may be configured to analyze driving data, determine driving behaviors, and calculate driver scores based on driving data transmitted using vehicle-to-vehicle (V2V) communications. Driving data from multiple vehicles may be collected by vehicle sensors or other vehicle-based systems, transmitted using V2V communications, and then analyzed and compared to determine various driving behaviors by the drivers of the vehicles. Driver scores may be calculated or adjusted based on the determined driving behaviors of vehicle drivers, and also may be calculated or adjusted based on other the driver scores of nearby vehicles.

Driving analysis using vehicle-to-vehicle communication

Event-based connected vehicle control and response systems, methods, and apparatus are disclosed. An example method comprises identifying the occurrence of an event, storing first data corresponding to apparatus operation for a first threshold amount of time prior to the event, during the occurrence of the event, and for a second threshold amount of time after the event, determining whether a responsive object is involved in or near the event, in response to determining that the responsive object is involved in or near the event, transmitting the first data to the responsive object, and receiving, from the responsive object, second data, analyzing the first data and the second data to determine a party at-fault for the event, aggregating the first data and second data into an event report, and causing, automatically, a response to be initiated through an entity associated with the party at-fault.

Event-based connected vehicle control and response systems

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.

Generating and transmitting parking instructions for autonomous and non- autonomous vehicles

Aspects of the disclosure relate to controlling autonomous vehicles to provide automated emergency response functions. A computing platform may receive vehicle data associated with a vehicle from an on-board vehicle monitoring system associated with the vehicle. Subsequently, the computing platform may detect an occurrence of an emergency at a location. Thereafter, the computing platform may select an autonomous vehicle to respond to the emergency at the location based on autonomous vehicle state information. Then, the computing platform may generate one or more dispatch commands directing the autonomous vehicle to move to the location and execute one or more emergency response functions. Subsequently, the computing platform may send, to an on-board autonomous vehicle control system associated with the autonomous vehicle, the one or more dispatch commands directing the autonomous vehicle to move to the location and execute the one or more emergency response functions.

Controlling autonomous vehicles to provide automated emergency response functions

Systems and methods are disclosed for managing the processing and execution of models that may have been developed on a variety of platforms. A multi-model execution module specifying a sequence of models to be executed may be determined. A multi-platform model processing and execution management engine may execute the multi-model execution module internally, or outsource the execution to a distributed model execution orchestration engine. A model data monitoring and analysis engine may monitor the internal and/or distributed execution of the multi-model execution module, and may further transmit notifications to various computing systems.

Multi-platform model processing and execution management engine

System, apparatus, and methods are disclosed for determining, through vehicle-to-vehicle communication, a drafting characteristic of a drafting relationship using vehicle operational data, where the drafting characteristic may include one or more of a vehicle spacing between a first vehicle and a second vehicle, vehicle speed, and vehicle type. Vehicle driving data and other information may be used to calculate an autonomous droning reward amount. In addition, vehicle involved in a drafting relationship in addition to, or apart from, an autonomous droning relationship may be financially rewarded. Moreover, aspects of the disclosure related to determining ruminative rewards and/or aspects of vehicle insurance procurement/underwriting.

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.

Intelligent structural protection systems and methods

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.

Polynomial risk maps

A method is disclosed for analyzing historical accident information to adjust driving actions of an autonomous vehicle over a travel route in order to avoid accidents which have occurred over the travel route. Historical accident information for the travel route can be analyzed to, for example, determine accident types which occurred over the travel route and determine causes and/or probable causes of the accident types. In response to determining accident types and causes/probable causes of the accident types over the travel route, adjustments can be made to the driving actions planned for the autonomous vehicle over the travel route. In addition, in an embodiment, historical accident information can be used to analyze available travel routes and select a route which presents less risk of accident than others.

Route risk mitigation

Aspects of the disclosure relate to controlling an autonomous vehicle to respond to a detected collision. An autonomous vehicle control system may receive sensor data associated with an autonomous vehicle in which the autonomous vehicle control system is installed. The autonomous vehicle control system may analyze the sensor data in real-time as the sensor data is received and may detect an occurrence of a collision involving the autonomous vehicle. In response to detecting the occurrence of the collision, the autonomous vehicle control system may generate claim information based on the sensor data and may process the claim information based on at least one insurance profile maintained by the autonomous vehicle control system. Then, the autonomous vehicle control system may generate a claim notification based on processing the claim information and may send the claim notification to a vehicle management computer system.

Growing community of inventors

Chicago, IL, United States of America

Mark V Slusar