State Farm Mutual Automobile Insurance Company

Rajiv C Shah

John Adrian Nepomuceno

Steven C Cielocha

William J Leise

Kristopher Keith Gaudin

Leo Nelson Chan

Roxane Lyons

Edward P Matesevac

Jeremy Myers

Jennifer Criswell Kellett

Matthew S Megyese

Jennifer L Crawford

Lucas Allen

Jeffrey David Hevrin

Sripriya Sundararaman

Michael Shawn Jacob

Jeffrey Wilson Stoiber

Sunish Shreenarayan Menon

Phillip Sangpil Moon

Erin Ann Olander

Jeffrey Kinsey

Ronald R Duehr

Taylor Griffin Smith

Jing Wan

Jeremy Cunningham

Brad Sliz

Shannon Morrison

James Dykeman

Ellakate LeFebre

EllaKate LeFebre

Systems and methods are disclosed for estimating slipperiness of a road surface. This estimate may be obtained using an image sensor mounted on a vehicle. The estimated road slipperiness may be utilized when calculating a risk index for the road, or for an area including the road. If a predetermined threshold for slipperiness is exceeded, corrective actions may be taken. For instance, warnings may be generated to human drivers that are in control of driving vehicle, and autonomous vehicles may automatically adjust vehicle speed based upon road slipperiness detected.

System for determining road slipperiness in bad weather conditions

Systems and methods relate to, inter alia, calculating a collision risk index for an area based upon historical traffic data. The systems and methods may further generate a notification to automatically engage or disengage an autonomous, or semi-autonomous, vehicle control feature in a vehicle based upon the collision risk index for the area. The systems and methods may further transmit the notification to a device of the vehicle to facilitate automatically engaging or disengaging an autonomous, or semi-autonomous, vehicle control feature in the vehicle as the vehicle approaches the area. As a result, vehicle collisions may be reduced, and vehicle safety enhanced.

Collision risk-based engagement and disengagement of autonomous control of a vehicle

Systems and methods are disclosed for educating vehicle drivers. Auto insurance claim data may be analyzed to identify hazardous areas associated with an abnormally high amount or severity of vehicle collisions. A virtual navigation map of roads within the hazardous areas may be built or generated. A common cause of several vehicle collisions at a hazardous area may be identified, and a virtual reconstruction of a scenario involving the common cause and/or a road map of collisions locations of may be created. The virtual reconstruction of the scenario may be displayed on a driver education virtual simulator to enhance driver education and reduce the likelihood of vehicle collisions.

System for driver's education

A computer-implemented method for determining features of a dataset that are indicative of anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) executing a time series decomposition algorithm on a portion of the features of the data to generate a first list of features, (3) implementing a plurality of traffic dispersion graphs to generate a second list of features, and (4) implementing an autoencoder and a random forest regressor to generate a third list of features.

Detection of anomalous computer behavior

A computer-implemented method for detecting anomalous behavior of one or more computers in a large group of computers comprises (1) receiving log files including a plurality of entries of data regarding connections between a plurality of computers belonging to an organization and a plurality of websites outside the organization, each entry being associated with the actions of one computer, (2) applying a first plurality of algorithms to determine features of the data which may contribute to anomalous behavior of the computers, and (3) applying a second plurality of algorithms to determine which computers are behaving anomalously based upon the features.

Systems and methods are disclosed for identifying high risk parking lots. High risk parking lots may be, for example, parking lots that pose a higher than average risk of collisions and/or theft. Auto insurance claim data may be analyzed to identify hazardous areas. A virtual navigation of roads within the hazardous area may be identified. Public parking lots within the virtual navigation map may be defined, with each public parking lot determined as either in a hazardous area or not. A vehicle may be determined to be approaching or parking in a parking lot in a hazardous area, and a nearby public parking lot not associated with the hazardous area may be selected instead. A route from a current position to the nearby public parking lot may be generated, and the vehicle may be routed to the nearby public parking lot. As a result, collisions and thefts may be reduced.

System for identifying high risk parking lots

A system and method are provided for improving vehicle awareness and safety by generating and transmitting alerts in response to detecting a hazard in the environment omnidirectional to a vehicle awareness system. Omnidirectional environment data, representing kinematic information pertaining to one or more physically detectable elements omnidirectional to the primary vehicle, is acquired by one or more sensors communicatively coupled to ta vehicle. The system analyzes the omnidirectional environment data to detect if one or more hazards in the omnidirectional environment data, representing a change in the kinetic behavior of the one or more physically detectable elements omnidirectional to the vehicle awareness system, has occurred. When the system detects one or more hazards in the omnidirectional environment data, the system generates and transmits an alert to vehicles, vehicle operators, mobile devices, or pedestrians at risk from the hazard.

Networked vehicle control systems to facilitate situational awareness of vehicles

A computer-implemented method for identifying a property usage type based upon sensor data includes, with customer permission or affirmative consent, receiving data generated by various sensors; generating a report that includes a listing of events recorded by each sensor; analyzing data from the report to determine a property usage type score; receiving data regarding types and levels of insurance coverage associated with the property usage type score; receiving data derived from a homeowner's insurance policy; comparing the types and levels of insurance coverage associated with the property usage type score with the types and levels of insurance coverage from the homeowner's current insurance policy; and transmitting a message to the homeowner to update their insurance policy if there are differences between (i) the insurance coverage that the homeowner has, and (ii) the insurance coverage the homeowner should have based upon the property usage type score.

Identifying property usage type based upon smart sensor data

Systems and methods for control systems for facilitating situational awareness of a vehicle are provided. Sensors located around a stationary vehicle may detect moving objects that pose potentially dangerous situations for the vehicle occupants and for the object. This can cause the doors to lock automatically in order to make the situation safer. Additionally, electromagnetic radiation sensors located around the vehicle may detect the lights and sounds of emergency vehicles. If the electromagnetic data from the electromagnetic radiation sensors match a known pattern of electromagnetic data emitted by emergency vehicles stored in a library an alarm may be sounded through the speakers of the vehicle in order to alert the vehicle occupants of an emergency vehicle in the vicinity.

Systems and method for providing awareness of emergency vehicles

An automatic alarm system for a vehicle includes a sensor adapted to detect electromagnetic data, a computer system, and an automatic alarm mechanism. The computer system receives data from the sensor. A computer processor compares the electromagnetic data detected by the sensor with known patterns of electromagnetic data emitted by emergency vehicles that are stored in a library. The automatic alarm mechanism activates in response to the processor determining that the comparison matches a known pattern of electromagnetic data for an emergency vehicle resulting in the computer processor sending a first signal to activate an alarm of the vehicle.

Systems and methods for providing awareness of emergency vehicles

Systems and methods relate to, inter alia, calculating a number of expected collisions in an area over a time period. The systems and methods may further determine a number of observed collisions in the area over the time period. The systems and methods may further calculate a risk index for the area based upon a comparison between the number of expected collisions and the number of observed collisions. The systems and methods may further determine one or more travel routes for a vehicle based upon the calculated risk index. The systems and methods may further display a notification depicting the one or more travel routes to facilitate routing of the vehicle that avoids traversing the area.

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Bloomington, IL, United States of America

Rajiv C Shah