Cambridge Mobile Telematics Inc.

Discovery, Inc.

Samuel Ross Madden

Hari Balakrishnan

William Francis Bradley

Jun-geun Park

Lewis David Girod

James E Hicks, Jr

Ben Bowne

Katherine Wellman

Robert Scott Kobetis

Brian Sachetta

Rafi Finegold

Sanujit Sahoo

Ilan Ossin

Paresh Govind Malalur

Arash Hadipanah

Lisa Pinals

Arash Hadapanah

Gábor Tyukász

Gregory David Padowski

Aspects of the embodiments include receiving, from one or more telematics sensors in a vehicle and by the one or more data processing systems, a plurality of telematics data items that are sampled at a plurality of sample times. For a given sample time, accessing a given telematics data item that satisfies the one or more conditions, computing a fuel consumption amount at the given sample time, and determining a distance traveled by the vehicle from a previous sample time that is prior to the given sample time, to the given sample time. For each telematics data items that satisfies the conditions, computing a total distance and a total fuel consumption amount, with the computing based on distances traveled and fuel consumption amounts for the plurality of telematics data items; and determining a fuel efficiency score the total fuel consumption amount and the total distance or a total time.

Method and system for obtaining vehicle fuel efficiency

A system can include a mobile device that includes one or more sensors for sensing information during a trip in a vehicle. A hardware processor can execute operations including receiving telematics information produced by one or more sensors during a trip in a vehicle; processing, by a hardware processor, the received telematics information to identify vehicle movement information for the vehicle during the trip; determining, by the hardware processor, a probability that an advanced driver assistance system (ADAS) feature of the vehicle was operational during the trip based, at least in part, on the vehicle movement information for the vehicle during the trip; and determining, by the hardware processor, a risk score for the vehicle or a driver of the vehicle based, at least in part, on the probability that the ADAS feature was operational.

Detecting use of driver assistance systems

Among other things, safety performance of vehicles is monitored or evaluated, or both. Sensor data is received from one or more sensors at a vehicle. One or more risk sources encountered by the vehicle during a period of operation of the vehicle are identified based on the sensor data. One or more safety events experienced by the vehicle during the period of operation are identified based on the sensor data. A risk of a safety event experienced by the vehicle during the period of operation is determined based on the one or more risk sources. A safety performance of the vehicle during the period of the vehicle during the period of operation of the vehicle is determined based on the one or more safety events and the risk.

Evaluating the safety performance of vehicles

Personal safety concerns for users of vehicles can be indicated, identified, communicated, analyzed, or acted on to make the users and other participants in the technology aware of the safety concerns and to reduce the risks to the users associated with the safety concerns. Personal safety concerns can be recognized based on safety concern triggers. Once recognized, the personal safety concerns can be reported to the users and other participants in the technology by safety alerts. The safety alert can prompt one or more telematics devices at the vehicle to capture, store, or transmit telematics data, including, for example, audio, image, or video data or combinations of them. The captured telematics data can be used to verify the safety alert and the safety concern and present the captured data to a third party participant to enable the third party participant to determine an appropriate response or action.

Safety for vehicle users

Among other things, instructions stored in a tangible storage medium are executable by a processor to receive data representing an attitude of a vehicle being driven relative to a frame of reference and process the received attitude data to determine a risk factor in the driving of the vehicle.

Reducing driving risk

A method for providing proactive driving alerts includes determining a road segment of interest, receiving GPS data and contextual data, and identifying a triggering condition in a vicinity of the segment of interest. The method also includes determining an applicability of the triggering condition to the segment of interest, retrieving an alert threshold corresponding to the triggering condition, and obtaining an action factor correlating to a user's driving behavior. The method further includes determining whether the action factor exceeds the alert threshold and providing a proactive alert related to the triggering condition to the user.

Method and system for driver alerts based on sensor data and contextual information

Among other things, information generated by sensors of a mobile phone and indicative of motion of the mobile phone and state information indicative of a state of operation of the mobile phone are monitored. Based on the monitoring, distraction by a user of the mobile phone who is a driver of a vehicle is determined.

Determining, scoring, and reporting mobile phone distraction of a driver

Data processing techniques and systems for processing telematics data associated with a vehicle, such as an automobile, to classify how the vehicle is being operated by a driver. The telematics data can include the use of image data captured by a camera of the vehicle. The image data is processed in conjunction with vehicular telematics data such as position, speed, and acceleration data of the vehicle obtained from, for example, smartphone sensors. The image data is processed and used by a processing system to provide a context for the telematics data. The image data and the telematics data are classified by the processing system to identify driving behavior of the driver, determine driving maneuvers that have occurred, scoring driving quality of the driver, or a combination of them.

Systems and methods for classifying driver behavior

A lock button of a mobile device with an iOS operating system, to be used by a user to effect an action when the mobile device is in the locked state, is enabled for other functions by executing an application on the mobile device. When the mobile device is in a locked state, the application is in a dormant mode. The OS of the mobile device monitors for a change of location of the device and/or for the receipt of a short range wireless data signal. Upon detecting either, the operating system communicates this to the application which in response enters a background mode and monitors the on/off state of a screen of the mobile device. If the screen state changes at least twice within a predetermined period of time, the application effects an action, such as sending a panic signal.

Triggering application action using non-standard controls

Accurate longitudinal acceleration, lateral acceleration (perpendicular to the principal direction of motion, and velocity, is inferred by processing raw data from a commodity three-axis accelerometer that may be oriented arbitrarily in a moving vehicle (or carried by a moving user), and whose orientation and position may change arbitrarily during the motion. The approach is applicable to a range of applications, including insurance telematics, driver behavior and risk assessment, and road surface quality assessment.

Growing community of inventors

Newton, MA, United States of America

Samuel Ross Madden