Toyota Connected North America, Inc.

University of Texas System

Christian Gilles Claudel

Raja Shekar Kilaru

Nimish Kumar Amlathe

Akshata Dundi Mohan

Brian M Kursar

Praveenkumar Pasupathy

Toshikazu Tanaka

Jorge Prozzi

Systems, methods, and other embodiments described herein relate to improving crash prediction through correlating an observed response and a predicted response of a vehicle. In one embodiment, a method includes generating the predicted response for the vehicle as a function of a response model and according to current vehicle inputs that are control inputs associated with steering, braking, and accelerating the vehicle. The response model is a learning model that predicts behaviors of the vehicle. The method includes computing a residual indicating an extent of correlation between the predicted response and the observed response. The method includes, in response to determining the residual satisfies a crash threshold that indicates an anomaly between the predicted response and the observed response, providing an alert indicating the vehicle has likely crashed.

Systems and methods for real-time crash detection using telematics data

System, methods, and other embodiments described herein relate to determining a location of a vehicle within a structure. In one embodiment, a method includes collecting, from at least one sensor in the vehicle, sensor data that indicates information associated with control of the vehicle in the structure. The method includes classifying gradients along a path of the vehicle through the structure according to the sensor data. The method includes identifying traversed levels of the structure along the path according to the gradients. The method includes providing the location including at least an indicator of the traversed levels as an electronic output.

Systems and methods for determining a location of a vehicle within a structure

A method, system and computer program product for calculating highway and airport pavement deflections. A pavement monitoring system includes a tractor and a semi-trailer, where a rigid horizontal beam is suspended under the semi-trailer. Various sensing elements are attached to the rigid beam, such as laser scanning sensors configured to measure a distance from the laser scanning sensors to the same point on the pavement at two different times, a gyrometer configured to estimate a raw pitch rate, an inertial measurement unit configured to estimate an orientation of the beam and accelerometers configured to measure an acceleration at a same level as corresponding laser scanning sensors. By utilizing these sensing elements, a more accurate pavement deflection is estimated by not relying on indirect measurements. Furthermore, the tractor and semi-trailer of the pavement monitoring system may travel at the velocity of traffic.

Measuring pavement deflections using data from laser scanning sensors as well as from high precision accelerometers and gyrometers

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Austin, TX, United States of America

Christian Gilles Claudel