Ford Global Technolgoies, LLC

Endre Hanak

Tarik Safir

Sam Harris

Jamie Mahmutyazicioglu

Jonathan Hannaford

Evangello Flouty

Stuart Alexander Lane

Benjamin George Brown

Examples provide systems and methods for detecting vehicle tamper events without relying on a clear line-of-sight. Namely, examples leverage an intelligent insight that many types of vehicle tamper events have unique audio signatures. Accordingly, examples detect/classify vehicle tamper events based on these unique audio signatures. Moreover, examples can verify these audio-based classifications by analyzing acceleration-related data (e.g., relative acceleration data for a body of a vehicle, relative jerk data for a body of a vehicle, etc.) to determine suspicious movement of a body of a vehicle during a potential/suspected vehicle tamper event. This acceleration-related verification step can reduce occurrence of false positive audio-based classifications caused by other noise events proximate to the vehicle that have similar audio signatures to vehicle tamper events (e.g., drilling or other noise from a construction site, rain, etc.).

Systems and methods of using a machine learning model to classify catalytic converter theft tamper events based on audio data

The systems and methods disclosed herein are configured to provide security to a vehicle. A system includes a point measurement device inside a space of a vehicle. The system identifies a threat if points that differ from a calibrated set of points are localized in an area of the space.

Systems and methods for providing security to a vehicle

The systems and methods disclosed herein are configured to unlock a vehicle. The systems use a hash function signature for authentication and use a vibration signal to transfer the hash function from a mobile device to a vehicle.

Systems and methods for unlocking a vehicle

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Colchester, United Kingdom

Endre Hanak