Ford Global Technolgoies, LLC

University of Michigan

Carnegie Mellon University

Yu Seung Kim

Jinhyoung Oh

Chun-Yu Chen

Patrick Tague

Kang Geun Shin

Yun Ho Lee

Byungho Cha

Thanh Le Nguyen

Jun Han

Joy Zhang

Disclosed are systems and methods to detect and identify vehicular anomalies. Techniques to detect and identify the vehicular anomalies include receiving signals from various sensors, grouping the signals into detection sets, detecting anomalies by a comparison to vehicle behavior models, and cross-referencing the detection sets with each other to narrow down and identify the source of the anomaly. The detection sets may be grouped such that facets of vehicle maneuverability are captured and cover causal relations between different maneuverability mechanisms.

Automated detection of vehicle data manipulation and mechanical failure

A method for detecting communication jamming attacks includes collecting, via a processor associated with a base station, local jamming information from a first vehicle and a second vehicle. The local jamming information having an attack time, an attack localization, and an attack frequency. The method further includes building a global jamming map comprising global jamming information, based on the local jamming information, determining, based on the global jamming map, a location of a communication jamming device, and causing to transmit global jamming information to a third vehicle. The global jamming information is associated with the location of the communication jamming device.

Collaborative signal jamming detection

Techniques for using a detector coupled to a controller area network (CAN) bus of a vehicle system to identify and generate alerts in response to wake-up attacks on the vehicle system. Techniques include an electronic control unit (ECU) sending a wake-up message across the CAN bus that is detected by the detector. The detector includes memory and a processor to identify the timestamp of the wake-up message when the vehicle ignition is off. The detector determines a total operational time for the ECU over an observation time period and generates a notification of a wake-up anomaly when the total operational time over the observation time period exceeds a predetermined threshold.

Detecting abnormal CAN bus wake-up pattern

Systems, methods, and devices for authenticating vehicle-to-vehicle communication are disclosed. A method includes receiving sensor data from a first vehicle and receiving secondary sensor data from a second vehicle. The method includes extracting, based on the sensor data and the secondary sensor data, an authentication comprising one or more of: a proximity of the second vehicle to the first vehicle or a common object identified by the sensor data and the secondary sensor data. The method includes determining whether the authentication satisfies a trust threshold of the first vehicle.

Authentication of vehicle-to-vehicle communications

A computer includes a processor and a memory, the memory storing instructions executable by the processor to initiate a calibration based on a portable device stability and a steering wheel angle, detect a vehicle pedal actuation, and, then, based on a portable device acceleration, determine a calibration vector between a portable device coordinate system and a vehicle coordinate system.

Portable device data calibration

A vehicle includes a wireless transceiver, configured to communicate with a mobile device; a vehicle sensor, configured provide vehicle motion data; and one or more processors, configured to responsive to receiving an authentication request from the mobile device, send a request for a device motion fingerprint for a first timeframe to the mobile device, receive the device motion fingerprint from the mobile device, generate a vehicle motion fingerprint for the first timeframe using the vehicle motion data, responsive to a driving maneuver input intensity being greater than an input threshold, compare the vehicle motion fingerprint with the device motion fingerprint to generate a similarity score, and responsive to the similarity score being indicative of at least a predefined threshold level of similarity between the vehicle motion fingerprint and the device motion fingerprint, authenticate a wireless connection between the mobile device and the vehicle via the wireless transceiver.

Sensor-based pairing between device and vehicle

A system includes, in one aspect, one or more processing devices that perform operations comprising: detecting one or more human objects in images captured by a visual image recording device; obtaining a motion timeseries for each of the detected one or more human objects using the captured images; obtaining a received signal strength (RSS) timeseries for each of the one or more mobile devices, based on received RF signals from the one or more mobile devices; and generating an association between (i) identifying data for a first mobile device of the one or more mobile devices, and (ii) identifying data for one of the one or more human objects representing a first human, wherein the first mobile device has an RSS timeseries that fluctuates at a time period corresponding to movement in the obtained motion timeseries for the one of the one or more human objects representing the first human.

Associating a user identity with a mobile device identity

Authentication of vehicles in preparation for V2V communication includes vehicle A taking a picture of vehicle B (V) and vehicle B taking a picture of vehicle A (V). These pictures are then shared. Vehicle A determines the relative location of vehicle B indicated in Vand V. If they agree, then vehicle A and B authenticate one another, such as using Diffie-Hellman key exchange. Identifying vehicle A and vehicle B in the pictures may include identifying the license plates of vehicle A and vehicle B in the pictures. Vehicles A and B may exchange license plate numbers prior to taking of the pictures. Image spoofing may be prevented by taking both forward and rearward facing pictures by both vehicles. Objects in pictures facing the same direction may be identified to verify that the pictures were taken nearly simultaneously.

Growing community of inventors

San Jose, CA, United States of America

Yu Seung Kim