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University of Michigan-Dearborn, USA
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Accelerated Secure Boot for Real-Time Embedded Safety Systems

SAE International Journal of Transportation Cybersecurity and Privacy

University of Michigan-Dearborn, USA-Ahmad M.K. Nasser, Di Ma
Rhein-Waal University of Applied Sciences-Kleve, Germany-Wonder Gumise
  • Journal Article
  • 11-02-01-0003
Published 2019-07-08 by SAE International in United States
Secure boot is a fundamental security primitive for establishing trust in computer systems. For real-time safety applications, the time taken to perform the boot measurement conflicts with the need for near instant availability. To speed up the boot measurement while establishing an acceptable degree of trust, we propose a dual-phase secure boot algorithm that balances the strong requirement for data tamper detection with the strong requirement for real-time availability. A probabilistic boot measurement is executed in the first phase to allow the system to be quickly booted. This is followed by a full boot measurement to verify the first-phase results and generate the new sampled space for the next boot cycle. The dual-phase approach allows the system to be operational within a fraction of the time needed for a full boot measurement while producing a high detection probability of data tampering. We propose two efficient schemes of the dual-phase approach along with calibratable parameters to achieve the desired tamper detection probability. We evaluate the tampering detection accuracy within a simulation environment. Then we build a…
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Detection of Lane-Changing Behavior Using Collaborative Representation Classifier-Based Sensor Fusion

SAE International Journal of Transportation Safety

University of Michigan-Dearborn, USA-Jun Gao, Yi Lu Murphey
Wuhan University of Technology, China-Honghui Zhu
  • Journal Article
  • 09-06-02-0010
Published 2018-10-29 by SAE International in United States
Sideswipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. In this article, a fusion approach is introduced that utilizes data from two differing modality sensors (a front-view camera and an onboard diagnostics (OBD) sensor) for the purpose of detecting driver’s behavior of lane changing. For lane change detection, both feature-level fusion and decision-level fusion are examined by using a collaborative representation classifier (CRC). Computationally efficient detection features are extracted from distances to the detected lane boundaries and vehicle dynamics signals. In the feature-level fusion, features generated from two differing modality sensors are merged before classification, while in the decision-level fusion, the Dempster-Shafer (D-S) theory is used to combine the classification outcomes from two classifiers, each corresponding to one sensor. The results indicated that the feature-level fusion outperformed the decision-level fusion, and the introduced fusion approach using a CRC performs significantly better in terms of detection accuracy, in comparison to other state-of-the-art classifiers.
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