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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Cybersecurity

Background:

  • Fully autonomous vehicles (FAVs) require in-cabin monitoring systems (IMS) for safety.
  • Existing systems raise privacy concerns due to facial data exposure.
  • Balancing surveillance needs with privacy is crucial for public acceptance.

Purpose of the Study:

  • To develop an intelligent IMS that protects personal privacy.
  • To enable person re-identification in abnormal situations within FAVs.
  • To address the conflicting demands of surveillance and privacy.

Main Methods:

  • Proposed a privacy-preserved IMS for FAVs.
  • Implemented onboard facial feature extraction and anonymization.
  • Utilized reserved feature vectors for re-identification from anonymized data.
  • Simulated abnormal scenarios within a vehicle cabin.

Main Results:

  • Successfully re-identified individuals using anonymized virtual faces and feature vectors.
  • Demonstrated effective privacy preservation during in-cabin monitoring.
  • Validated the system's capability to handle abnormal situations.

Conclusions:

  • The proposed IMS effectively balances privacy protection and security.
  • Facial data anonymization at the edge enhances user privacy.
  • This approach supports secure and private surveillance in autonomous vehicles.