Title: An AI-driven automotive smart black box for accident and theft prevention
Authors: Muhammad Kashif Shaikh; Sellappan Palaniappan; Touraj Khodadadi
Addresses: Malaysia University of Science and Technology (MUST), Block B, Encorp Strand Garden Office, No. 12, Jalan PJU 5\1 Kota Damansara 47810, Petaling Jaya, Selangor, Malaysia ' School of Science and Engineering, Malaysia University of Science and Technology (MUST), Kuala Lumpur, Malaysia ' School of Science and Engineering, Malaysia University of Science and Technology (MUST), Kuala Lumpur, Malaysia
Abstract: This paper proposes an automotive smart black box (SBB) for accident and theft prevention using artificial intelligence (AI). The SBB is a versatile device and can work with any type of vehicle including electric and conventional cars. It has five smart features including constant facial recognition through AI to identify the driver's face and drowsiness detection. Drowsiness detection will help avoid catastrophic disasters by alarming the drivers if they are about to fall asleep. The SBB also has a 24/7 voice-recording feature that can be used to identify the reasons that caused the accident. Another feature is real-time vehicle tracking using global system for mobile communication (GSM) technology. The SBB would immediately notify the owner if there is any abnormal vehicle movement and help prevent theft. Experimental results prove the efficacy of the designed SBB in recording the relevant information and helping prevent both accidents and thefts of vehicles.
Keywords: face recognition; artificial intelligence; drowsiness detection; GPS tracking; GSM module; smart black box; SBB.
DOI: 10.1504/IJMIC.2021.123800
International Journal of Modelling, Identification and Control, 2021 Vol.39 No.4, pp.332 - 339
Received: 28 Jan 2021
Accepted: 06 Apr 2021
Published online: 04 Jul 2022 *