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Minimizing Data Loss by Encrypting Brake-Light Images and Avoiding Rear-End Collisions Using Artificial Neural Network
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Author(s): Abirami M. S. (SRM Institute of Science and Technology, India)and Manoj Kushwaha (SRM Institute of Science and Technology, India)
Copyright: 2024
Pages: 18
Source title:
Innovative Machine Learning Applications for Cryptography
Source Author(s)/Editor(s): J. Anitha Ruth (SRM Institute of Science and Technology, Vadapalani, India), G.V. Mahesh Vijayalakshmi (BMS Institute of Technology and Management, India), P. Visalakshi (Department of Networking and Communications, College of Engineering and Technology, SRM Institute of Science and Technology, Katankulathur, India), R. Uma (Sri Sairam Engineering College, Chennai, India)and A. Meenakshi (SRM Institute of Science and Technology, Vadapalani, India)
DOI: 10.4018/979-8-3693-1642-9.ch008
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Abstract
Rear-end collisions are a threat to road safety, so reliable collision avoidance technologies are essential. Traditional systems present several issues due to data loss and privacy concerns. The authors introduce an encrypted artificial neural network (ANN) method to prevent front-vehicle rear-end collisions. This system uses encryption techniques and ANN algorithm to recognize the front vehicle brake light in real time. Information can't be deciphered without the appropriate key using encryption. Intercepting data during transmission prevents reading. The system works day and night. ANN outperforms LR, SVM, DT, RF, and KNN in accuracy. An encrypted ANN-based ML model distinguishes between brake and normal signals. ANN accuracy was 93.7%. Driver receives further alerts to avoid rear-end collisions. This work proposes a lightweight, secure ANN-based brake light picture encryption method. The proposed approach may be applied to other collision circumstances, including side and frontal strikes. The technique would be more adaptable and applicable to many road safety circumstances.
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