A Machine Learning- Based Driving Assistance System for Lane and Drowsiness Monitoring
- Title
- A Machine Learning- Based Driving Assistance System for Lane and Drowsiness Monitoring
- Creator
- Ramasamy S.A.G.
- Description
- Lane line detection is a vital component when driving heavy vehicles; this concept follows the path for driving a vehicle to prevent the risk of accidentally entering another lane without the drivers knowledge, which could result in an accident. To detect the lane, use frame masking and Hough line transformation with efficient machine learning algorithms, pre-processed and trained adequately for optimum accuracy as per the provided dataset to spot the white markings on both sides of the lane. Long-distance truck drivers suffer from sleep deprivation, making driving extremely dangerous while tired and they ignore the line markings and wander into the wrong lane. This chapter proposes a portable system that does not require any sensors or interference with the vehicles wiring system; instead, a system that fits on a windshield or any surface to monitor the actions of the driver, using computer vision and feature-extracted datasets within a trained neural network model using cameras. This driver-assisted system can detect drowsiness and give an alarm to wake up the driver by identifying the Region of Interest. These predictions are made based on eye movements, and the algorithm generates a score. The higher the score, the longer the time between alarms. 2024 Taylor & Francis Group, LLC.
- Source
- Machine Intelligence: Computer Vision and Natural Language Processing, pp. 193-208.
- Date
- 2023-01-01
- Publisher
- CRC Press
- Coverage
- Ramasamy S.A.G., Christ University, Bangalore, India
- Rights
- Restricted Access
- Relation
- ISBN: 978-100096031-0; 978-103220199-3
- Format
- Online
- Language
- English
- Type
- Book chapter
Collection
Citation
Ramasamy S.A.G., “A Machine Learning- Based Driving Assistance System for Lane and Drowsiness Monitoring,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/18416.