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International Journal of Zoology and Applied Biosciences Research Article
Next-generation driver assistance and collision prevention system
Thangasubha T, Nirmala B, Lavanya R, Swetha M and Geetha C
Year : 2025 | Pages: 551-553
Received on: 28/09/2025
Revised on: 22/10/2025
Accepted on: 26/11/2025
Published on: 01/12/2025
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Thangasubha T, Nirmala B, Lavanya R, Swetha M and Geetha C( 2025).
Next-generation driver assistance and collision prevention system
. International Journal of Zoology and Applied Biosciences, 10( 6), 551-553.
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Abstract
This study presents a next-generation Driver Assistance and Collision Prevention System integrating multi-sensor fusion, machine learning–based object detection, and real-time risk prediction algorithms. The proposed system employs camera, radar, and LiDAR data to detect obstacles, classify road users, estimate vehicle trajectories, and generate timely alerts and automated braking responses. A deep-learning model (YOLOv8) is used for object detection, while a Kalman filter ensures precise tracking under dynamic driving conditions. Experimental simulation using MATLAB/CarSim demonstrates an increase in collision-avoidance efficiency by 32% compared to conventional ADAS modules. The findings highlight the significance of AI-based multi-sensor fusion in improving road safety and enabling future autonomous mobility.
Keywords
ADAS, Collision Prevention, Multi-Sensor Fusion, LiDAR, YOLOv8, Autonomous Vehicles.
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© The Author(s) 2025. This article is published by International Journal of Zoology and Applied Biosciences under the terms of the Creative Commons Attribution 4.0 International License (creativecommons.org), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
