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Using Model Predictive Control for Collision Avoidance During Lane Change Maneuvers in Autonomous Vehicles
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Author(s): Ananya Dutta (Gauhati University, India), Aradhana Misra (Gauhati University, India), Ridip Tukaria (Gauhati University, India), Surajit Deka (Gauhati University, India)and Kandarpa Kumar Sarma (Gauhati University, India)
Copyright: 2025
Volume: 17
Issue: 1
Pages: 21
Source title:
International Journal of Software Science and Computational Intelligence (IJSSCI)
Editor(s)-in-Chief: Brij Gupta (Asia University, Taichung City, Taiwan)and Andrew W.H. Ip (University of Saskatchewan, Canada)
DOI: 10.4018/IJSSCI.391244
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Abstract
This paper proposes a novel approach for collision avoidance during lane changes using Model Predictive Control (MPC). The proposed method integrates real-time trajectory planning with dynamic vehicle modeling to predict and optimize the vehicle's motion over a finite time horizon. The paper presents the fundamental principles of MPC, its integration with vehicle dynamics, and its application to real-time control. Simulation results demonstrate the effectiveness of MPC in optimizing trajectory planning and ensuring safety under various traffic scenarios. This paper provides a comprehensive comparison of MPC with other control models such as Proportional-Integral-Derivative (PID) control, Rule-Based Control (RBC), and Reinforcement Learning (RL)-based approaches. Simulation results demonstrate the effectiveness of the proposed method in a variety of traffic scenarios, including high-density and mixed-traffic environments. Experimental results highlight the relative performance of these models under simulated environments in MATLAB.
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