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Using Model Predictive Control for Collision Avoidance During Lane Change Maneuvers in Autonomous Vehicles

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Xuất bản năm:International Journal of Software Science and Computational Intelligence vol. 17, no. 1 (2025), p. 1-22
Tác giả chính: Sarma, Kandarpa Kumar
Tác giả khác: Deka, Surajit, Misra, Aradhana, Tukaria, Ridip, Dutta, Ananya
Được phát hành:
IGI Global
Những chủ đề:
Simulation
Software
Proportional integral derivative
Control algorithms
Trajectory optimization
Lane changing
Traffic
Optimization
Decision making
Autonomous vehicles
Process controls
Effectiveness
Game theory
Predictive control
Collision avoidance
Real time
Trajectory planning
Business metrics
Truy cập trực tuyến:Citation/Abstract
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Bài tóm tắt: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.
số ISSN:1942-9045
1942-9037
DOI:10.4018/IJSSCI.391244
Nguồn:Computer Science Database