Using Model Predictive Control for Collision Avoidance During Lane Change Maneuvers in Autonomous Vehicles
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| Publicado en: | International Journal of Software Science and Computational Intelligence vol. 17, no. 1 (2025), p. 1-22 |
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| Otros Autores: | , , , |
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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MARC
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| 003 | UK-CbPIL | ||
| 022 | |a 1942-9045 | ||
| 022 | |a 1942-9037 | ||
| 024 | 7 | |a 10.4018/IJSSCI.391244 |2 doi | |
| 035 | |a 3262245607 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Sarma, Kandarpa Kumar |u Gauhati University, India | |
| 245 | 1 | |a Using Model Predictive Control for Collision Avoidance During Lane Change Maneuvers in Autonomous Vehicles | |
| 260 | |b IGI Global |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Simulation | ||
| 653 | |a Software | ||
| 653 | |a Proportional integral derivative | ||
| 653 | |a Control algorithms | ||
| 653 | |a Trajectory optimization | ||
| 653 | |a Lane changing | ||
| 653 | |a Traffic | ||
| 653 | |a Optimization | ||
| 653 | |a Decision making | ||
| 653 | |a Autonomous vehicles | ||
| 653 | |a Process controls | ||
| 653 | |a Effectiveness | ||
| 653 | |a Game theory | ||
| 653 | |a Predictive control | ||
| 653 | |a Collision avoidance | ||
| 653 | |a Real time | ||
| 653 | |a Trajectory planning | ||
| 653 | |a Business metrics | ||
| 700 | 1 | |a Deka, Surajit |u Gauhati University, India | |
| 700 | 1 | |a Misra, Aradhana |u Gauhati University, India | |
| 700 | 1 | |a Tukaria, Ridip |u Gauhati University, India | |
| 700 | 1 | |a Dutta, Ananya |u Gauhati University, India | |
| 773 | 0 | |t International Journal of Software Science and Computational Intelligence |g vol. 17, no. 1 (2025), p. 1-22 | |
| 786 | 0 | |d ProQuest |t Computer Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3262245607/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3262245607/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |