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@article{167542,
        author = {Hriday Parikh},
        title = {Autonomous Vehicle Overtaking: A Comprehensive Review of Decision-Making, Learning, Control, and Trajectory Planning Approaches},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {3},
        pages = {1710-1720},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=167542},
        abstract = {Autonomous vehicle overtaking presents a complex challenge in the development of self-driving technology, with significant implications for road safety, traffic efficiency, and user acceptance. The global autonomous vehicle market is projected to grow rapidly, with overtaking capabilities being a crucial component of this technology. This has generated significant interest in developing robust and efficient overtaking systems for autonomous vehicles. Autonomous overtaking systems offer several advantages over human-controlled overtaking, including improved safety, enhanced decision-making, and optimized trajectory planning. These systems have the potential to revolutionize traffic flow and reduce accidents caused by human error during overtaking manoeuvres. This paper reviews various approaches to autonomous vehicle overtaking, including rule-based systems, reinforcement learning methods, model predictive control strategies, and trajectory optimization techniques. It also discusses decision-making processes, learning approaches, control strategies, and trajectory planning methods for autonomous overtaking. Challenges, economic implications, and future prospects of autonomous overtaking technology in the context of the broader autonomous vehicle industry are also explored.},
        keywords = {Autonomous vehicles, Overtaking manoeuvres, Reinforcement learning, Model predictive control, Trajectory optimization},
        month = {September},
        }