This paper provides a comprehensive survey of advancements made over the past decade in the field of vision-based target tracking for autonomous vehicle navigation. The introduction begins by highlighting the motivations and wide-ranging applications of vision-based target tracking in autonomous vehicle navigation. These applications span various domains, underscoring the necessity for the development of robust algorithms capable of handling the diverse challenges faced by autonomous vehicles in dynamic environments. The discussion establishes that creating resilient vision-based tracking solutions is crucial for the efficient operation of autonomous systems. The review is organized into three primary categories: land, underwater, and aerial vehicles. Each category explores the specific techniques and methodologies developed for target tracking within its respective domain. For land-based autonomous vehicles, the focus is on approaches that manage obstacles, uneven terrains, and dynamic road conditions. In the context of underwater vehicles, challenges such as poor visibility, varying water conditions, and the need for energy-efficient operations are examined. For aerial vehicles, the discussion highlights the importance of precise tracking in three-dimensional space, which is critical for applications like surveillance, delivery, and disaster response. Additionally, the paper delves into the growing trend of integrating data fusion techniques to enhance the performance and robustness of vision-based target tracking systems. By combining data from multiple sensors and modalities, data fusion helps address limitations like occlusion, noise, and environmental variability, thereby improving tracking accuracy and reliability. Finally, the paper identifies several research challenges that remain unresolved, including issues like computational efficiency, real-time processing, and adapting to highly dynamic environments. It also explores potential future research directions, such as leveraging advancements in artificial intelligence, deep learning, and multi-modal data integration to further enhance the capabilities of vision-based target tracking systems for autonomous navigation.