Exploring the challenges and opportunities of image processing and sensor fusion in autonomous vehicles: A comprehensive review

Deven Nahata, Kareem Othman
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Abstract

Autonomous vehicles are at the forefront of future transportation solutions, but their success hinges on reliable perception. This review paper surveys image processing and sensor fusion techniques vital for ensuring vehicle safety and efficiency. The paper focuses on object detection, recognition, tracking, and scene comprehension via computer vision and machine learning methodologies. In addition, the paper explores challenges within the field, such as robustness in adverse weather conditions, the demand for real-time processing, and the integration of complex sensor data. Furthermore, we examine localization techniques specific to autonomous vehicles. The results show that while substantial progress has been made in each subfield, there are persistent limitations. These include a shortage of comprehensive large-scale testing, the absence of diverse and robust datasets, and occasional inaccuracies in certain studies. These issues impede the seamless deployment of this technology in real-world scenarios. This comprehensive literature review contributes to a deeper understanding of the current state and future directions of image processing and sensor fusion in autonomous vehicles, aiding researchers and practitioners in advancing the development of reliable autonomous driving systems.

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探索自动驾驶汽车中图像处理和传感器融合的挑战和机遇:综合综述
& lt; abstract>自动驾驶汽车是未来交通解决方案的前沿,但它们的成功取决于可靠的感知能力。本文综述了图像处理和传感器融合技术对保证车辆安全和效率的重要性。本文主要关注通过计算机视觉和机器学习方法进行目标检测、识别、跟踪和场景理解。此外,本文还探讨了该领域面临的挑战,例如恶劣天气条件下的鲁棒性、对实时处理的需求以及复杂传感器数据的集成。此外,我们还研究了针对自动驾驶汽车的定位技术。结果表明,虽然在每个子领域都取得了实质性进展,但仍然存在局限性。这些问题包括缺乏全面的大规模测试,缺乏多样化和可靠的数据集,以及某些研究中偶尔出现的不准确。这些问题阻碍了该技术在现实场景中的无缝部署。这篇全面的文献综述有助于更深入地了解自动驾驶汽车图像处理和传感器融合的现状和未来方向,帮助研究人员和从业人员推进可靠的自动驾驶系统的开发。& lt; / abstract>
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来源期刊
AIMS Electronics and Electrical Engineering
AIMS Electronics and Electrical Engineering Engineering-Control and Systems Engineering
CiteScore
2.40
自引率
0.00%
发文量
19
审稿时长
8 weeks
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