Multisensor Information Fusion: Future of Environmental Perception in Intelligent Vehicles

Yongsheng Zhang;Chen Tu;Kun Gao;Liang Wang
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Abstract

As urban transportation increasingly impacts daily life, efficiently utilizing traffic resources and developing public transportation have become crucial for addressing issues such as congestion, frequent accidents, and noise pollution. The rapid advancement of intelligent autonomous driving technologies, particularly environmental perception technologies, offers new directions for solving these problems. This review discusses the application of multisensor information fusion technology in environmental perception for intelligent vehicles, analyzing the components and performance of various sensors and their specific applications in autonomous driving. Through multisensor information fusion, the accuracy of environmental perception is enhanced, optimizing decision support for autonomous driving systems and thereby improving vehicle safety and driving efficiency. This study also discusses the challenges faced by information fusion technology and future development trends, providing references for further research and application in intelligent transportation systems.
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多传感器信息融合:智能汽车环境感知的未来
随着城市交通对日常生活的影响越来越大,有效利用交通资源和发展公共交通已成为解决交通拥堵、事故频发和噪声污染等问题的关键。智能自动驾驶技术,尤其是环境感知技术的快速发展,为解决这些问题提供了新的方向。本综述讨论了多传感器信息融合技术在智能汽车环境感知中的应用,分析了各种传感器的组成和性能及其在自动驾驶中的具体应用。通过多传感器信息融合,可提高环境感知的准确性,优化自动驾驶系统的决策支持,从而提高车辆安全性和驾驶效率。本研究还探讨了信息融合技术面临的挑战和未来发展趋势,为智能交通系统的进一步研究和应用提供了参考。
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Front Cover Contents Advancements and Prospects in Multisensor Fusion for Autonomous Driving Extracting Networkwide Road Segment Location, Direction, and Turning Movement Rules From Global Positioning System Vehicle Trajectory Data for Macrosimulation Decision Making and Control of Autonomous Vehicles Under the Condition of Front Vehicle Sideslip
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