Huang Kaibo, Ding Juan, Deng Weiwen
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China.
PanoSim Technology Limited Company, Jiaxing 314000, China.
Sensors (Basel). 2024 May 22;24(11):3310. doi: 10.3390/s24113310.
Autonomous driving technology is considered the trend of future transportation. Millimeter-wave radar, with its ability for long-distance detection and all-weather operation, is a key sensor for autonomous driving. The development of various technologies in autonomous driving relies on extensive simulation testing, wherein simulating the output of real radar through radar models plays a crucial role. Currently, there are numerous distinctive radar modeling methods. To facilitate the better application and development of radar modeling methods, this study first analyzes the mechanism of radar detection and the interference factors it faces, to clarify the content of modeling and the key factors influencing modeling quality. Then, based on the actual application requirements, key indicators for measuring radar model performance are proposed. Furthermore, a comprehensive introduction is provided to various radar modeling techniques, along with the principles and relevant research progress. The advantages and disadvantages of these modeling methods are evaluated to determine their characteristics. Lastly, considering the development trends of autonomous driving technology, the future direction of radar modeling techniques is analyzed. Through the above content, this paper provides useful references and assistance for the development and application of radar modeling methods.
自动驾驶技术被认为是未来交通运输的发展趋势。毫米波雷达凭借其远距离探测能力和全天候运行能力，成为自动驾驶的关键传感器。自动驾驶中各种技术的发展依赖于广泛的仿真测试，其中通过雷达模型模拟真实雷达的输出起着至关重要的作用。目前，有许多独特的雷达建模方法。为了促进雷达建模方法的更好应用和发展，本研究首先分析雷达探测机制及其面临的干扰因素，以明确建模内容和影响建模质量的关键因素。然后，根据实际应用需求，提出了衡量雷达模型性能的关键指标。此外，还全面介绍了各种雷达建模技术及其原理和相关研究进展。对这些建模方法的优缺点进行了评估，以确定它们的特点。最后，结合自动驾驶技术的发展趋势，分析了雷达建模技术的未来发展方向。通过上述内容，本文为雷达建模方法的发展和应用提供了有益的参考和帮助。
