PDF(Pubmed)
Abstract：
Efficient transportation systems are essential for the development of smart cities. Autonomous vehicles and Intelligent Transportation Systems (ITS) are crucial components of such systems, contributing to safe, reliable, and sustainable transportation. They can reduce traffic congestion, improve traffic flow, and enhance road safety, thereby making urban transportation more efficient and environmentally friendly. We present an innovative combination of photonic radar technology and Support Vector Machine classification, aimed at improving multi-target detection in complex traffic scenarios. Central to our approach is the Frequency-Modulated Continuous-Wave photonic radar, augmented with spatial multiplexing, enabling the identification of multiple targets in various environmental conditions, including challenging weather. Notably, our system achieves an impressive range resolution of 7 cm, even under adverse weather conditions, utilizing an operating bandwidth of 4 GHz. This feature is particularly crucial for precise detection and classification in dynamic traffic environments. The radar system\'s low power requirement and compact design enhance its suitability for deployment in autonomous vehicles. Through comprehensive numerical simulations, our system demonstrated its capability to accurately detect targets at varying distances and movement states, achieving classification accuracies of 75% for stationary and 33% for moving targets. This research substantially contributes to ITS by offering a sophisticated solution for obstacle detection and classification, thereby improving the safety and efficiency of autonomous vehicles navigating through urban environments.
摘要：
高效的交通系统对于智慧城市的发展至关重要。自动驾驶汽车和智能交通系统（ITS）是此类系统的关键组成部分，有助于安全，可靠,可持续交通。它们可以减少交通拥堵，改善交通流量，加强道路安全，从而使城市交通更加高效和环保。我们提出了光子雷达技术和支持向量机分类的创新组合，旨在改善复杂交通场景下的多目标检测。我们方法的核心是调频连续波光子雷达，用空间复用增强，能够在各种环境条件下识别多个目标，包括具有挑战性的天气。值得注意的是,我们的系统实现了7厘米的令人印象深刻的范围分辨率，即使在恶劣的天气条件下，利用4GHz的工作带宽。此功能对于动态交通环境中的精确检测和分类尤为重要。雷达系统的低功耗要求和紧凑的设计增强了其在自动驾驶汽车中的部署适用性。通过全面的数值模拟,我们的系统展示了它在不同距离和运动状态下准确检测目标的能力，固定目标的分类精度为75%，移动目标的分类精度为33%。这项研究通过为障碍物检测和分类提供复杂的解决方案，大大有助于ITS，从而提高自主车辆在城市环境中导航的安全性和效率。
