Chaudhary Sushank, Sharma Abhishek, Khichar Sunita, Meng Yahui, Malhotra Jyoteesh
School of Computer, Guangdong University of Petrochemical Technology, Maoming, 525000, China.
Department of Electronics Technology, Guru Nanak Dev University, Amritsar, India.
Sci Rep. 2024 Jul 28;14(1):17339. doi: 10.1038/s41598-024-66850-z.
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.
高效的交通系统对于智慧城市的发展至关重要。自动驾驶车辆和智能交通系统（ITS）是此类系统的关键组成部分，有助于实现安全、可靠和可持续的交通。它们可以减少交通拥堵、改善交通流量并提高道路安全性，从而使城市交通更加高效且环保。我们提出了一种将光子雷达技术与支持向量机分类相结合的创新方法，旨在改善复杂交通场景中的多目标检测。我们方法的核心是调频连续波光子雷达，并辅以空间复用技术，能够在包括恶劣天气在内的各种环境条件下识别多个目标。值得注意的是，即使在恶劣天气条件下，我们的系统利用4GHz的工作带宽也能实现令人印象深刻的7厘米距离分辨率。这一特性对于动态交通环境中的精确检测和分类尤为关键。雷达系统的低功耗要求和紧凑设计增强了其在自动驾驶车辆中部署的适用性。通过全面的数值模拟，我们的系统展示了其在不同距离和运动状态下准确检测目标的能力，静止目标的分类准确率达到75%，移动目标的分类准确率达到33%。这项研究通过提供一种用于障碍物检测和分类的复杂解决方案，为智能交通系统做出了重大贡献，从而提高了自动驾驶车辆在城市环境中行驶的安全性和效率。
