CHEN Ding1, NI Jin-ping1, BAI Lang2, CHEN Da-chuan3
（1. Shaanxi Province Key Laboratory of Photoelectric Measurement and Instrument Technology, Xi’an Technological University, Xi’an 710021, China； 2. School of Optoelectronic Engineering, Xi’an Technological University, Xi’an 710021, China； 3. Faculty of Electronic Engineering, Aviation University of Air Force, Changchun 130022, China）
Abstract： This paper presents a method using range deception jamming to evaluate the safety performance of the autonomous vehicle with millimetre wave (MMW) radar. The working principle of this method is described. Combined with a waveform edition software, an experimental platform is developed to generate a deceptive signal that contains false distance information. According to related theories and its principle, the configuration parameters of the experimental setup are calculated and configured. The MMW radar of evaluated vehicle should identify an objective when it receives the deceptive signal from the experimental setup. Even if no obstacle, the evaluated vehicle can immediately brake in order that its braking distance is measured. The experimental results show that the proposed method can meet the requirements of the safety performance evaluation for the autonomous vehicle with MMW radar, and it also overcomes some deficiencies of previous methods.
Key words： vehicle safety evaluation； braking distance measurement； semi-physical simulation； range deception jamming； spurious echo generation
CLD number： TN957.52 doi： 10.3969/j.issn.1674-8042.2020.01.001
References
［1］Kim B, Yi K. Probabilistic and holistic prediction of vehicle states using sensor fusion for application to integrated vehicle safety systems. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(5): 2178-2190.
［2］ Aliyu A, Kolo J G, Mikail O O, et al. An ultrasonic sensor distance induced automatic braking automobile collision avoidance system. In: Proeedings of IEEE 3rd International Conference on Electro-Technology for National Development, 2017: 570-576.
［3］Odat E, Shamma J S, Claudel C. Vehicle classification and speed estimation using combined passive infrared/ultrasonic sensors. IEEE Transactions on Intelligent Transportation Systems, 2017, 99: 1-14.
［4］Chen S, Duan H, Deng Y, et al. Drogue pose estimation for unmanned aerial vehicle autonomous aerial refueling system based on infrared vision sensor. Optical Engineering, 2017, 56(12): 1-7.
［5］Hata A Y, Wolf D F. Feature detection for vehicle localization in urban environments using a multilayer lidar. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(2): 420-429.
［6］Fortin B, Lherbier R, Noyer J C. A model-based joint detection and tracking approach for multi-vehicle tracking with lidar sensor. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(4): 1883-1895.
［7］Futatsumori S, Kohmura A, Yonemoto N, et al. Small transmitting power and high sensitivity 76 GHz millimeter-wave radar for obstacle detection and collision avoidance of civil helicopters. In: Proceedings of IEEE National Radar Conference, 2016: 14-16.
［8］Wang X, Xu L, Sun H, et al. On-road vehicle detection and tracking using MMW radar and monovision fusion. IEEE Transactions on Intelligent Transportation Systems, 2016, 17(7): 2075-2084.
［9］Liu D X, Gaucher B, Pfeiffer U, et al. Advanced millimeter-wave technologies: antennas, packaging and circuits. New York: John Wiley & Sons, 2009.
［10］Bruder J A, Brinkmann M C, Whitley G R, et al. Testing of MMW radar performance in adverse weather conditions and clutter backgrounds. In: Proceedings of IET Radar Conference, 2002: 547-551.
［11］Lie A. Managing traffic safety: an approach to the evaluation of new vehicle safety systems. Medical Physics, 2012, 27(4): 662-667.
［12］Lee T, Yi K, Lee C, et al. Impact assessment of enhanced longitudinal safety by advanced cruise control system. Lancet, 2013, 259: 1059.
［13］Lee S, Yoon Y J, Kang S, et al. Enhanced performance of music algorithm using spatial interpolation in automotive FMCW radar systems. IEICE Transactions on Communication, 2017, 101: 163-175.
［14］Chen S, Chen F. Simulation-based assessment of vehicle safety behavior under hazardous driving conditions. Journal of Transportation Engineering, 2010, 136(4): 304-315.
［15］Qu J, Cui Y, Zhu W. Algorithm and its implementation of vehicle safety distance control based on the numerical simulation. Journal of Networks, 2014, 9(12): 3486-3493
［16］Chen D, Ni J P, Bai L, et al. Evaluation method for the performance of light screen array measurement system based on semi-physical simulation’, Optik, 2019, 178: 884-891.
［17］Skolnik M I, Merrill I S. Introduction to radar systems. 3rd ed. New York: McGowan-Hill, 2001.
［18］Nakagawa K, Mitsumoto M, Kai K. Development of millimeter-wave radar for latest vehicle systems. Berlin Heidelberg: Springer, 2015.
［19］Merrill I S. Radar Handbook.3rd ed. New York: McGowan-Hill, 2008.
［20］Zhao G Q. Principle of radar countermeasures. 2nd ed. Xidian Press, Xi’an, China, 2015.
［21］Zhao S J, Zhao J X. Signal detection and estimation theory. Beijing: Publishing House of Electronics Industry, 2013.
［22］National Standards in the People’s Republic of China, GB7258-2017. Technical specifications for safety of power-driven vehicles operating on roads. Beijing: Standards Press of China, 2017.
基于毫米波雷达的无人驾驶汽车安全性能评估
陈 丁1， 倪晋平1， 白 浪2， 陈大川3
（1. 西安工业大学 陕西省光电测试与仪器技术重点实验室， 陕西 西安 710021； 2. 西安工业大学 光电工程学院， 陕西 西安 710021； 3. 空军航空大学 电子工程系， 吉林 长春 130022）
摘 要: 为了评估基于毫米波的无人驾驶汽车安全性能， 提出一种采用距离欺骗干扰的评估方法。 首先， 详细介绍了该方法的工作原理， 结合一种波形编辑软件， 研制出了一种试验平台可产生含有虚假距离信息的欺骗干扰信号。 然后， 根据毫米波雷达工作原理及雷达干扰相关理论， 科学地计算并配置了试验装置的相关参数。 在实验中， 当收到来自试验装置的欺骗信号后， 被测车辆的毫米波雷达可以将其识别成一个障碍物。 虽然障碍物并不存在， 但被测车辆依然即使采取了紧急制动措施， 以供测量其有效制动距离。 实验结果表明， 该方法可以满足基于毫米波的无人驾驶汽车安全性能测试， 且避免了之前方法的种种弊端。
关键词： 汽车安全性评估； 制动距离测量； 半实物仿真； 距离欺骗干扰； 虚假回波生成
引用格式： CHEN Ding, NI Jin-ping, BAI Lang, et al. Evaluation on safety performance of a millimetre wave radar-based autonomous vehicle. Journal of Measurement Science and Instrumentation, 2020, 11（1）： 1-10. ［doi： 10.3969／j.issn.1674-8042.2020.01.001］
[full text view]
