计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 196-205.doi: 10.11896/JsJkx.190900066
吴宏涛1, 刘力源1, 孟颖1, 荣亚鹏1, 李路凯2
WU Hong-tao1, LIU Li-yuan1, MENG Ying1, RONG Ya-peng1 and LI Lu-kai2
摘要: 道路上的遗洒物可能对交通运输构成潜在的安全威胁。在自动驾驶环境感知的行业应用背景下,提出一种基于动态多特征融合的道路遗洒物威胁度分析方法,一方面可以实现对道路多车辆目标的跟踪,另一方面可以实现行驶区域内遗洒物对车辆行驶的威胁度自动分析。为提取道路前景车辆目标的交通特性参数,首先开展多车辆跟踪方法研究,提出一种基于Camshift和身份数据关联的多目标跟踪算法,通过建立跟踪链表,对跟踪车辆身份数据进行记录,实时跟踪道路前景车辆目标,提取并记录感兴趣车辆交通特性参数;然后结合交通特性参数提取道路车辆动态特征,在该类目标跟踪基础上建立道路遗洒物安全分析模型,通过分析被跟踪车辆的特征变化,提出一种多特征融合的道路遗洒物威胁度分析方法,突破单一动态特征分析在自动驾驶环境感知应用中的局限性,利用动态多特征的融合决策方法,准确量化判断道路遗洒物对交通运输造成的威胁程度;最后,为了验证算法的鲁棒性和实用性,设计了仿真视频结合实际传感器获取的道路视频对所提威胁度分析方法进行验证,仿真视频用3dmax仿真得到,实采视频由CCD摄像机拍摄得到。相关算法验证采用VS2008和OpenCV搭建软件平台,仿真图由MATLAB2014得到,视频图像的分辨率为320*240。实验结果表明,该方法能准确、真实地确定遗洒物的威胁程度,利用第三方实验视角拓宽了特定车辆安全威胁区域分析的应用范围,通过对自动驾驶主车体行驶范围内的安全威胁环境建模,为自动驾驶车辆安全行驶决策的车载应用提供理论依据和技术支持。
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[1] CHEN H,XU J B.Trends of Intelligent Vehicle Technology.China lntegrated Circult,2014,23(11):64-70. [2] ZHAO E,SONG S,MA Z.Design and initial testing of an integrated switched reluctance starter/generator system for unmanned aerial vehicle.IEEE Transactions on Electrical Machines and Systems.2018,2(4):277-283. [3] REN D,XU B.An Algorithm for Improving the Accuracy and Real-Time of Target Tracking//Technology,Networking,Electronic&Automation Control Conference.Chengdu:IEEE,2017:997-1001. [4] AMROUCHE N,KHENCHAF A,BERKANI D.Multiple target tracking using track before detect algorithm //IEEE 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA).Verona:IEEE,2017:201-210. [5] QIAN X Y,ZHANG D H,ZHANG Y L.Video target depth tracking with multi-feature fusion.Science and Technology and Engineering,2019,3(7):139-147. [6] MA K,ZHANG H,WANG R.Target tracking system for multi-sensor data fusion//2017 IEEE 2nd Information Technology,Networking,Electronic and Automation ControlConfe-rence (ITNEC).Chengdu:IEEE,2017:1768-1772. [7] CHENG Y.Mean Shift,Mode Seeking,and Clustering.IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(8):790-799. [8] COLLINS R T.Mean-shift blob tracking through scale space //2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.Madison:IEEE,2003:232-234. [9] COMANICIU D,RAMESH V,MEER P.Kernel-based ObJect Tracking.IEEE Transaction on Pattern Analysis Machine Intelligence,2003,25(5):564-577. [10] WANG G,WU J,LI X.A real-time tracking prediction for maneuvering target//2018 Chinese Control And Decision Conference (CCDC).Shenyang:IEEE,2018:3562-3566. [11] DAI G J,ZHANG Y.A novel auto-camshift algorithm used in obJect tracking//2008 27th Chinese Control Conference.Kunming:IEEE,2008:369-373. [12] LIU Z S,DONG S H.The Study of Improving Camshift Algorithms for Goal Tracking in Complex Environment//Second International Conference on Information and Computing Science.Manchester:IEEE,2009:27-31. [13] LI H W,CHEN L,BAI J B.ProJection Pursuit Threat Target Evaluation Model Based on CMFO Algorithm.Computer Engineering and Applications,2017(5):81-88. [14] BACˇKALIC′ S,JOVANOVIC′ D,BACˇKALIC′ T.Reliability reallocation models as a support tools in traffic safety analysis.Accident Analysis & Prevention,2014,9(65):47-52. [15] KRMER P,BESHAH T,EJIGU D,et al.Mining traffic accident features by evolutionary fuzzy rules//2013 IEEE Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS).Singapore:IEEE,2013:38-43. [16] YANG H Y,HAN C,ZHANG S W.Aerial target threat assess-ment method based on FDBN.Firepower and Command and Control,2019(1):209-219. [17] CHEN H,HE Z,LIU B.Threat assessment and sensor control for multi-target tracking via PHD filter//2017 IEEE International Conference on Mechatronics and Automation (ICMA).Changsha:IEEE,2017:1000-1007. [18] MA S,YANG G,ZHANG H,et al.Target threat level assessment technology based on cloud model and Bayesian revision in air combat simulation//2016 35th Chinese Control Confe-rence (CCC).Chengdu:IEEE,2016:9753-9757. [19] GAO Y Y,YU M J,WANG Z B L.A new method of multi-target threat assessment for air combat//2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics,Automation and Mechatronics (RAM).Ningbo:IEEE,2017:779-784. [20] ALI M,FALCONE P,SJBERG J.Model-based threat assessment in semi-autonomous vehicles with model parameter uncertainties//2011 50th IEEE Conference on Decision and Control and European Control Conference.Orlando:IEEE,2011:6822-6827. [21] MATTEOLI S,DIANI M,CORSINI G.Automatic Target Re-cognition Within Anomalous Regions of Interest in Hyperspectral Images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2018,11(4):1056-1069. [22] CHO H,CHO Y,SONG W.Image Matting for Automatic Target Recognition.IEEE Transactions on Aerospace and Electronic Systems,2017,53(5):2233-2250. |
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