动态多特征融合的道路遗洒物威胁度分析方法

doi:10.11896/JsJkx.190900066

摘要/Abstract

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。

关键词: 道路安全决策, 多特征融合, 目标威胁度分析, 遗洒物, 自动驾驶环境感知

Abstract: Scattered obJects in road traffic may cause potential safety threat to transportation.In the context of industry application of auto-driving environment sensing,a novel threat-degree analysis method for abandoned obJect in road traffic based on dynamic multi-feature fusion was proposed in this paper.It realizes multiple vehicles tracking and the automatic analysis of the threat degree of the scattered obJects to vehicles in the driving area.In the proposed method,in order to extract the traffic characteristic parameters of the vehicles in foreground,firstly,the multi-vehicle tracking method was studied,and a novel tracking algorithm based on Camshift and identity data association was proposed.This algorithm records the identity data of the tracked vehicles by establishing the track list,real-time tracking of multi-vehicle targets in foreground can be realized.Then,the dynamic features of the vehicles are extracted based on the traffic characteristic parameters,the scattered obJects safety analysis modeling is established based on target tracking.A threat-degree analysis method of scattered obJects in road traffic was put forward by analyzing the dynamic multi-feature of the tracked vehicles.The proposed method not only overcomes the limitation of the environment sensing using only one dynamic feature,but also can accurately estimate the threat-degree of scattered obJects in road traffic to transportation by the multiple features fusion decision method.Finally,in order to verify the robustness and practicability of the proposed threat-degree analysis method,this paper designed an experiment that using the simulation and the real road video.The simulation video was simulated by 3dmax,and the real video was captured by CCD camera.The proposed algorithm was tes-ted on the software platform constructed by VS2008 and OpenCV,and the simulation figures were obtained by MATLAB2014.The resolution of video image was 320*240.The results show that the proposed method can accurately analyze the threat-degree of scattered obJects in road traffic,and utilizing the third party’s test perspective can broaden the application range of the particular vehicle’s safety area threat-degree analysis.By designing a safety threat-degree analysis model of the auto-vehicle’s surrounding environment,the proposed method can provide theoretical basis and technical support for on-board application of safe driving decision-making of auto-driving vehicles.

Key words: Auto-driving environment sensing, Multiple features fusion, Road safety decision-making, Scattered obJect, Target threat-degree analysis

中图分类号:

U495

吴宏涛, 刘力源, 孟颖, 荣亚鹏, 李路凯. 动态多特征融合的道路遗洒物威胁度分析方法[J]. 计算机科学, 2020, 47(6A): 196-205. https://doi.org/10.11896/JsJkx.190900066

WU Hong-tao, LIU Li-yuan, MENG Ying, RONG Ya-peng and LI Lu-kai. Novel Threat Degree Analysis Method for Scattered ObJects in Road Traffic Based on Dynamic Multi-feature Fusion[J]. Computer Science, 2020, 47(6A): 196-205. https://doi.org/10.11896/JsJkx.190900066

参考文献

[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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