车辆姿态感知与运动状态预测技术
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摘要
目前大多数已投入使用的汽车主动安全预警系统价格昂贵,而且停留在对汽车运动姿态的监测和报警功能上,缺乏对汽车未来时刻自身运动姿态作进一步预测。针对这些不足性,本文进行汽车运动姿态在线预测的研究,提前预测汽车未来时刻可能的行驶运行状态,对提高汽车的主动安全性,减少道路交通事故将起到十分重要的作用。采用最新的6轴运动处理组件MPU-6050实现汽车运动姿态参数的在线感知,设计卡尔曼滤波器,通过信号融合处理,获取汽车运动姿态参数的最优估计值,采用多层递阶模型来开展汽车运动姿态参数的预测。最后以纵向车速、侧倾角和横摆角为例开展感知与预测,进行实车道路试验,取得了很好的预测效果。实验结果表明所开展的汽车感知与预测技术的可行性和探索性。
Currently most automotive active safety warning system which has been put into use is expensive,but stays in the vehicle motion posture monitoring and alarm functions,lacking of further prediction of the vehicle’s own motion attitude for future time.Considering these shortcomings,launching vehicle motion prediction and predicting vehicle motion posture for future time in advance,can play a very important role to improve the vehicle’s active safety and reduce road traffic accidents.The latest 6-axis Motion Processing Unit(MPU) MPU-6050 can realize vehicle posture parameters online perception.The Kalman filter is designed,through signal fusion processing we can get the optimal estimate of the vehicle motion posture parameters,using multi-level recursive model to carry out the prediction research.Finally,taking vertical speed,roll angle and yaw angle for example,we carry on perception and prediction.A good prediction effect is got by road test,which shows the feasibility and exploratory of the vehicle perception and prediction technology.
Currently most automotive active safety warning system which has been put into use is expensive,but stays in the vehicle motion posture monitoring and alarm functions,lacking of further prediction of the vehicle’s own motion attitude for future time.Considering these shortcomings,launching vehicle motion prediction and predicting vehicle motion posture for future time in advance,can play a very important role to improve the vehicle’s active safety and reduce road traffic accidents.The latest 6-axis Motion Processing Unit(MPU) MPU-6050 can realize vehicle posture parameters online perception.The Kalman filter is designed,through signal fusion processing we can get the optimal estimate of the vehicle motion posture parameters,using multi-level recursive model to carry out the prediction research.Finally,taking vertical speed,roll angle and yaw angle for example,we carry on perception and prediction.A good prediction effect is got by road test,which shows the feasibility and exploratory of the vehicle perception and prediction technology.
引文
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[13]项凤涛,王正志,吴第旻,等.捷联系统四元数姿态解算的精细积分法[J].四川兵工学报,2010,31(5):103-106.
[14]刘军,董晶晶,时枭鹏,等.汽车运动状态在线测量及预报技术[J].中国公路学报,2011,24(4):114-121.
[2]林培.新华日报电子版:二十五万人死于车祸的生命警示[DB/OL].http://xh.xhby.net/mp1/html/2008-04/07/content_6756559.htm,2008-04-07.
[3]薄大明,王元海,王军利,等.公安交通安全管理现状分析与对策研究[J].中国人民公安大学学报:自然科学版,2008(1):82-85.
[4]公安部交通管理局.道路交通事故统计年报[Z].无锡:公安部交通管理科学研究所,2009.
[5]NTSB.Special Investigation Report-Highway:Vehicle-andInfrastructure-Based Technology for the Prevention of Rear-End Collisions[EB/OL].http://www.ntsb.gov/safety/safetystudies/SIR0101.htm,2013-04-03.
[6]黄金敢.交通事故动力学分析及再现研究[D].福州:福州大学,2004.
[7]韩峻峰,张惠敏,潘盛辉,等.汽车防撞雷达概述[J].广西工学院学报,2011,22(4):54-58.
[8]永井正夫.基于先进控制技术的车辆主动安全领域研究展望[J].汽车安全与节能学报,2010,1(1):14-22.
[9]Bilimoria K D,Wie B.Time-optimal three-axis reorien-taion of a rigid spacecraft[J].Journal of Guidance,Con-trol and Dynamics,1993,16(3):446-452.
[10]Robinettr P G.Spacecraft euler parameter tracking of large-angle maneuvers via sliding mode control[J].Jouranl ofGuidance,Control and Dyanmics,1996,19(3):702-703.
[11]韩志刚.多层递阶方法理论与应用的进展[J].控制与决策,2001,16(2):129-132,185.
[12]张晓东,韩志刚.一种改进的多层递阶预报方法研究[J].哈尔滨工业大学学报,2002(3):436-439.
[13]项凤涛,王正志,吴第旻,等.捷联系统四元数姿态解算的精细积分法[J].四川兵工学报,2010,31(5):103-106.
[14]刘军,董晶晶,时枭鹏,等.汽车运动状态在线测量及预报技术[J].中国公路学报,2011,24(4):114-121.