Computer Science ›› 2019, Vol. 46 ›› Issue (10): 273-278.doi: 10.11896/jsjkx.190400147

• Artificial Intelligence • Previous Articles     Next Articles

Research on Intelligent Vehicle Speed Planning Algorithms Based on Trapezoidal Planning Curve

CAO Bo1, LI Yong-le2, ZHU Ying-jie1, JIA Bin2, XU You-chun2   

  1. (Student Brigade 5,Army Military Transportation University,Tianjin 300161,China)1
    (Military Transportation Research Institute,Army Military Transportation University,Tianjin 300161,China)2
  • Received:2019-04-26 Revised:2019-07-07 Online:2019-10-15 Published:2019-10-21

Abstract: Aiming at the problem of short deceleration distance and poor stationarity caused by late deceleration in par-king process when QP (quadratic programming) algorithm is applied to speed planning of intelligent vehicles,there are some problems such as short deceleration distance and poor stationarity caused by late deceleration in the stopping process.This paper presented an intelligent vehicle speed planning algorithm based on the trapezoidal programming curve.Firstly,the QP model of speed planning is established and solved.Then,the stopping process based on trapezoidal programming curve at different initial speeds is analyzed,andits results are considered as nonlinear constraint to instantiate and solved QP model .Finally,the experimental results of QP algorithm and the algorithm were compared and analyzed through simulation experiment and real car experiment.In the simulation experiment,the initial speed of 39.8 km/h,31.5 km/h and 20.6 km/h was used to enter the parking process respectively.The speed curve shows that the proposed algorithm can advance the deceleration time,which preliminarily shows that the algorithm has the optimization effect.In the real vehicle experiment,compared with QP algorithm,the proposed algorithm advances the parking process of the three initial speed by 5.9 s,5.0 s and 3.7 s,the absolute value of the average acceleration decreases by 0.5 m/s2,0.5 m/s2and 0.4 m/s2,the absolute value of the maximum acceleration decreases respectively by 0.16 m/s2,0.33 m/s2 and 0.35 m/s2.The simulation and real vehicle experiments show that the improved method has obvious improvement effect and significant optimization effect.

Key words: Intelligent vehicle, QP algorithm, Speed planning, Trapezoidal planning curve

CLC Number: 

  • TP242
[1]DAVID J G,CHARLES B N,MAZEN F.A hierarchical approach for primitive-based motion planning and control of autonomous vehicles[J].Robotics and Autonomous Systems,2014,62(2):214-228.
[2]INGRID J,JIN J C,MA X L,et al.Look-ahead speed planning for heavy-duty vehicle platoons using traffic information[J].Transportation Research Procedia,2017,22(3):561-569.
[3]JORGE V,VICENTE M,JOSHUE P,et al.Path and speed planning for an automated public transport vehicle[J].Robotics and Autonomous Systems,2012,60(2):252-265.
[4]JIANG Y,GONG J W,XIONG G M,et al.Research on Differential Constraints-based Planning Algorithm for Autonomous-driving Vehicles[J].Acta Automatic Sinica,2013,39(12):2012-2020.(in Chinese)
姜岩,龚建伟,熊光明,等.基于运动微分约束的无人车辆纵横向协同规划算法的研究[J].自动化学报,2013,39(12):2012-2020.
[5]REZA T,NICHOLAS M,STEPHEN B,et al.A simple effective heuristic for embedded mixed-integer quadratic programming[J/OL].International Journal of Control,[2017-4-24][2019-3-10].http://dx.doi.org/10.1080/00207179.2017.1316016.
[6]ZHAO M,LI S Y.Nonlinear model predictive control optimization algorithm based on the trust-region quadratic programming [J].Control Theory & Applications,2009,26(6):634-640.(in Chinese)
赵敏,李少远.基于信赖域二次规划的非线性模型预测控制优化方法[J].控制理论与应用,2009,26(6):634-640.
[7]BRAND M,SHILPIEKANDULA V,YAO C,et al.A parallel quadratic programming algorithm for model predictive control[C]//18th World Congress of the International Federation of Automatic Control.Milano:IEEE Press,2011:1031-1039.
[8]ZENG X R,WANG J M.Globally energy-optimal speed plan-ning for road vehicles on a given route[J].Transportation Research Part C,2018,93(8):148-160.
[9]STEFAN F.Campbell Steering Control of an Autonomous Ground Vehicle with Application to the Data Urban Challenge[D].Massachusetts:Massachusetts Institute of Technology,2005.
[10]LIU R,KOCH A,ZELL A.Path following with passive UHF RFID received signal strength in unknown environments[C]//IEEE/RSJ International Conference on Intelligent Robots and Systems.Washington D.C:IEEE Press,2013:2250-2255.
[11]JIANG Y,ZHAO X J,GONG J W,et al.System Design of Self-driving in Simplified Urban Environments [J].Journal of Mechanical Engineering,2012,48(20):103-112.(in Chinese)
姜岩,赵熙俊,龚建伟,等.简单城市环境下地面无人驾驶系统的设计研究[J].机械工程学报,2012,48(20):103-112.
[12]PIAZZI A,BIANCO C G L,BERTOZZI M,et al.Quintic G2-splines for the iterative steering of vision-based autonomous vehicles[J].IEEE Transactions on Intelligent Transportation Systems,2002,3(1):27-36.
[13]XIAO Y L.Development of Speed Advisory for Commercial Vehicles Based on Environmental Conditions[C]//15th ITS World Congress.New York:IEEE Press,2008:50-51.
[14]ZHAO S E,QU X,ZHANG J L.Prediction of Safe Vehicle Speed on Curved Roads Based on Driver-Vehicle-Road Collaboration[J].Automotive Engineering,2015,37(10):1208-1220.(in Chinese)
赵树恩,曲贤,张金龙.基于人车路协同的车辆弯道安全车速预测[J].汽车工程,2015,37(10):1208-1220.
[15]HANS J F,CHRISTIAN K,ANDREAS P,et al.qpOASES:a parametric active-set algorithm for quadratic programming[J/OL].Mathematical Programming Computation,[2014-4-30][2019-3-15].http://dx.doi.org/10.1007/s12532-014-0071-1.
[16]ZHU Y,CHEN H,MU H H.A novel approach of tuning trapezoidal velocity profile for energy saving in servomotor[C]//2015 34th Chinese Control Conference (CCC).Hangzhou:IEEE Press,2015:4412-4417.
[17]XIA T.The Research of Velocity Planning And Path Tracking control Method For Intelligent Vehicle[D].Beijing:Beijing University of Technology,2017.(in Chinese)
夏天.智能车速度规划及路径跟踪控制方法研究[D] 北京:北京工业大学,2017.
[1] GU Ming-qin,CAI Zi-xing and YI Liang. Auto Exposure Algorithm for Perception System of Intelligent Vehicle [J]. Computer Science, 2013, 40(6): 300-302.
[2] . Discussion on the Intelligent Vehicle Technologies [J]. Computer Science, 2012, 39(5): 1-8.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!