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, Speed planning, QP algorithm, Trapezoidal planning curve

CLC Number: 

  • TP242
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