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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2018  >  18(2): 148-158
HUI Fei, MU Ke-nan, ZHAO Xiang-mo. Assistant driving decision method of vehicle lane change based on dynamic probability grid and Bayesian decision network[J]. Journal of Traffic and Transportation Engineering, 2018, 18(2): 148-158. doi: 10.19818/j.cnki.1671-1637.2018.02.016
Citation: HUI Fei, MU Ke-nan, ZHAO Xiang-mo. Assistant driving decision method of vehicle lane change based on dynamic probability grid and Bayesian decision network[J]. Journal of Traffic and Transportation Engineering, 2018, 18(2): 148-158. doi: 10.19818/j.cnki.1671-1637.2018.02.016
shu

Assistant driving decision method of vehicle lane change based on dynamic probability grid and Bayesian decision network

doi: 10.19818/j.cnki.1671-1637.2018.02.016

  • School of Information Engineering, Chang'an University, Xi'an 710064, Shaanxi, China

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  • Author Bio:

    HUI Fei(1982-), male, associate professor, PhD, feihui@chd.edu.cn

    MU Ke-nan(1990-), female, engineer, PhD, kenanmu@chd.edu.cn

  • Received Date: 2017-11-09
  • Publish Date: 2018-04-25
    Abstract
  • An assistant decision method of vehicle lane change was proposed to provide lane changing behavior decision for drivers.A driving environment information perception device consisting of on-board GPS, camera sensors, and radars was constructed.A non-uniform B-spline (NUBS) curve model was applied to modeling lane line.The lane line was detected, tracked and classified by coordinate calculation and searching strategy of control points.The effective driving region was determined based on lane line information.A driving environment geometric model of dynamic probability grid was established and considered as a compact representation of effective driving region.The influence of vehicle on the reliability of driving environment representation result was considered.Vehicle coordination information was mapped into the dynamic probabilitygrid based on Gaussian distribution, and the occupancy probability of each cell unit was calculated.Lane line information and occupied probability of cell unit were used as initial node state parameters and input into Bayesian decision network to estimate the occupancy state of probability grid unit, output the quantified representation result of current driving environment, and compute the expect utility values of three lane changing decisions including "no lane changing", "left lane changing"and "right lane changing".The optimal lane changing decision was determined by computing the ratio of each decision-making expect utility value.Experimental result shows that, in situation 1, the maximum expect utility value is 0.70 of "left lane changing", which is the optimal decision.In the corresponding dynamic probability grid, the"solid"state parameter of right lane line is 100.00%, so the expect utility of "right lane changing"is the lowest.The output optimal decision of decision-making system is "no lane changing", which is consistent with Chinese traffic laws and verifies the necessity of lane line classification.In situation 2, the decision expect utility values of"no lane changing"and "right lane changing"are 0.43 and 0.44, respectively, the ratio is close to 1, so it is unable to judge the optimal decision, and drivers can make the decision whether or not to change lane based on experience.

     

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