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Multivehicle Cooperative Lane Change Control Strategy for Intelligent Connected Vehicle

机译:智能连接车辆的多光线合作巷改变控制策略

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In order to improve the safety, stability, and efficiency of lane change operating, this paper proposes a multivehicle-coordinated strategy under the vehicle network environment. The feasibility of collaborative lane change operation is established by establishing a gain function based on the incentive model. By comparing lane change gain with lane keeping gain, whether it is feasible to perform the collaboration under current conditions can be judged. Based on the model predictive control (MPC), a multiobjective optimization control function for cooperative lane change is established to realize the distributed control. A novel two-stage cooperative lane change framework is proposed, which divides the lane change process into the lane change phase and the longitudinal headway adjustment phase. It is significant to solve the difficult numerical problem caused by the dimension of collision-avoidance constraints and the nonlinearity of vehicle kinematics. In the first stage, the subject vehicle completes lane change operation. Both longitudinal and lateral movements of the vehicle are considered to optimize the acceleration and the error of following distance at this stage; in the second stage, the operation of adjusting longitudinal headway between vehicles in the target lane is completed, and at this period, only the longitudinal motion of the vehicle is considered to optimize the vehicle acceleration error. The rolling optimization time domain algorithm is used to solve the optimization control problem step by step. Finally, based on the US NGSIM open-source traffic flow database, the accuracy and feasibility of the proposed strategy are verified.
机译:为了提高车道变化运行的安全,稳定性和效率,本文提出了在车辆网络环境下的多光线协调战略。通过基于激励模型建立增益函数来建立协作车道改变操作的可行性。通过将车道变化增益与车道保持增益进行比较,可以判断是否可以在当前条件下执行协作是可行的。基于模型预测控制(MPC),建立了用于协作车道改变的多目标优化控制功能以实现分布式控制。提出了一种新型的两级协同车道改变框架,其将车道改变过程分成车道变化阶段和纵向前进调整阶段。解决了由避免限制的尺寸和车辆运动学的非线性引起的困难数值问题很重要。在第一阶段,主题车辆完成车道改变操作。纵向和横向运动的纵向和横向运动被认为是在该阶段优化随后距离的加速度和误差;在第二阶段,完成了在目标车道中的车辆之间调节纵向前进的操作,并且在此时段仅考虑车辆的纵向运动来优化车辆加速度误差。滚动优化时域算法用于逐步解决优化控制问题。最后,根据美国NGSIM开源流量流量数据库,验证了所提出的策略的准确性和可行性。

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