Research on Lane Change Motion Planning Steering Input Based on Optimal Control Theory

Abstract

Aiming at traditional control methods are not suitable for solving the multiconstraint problem of the vehicle steering and lane-change maneuvering process, a hybrid optimization scheme based on dynamic adaptive salp swarm algorithm and Gaussian pseudospectral method (DASSA-GPM) is proposed for its strong global search capability. Based on a steering inverse dynamics model, the lane change motion planning steering problem is converted into an optimal control problem which is then converted into a nonlinear programming problem by applying the adaptive salp group algorithm which is solved through the sequential quadratic programming (SQP) method finally. The simulation results verify the effectiveness and feasibility of the proposed dynamic adaptive salp swarm algorithm and hybrid optimization scheme. In addition, compared with GPM which the absolute error of steering wheel angle is 8.2 × 10−4 degree, the proposed method has higher computational accuracy, which absolute error of steering wheel angle is 5.5 × 10−4 degree. At the same time, under the same calculation accuracy (10−3), compared with GPM which CPU time is 4.81 s, the proposed method has higher calculation efficiency which CPU time is 3.86 s when solving non-smooth problems. The proposed method can provide a reference value into the active safety of manned and unmanned vehicles.