A Novel Solver Based on Interior Point Method Applied to Parking Route Planning

In recent years, parking technology has seen significant advancements, with many automobile manufacturers incorporating automatic parking systems into their new vehicles. A critical component of these systems is parking route planning, which involves accurately describing parking scenarios and vehicle kinematics by formulating the problem as an optimal control problem. It is difficult to parameterize the optimal control problem as a nonlinear programming (NLP) problem. Instead, in this paper the optimal control problem is discretized into a relatively tractable NLP problem using an orthogonal configuration finite element method. Previously many used the Interior Point Optimize (IPOPT) solver in interior point method to solve the NLP problem, whereas in this study a new solver Nonlinear Interior-point Trust Region Optimization (KNITRO) solver will be proposed, which is simulated and compared with the IPOPT solver to verify that it is more effective in making the traj ectories of perpendicular and parallel parking paths planning shorter and requires less time.