Dynamic Trajectory Planning and Tracking for Autonomous Vehicle With Obstacle Avoidance Based on Model Predictive Control

Abstract

In this study, an obstacle avoidance controller based on nonlinear model predictive control is designed in autonomous vehicle navigation. The reference trajectory is predefined using a sigmoid function in accordance with road conditions. When obstacles suddenly appear on a predefined trajectory, the reference trajectory should be adjusted dynamically. For dynamic obstacles, a moving trend function is constructed to predict the obstacle position variances in the predictive horizon. Furthermore, a risk index is constructed and introduced into the cost function to realize collision avoidance by combining the relative position relationship between vehicle and obstacles in the predictive horizon. Meanwhile, lateral acceleration constraint is also considered to ensure vehicle stability. Finally, trajectory dynamic planning and tracking are integrated into a single-level model predictive controller. Simulation tests reveal that the designed controller can ensure real-time trajectory tracking and collision avoidance.INDEX TERMS Autonomous vehicle, dynamic obstacles, moving trend function, dynamic planning and tracking, single-level controller.

Impact of MPC Embedded Performance Index on Control Quality

“…The researchers have investigated multivariate, nonlinear constrained MPC configurations [3], [4]. As a result, MPC algorithms have been applied to numerous processes, ranging from relatively slow process control plants such as chemical reactors [5], distillation columns [6], NOx control [7] and coal mills [8] to very systems such as fast robots [9], micro grids [10], electric drives [11], sparkignition gasoline engines [12] and autonomous vehicles [13]. Recently, Williams et al [14] proposed a sampling-based and derivative-free MPC algorithm, known as Model Predictive Path Integral (MPPI) control framework, that can be easily utilized without requiring the first-or second-order approxi-mation of the system dynamics and quadratic approximation of the objective functions.…”

Computationally Simple Nonlinear MPC Algorithm for Vehicle Obstacle Avoidance With Minimization of Fuel Utilization

“…The most important practical problem that characterizes the existing MPC algorithms used for obstacle avoidance is the necessity of solving in real-time nonlinear optimization tasks. The gradient-based Sequential Quadratic Programming (SQP) algorithm may be used for this purpose [20], [25]- [29]. An alternative is to use heuristic Particle Swarm Optimization (PSO) algorithm [22].…”

“…Numerous works, e.g. [20], [22], [25]- [29], use for the first of the mentioned tasks nonlinear on-line optimization, in this work much simpler quadratic programming is only necessary. As pointed out previously, fuel usage optimization is typically treated separately while in this work these two objectives are possible in MPC.…”

MPC Based Vehicular Trajectory Planning in Structured Environment

“…Due to these factors, optimization approaches are limited in the simple design of objective functions or constraint design. The applicability of quadratic programs has been demonstrated in [4], [17]. Additionally, integrated trajectory planning with dynamic control by MPC scheme was solved by quadratic objective function and linear constraints.…”