An Integrated Motion Planning Scheme for Safe Autonomous Vehicles in Highly Dynamic Environments

Abstract

This study proposes a new integrated approach to the motion control of autonomous vehicles, which differs from the conventional method of treating planning and tracking tasks as separate or hierarchical components. By means of the proposed approach we can reduce the side effects on the performance of autonomous vehicles under challenging driving circumstances. To this end, our approach processes both of the aforementioned tasks asynchronously and simultaneously utilizes a multi-threaded architecture to enhance control performance. Meanwhile, the behavior planning feature is integrated into the path-tracking module. Then, a linear parameter-varying model predictive control is deployed for trajectory tracking of autonomous vehicles and compared with the linear model predictive control method. Finally, the control performance of the proposed approach was evaluated through simulation trials on urban roads with placed obstacles. The outcomes revealed that the suggested framework satisfies the processing rate and high-precision criteria, while safely avoiding obstacles, indicating that it is a promising control strategy for real-world applications.

Planning Speed Mode of All-Wheel Drive Autonomous Vehicles Considering Complete Constraint Set

“…where j = [1,3] for the front axle, and j = [2,4] for the rear axle. Then, the ratios k f , k r of the moments on the front and rear axles' wheels…”

“…Usually, planning and tracking stages are separated as hierarchical components; however, a complex approach is also possible [1]. The planning quality reduces the time cost needed for optimizing the control at the tracking stage, as well as minimizes the probability of AV's unstable motion modes.…”

“…A lateral speed prediction model for high-level motion planning was considered with economic nonlinear model predictive control (E-NMPC). In [1], a linear parameter-varying (LPV) MPC was deployed for AV trajectory tracking and compared with the linear MPC. The outcomes satisfy the processing rate and high-precision criteria, including safely avoiding obstacles.…”

Finite-Time Robust Path-Following Control of Perturbed Autonomous Ground Vehicles Using a Novel Self-Tuning Nonsingular Fast Terminal Sliding Manifold

“…In path following, position and orientation errors are handled through several approaches [3,4]. Although their effectiveness was proven to bring good performance, the system stability is hardly considered [5], and it is quite complex to consider a multi-input-multi-output nonlinear system [6][7][8]. Fortunately, the control design process for path following can be simplified with the assistance of the Lyapunov function [9].…”