Multi‐mode switching‐based model predictive control approach for longitudinal autonomous driving with acceleration estimation

Abstract

This study proposes a multi-mode switching longitudinal autonomous driving system based on model predictive control (MPC) with acceleration estimation of proceeding vehicle. A hierarchical control framework composed of three layers is utilised. In the first layer, five longitudinal driving scenarios are defined based on emergency degree. In the second layer, the MPC for longitudinal autonomous driving is designed and serving as the upper controller. Among which a non-linear tracking differentiator is used for acceleration estimation of preceding vehicle. In the third layer, the inverse longitudinal vehicle system dynamic model with strong non-linearity is considered in the lower controller. Proportional-integral-derivative feedback and feedforward control are combined to track the desired acceleration. Simulation and hardware-in-loop test results show that the multi-mode switching longitudinal autonomous driving system is feasible and effective, and has important value for engineering application.

Mode Switching Control Using Lane Keeping Assist and Waypoints Tracking for Autonomous Driving in a City Environment

“…The full simulation uses 29 waypoints in total, including waypoints on the main road in case LKA sensing failed and waypoints at the junction. The additional constraints compared with scenario 1 are a prescribed time headway t h = 1:5s from a lead vehicle and an acceleration in the range [23,2] m.s 22 . The thresholds of similarity e 1 , e 2 between steering and acceleration using waypoint tracking and LKA MPC are both equal to 0.002, such that LKA MPC is used on the main road (F\ G), where there are no lane pattern issues and G is the junction area in this example where N J = 1.…”

“…The latter was shown in Bujarbaruah et al ( 21 ), where adaptive MPC was used to control the steering at constant speeds using a set membership-based switching strategy to deal with curvature changes during autonomous lane keeping. In Qu et al ( 22 ), MPC was used with five driving modes depending on a congestion dependent emergency coefficient and on the estimated acceleration of a lead vehicle, with improvements compared with single mode MPC in driving comfort and safety. In Xue et al ( 23 ), adaptive MPC was used for automated fallback maneuvers in presence of sensor malfunctions.…”

An Optimal Hierarchical Control Strategy for 4WS-4WD Vehicles Using Nonlinear Model Predictive Control

“…In these studies, self-driving cars use hierarchical control to manage complex tasks, which can be broadly categorized into different layers of environment sensing, path planning, and trajectory tracking; there is also policy switching in different scenarios. But hierarchical controllers can be used as a way to cope with the processing of complex nonlinear models and to link the dynamic model parameter requirements with kinematic control strategies [30]. Therefore, it is necessary to consider the vehicle's pose variation and introduce it into the tracking object of the controller.…”

High‐level decision‐making for autonomous overtaking: An MPC‐based switching control approach

“…To effectively deal with the aforementioned challenges and provide an alternative solution, model predictive control (MPC)-based optimisation methods are receiving significant attention because they can generate an optimal path/decision by solving an input and state constrained optimisation problem based on the latest available information in a receding prediction horizon fashion (see [16,[34][35][36][37][38][39][40][41][42][43][44][45][46][47]). This means that MPC method is applicable in real time and can avoid moving obstacles.…”

“…A finite state machine was employed as a high‐level decision‐maker in [16], where MPC was chosen as a trajectory planner. Reference [45] investigated a multi‐mode switching longitudinal autonomous driving system based on MPC, which is serving as the upper‐level controller. While these papers did not consider oncoming vehicles (as depicted in Figures 2 and 3), in practice, however, it is inevitable that a vehicle may emerge suddenly on the opposite lane from a crossing road after overtaking is initiated (see Figure 2), or the sensors of the autonomous vehicle did not detect an oncoming vehicle (e.g.…”