Event-Triggered Model Predictive Adaptive Dynamic Programming for Road Intersection Path Planning of Unmanned Ground Vehicle

Hu, Chaofang; Zhao, Lingxue; Qu, Ge

doi:10.1109/tvt.2021.3111692

Cited by 23 publications

(2 citation statements)

References 33 publications

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“…Numerous research papers have addressed the low-level motion planning problem and control at urban unsignalized intersections using Model Predictive Control (MPC) principles. For instance, Hu et al [32] proposed an event-triggered model predictive adaptive dynamic programming technique for motion planning at urban intersections. The method takes urban speed, vehicle kinematics and road constraints into consideration while solving a multi-objective optimization problem.…”

Section: E Mpc For Motion Planning At Intersectionsmentioning

confidence: 99%

Self-Learned Autonomous Driving at Unsignalized Intersections: A Hierarchical Reinforced Learning Approach for Feasible Decision-Making

Al-Sharman¹,

Dempster²,

Daoud³

et al. 2022

Preprint

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<p> Reinforcement learning-based techniques, empowered by deep-structured neural nets, have demonstrated superiority over rule-based methods in terms of making high-level behavioral decisions due to qualities related to handling large state spaces. Nonetheless, their training time, sample efficiency and the feasibility of the learnt behaviors remain key concerns. In this paper, we propose a novel hierarchical reinforcement learning-based decision-making architecture for learning left-turn policies at unsignalized intersections with feasibility guarantees. The proposed technique is comprised of two layers; a high-level learning-based behavioral planning layer which adopts soft actor-critic principles to learn high-level, non-conservative yet safe, driving behaviors, and a low-level Model Predictive Control (MPC) framework to ensure feasibility of the two-dimensional left-turn maneuver. The high-level layer generates reference signals of velocity and yaw angles for the ego vehicle taking into account safety and collision avoidance with the intersection vehicles, whereas the low-level planning layer solves an optimization problem to track these reference commands taking into account several vehicle dynamic constraints and ride comfort. We validate the proposed decision-making scheme in simulated environments and compare with other model free Reinforcement Learning (RL) baselines. The results demonstrate that the proposed integrated framework possesses better training and navigation capabilities.</p>

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Section: E Mpc For Motion Planning At Intersectionsmentioning

confidence: 99%

Self-Learned Autonomous Driving at Unsignalized Intersections: A Hierarchical Reinforced Learning Approach for Feasible Decision-Making

Al-Sharman¹,

Dempster²,

Daoud³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In Kong et al (2020), a robust tracking control scheme based on ADP is designed for robots. In Hu et al (2021), an ADP algorithm is proposed to solve path planning problem of the unmanned ground vehicle. It also be well applied to flight control, such as regulation and tracking control (Xia et al, 2020; Zhang et al, 2020; Zheng et al, 2019; Zhou et al, 2017, 2018).…”

Section: Introductionmentioning

confidence: 99%

Incremental adaptive optimal control for nonlinear systems with disturbance and input time-delay

Zhang

2022

Transactions of the Institute of Measurement and Control

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In this paper, a model-free incremental adaptive optimal control scheme is proposed for nonlinear systems in presence of disturbance and input time-delay. The linear time-varying approximate model of the nonlinear system is obtained by incremental method, in which the relevant matrix parameters are identified by recursive least squares (RLS) estimation. A time-delay matrix function is constructed by neural network (NN) to eliminate the input time-delay, and the external disturbance of nonlinear system is dealt with by [Formula: see text] optimal control; incremental adaptive dynamic programming (IADP) algorithm is used to get the control law by solving Hamilton–Jacobi–Isaacs (HJI) equation. Convergence analysis of the proposed control scheme is provided. Simulations are given to verify the effectiveness of the proposed control scheme.

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Event-Triggered bundled target traversing path planning using a dynamic elliptical guidance region for unmanned surface vehicles

Jin,

Zhang,

et al. 2023

Applied Ocean Research

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Event-Triggered Model Predictive Adaptive Dynamic Programming for Road Intersection Path Planning of Unmanned Ground Vehicle

Cited by 23 publications

References 33 publications

Self-Learned Autonomous Driving at Unsignalized Intersections: A Hierarchical Reinforced Learning Approach for Feasible Decision-Making

Self-Learned Autonomous Driving at Unsignalized Intersections: A Hierarchical Reinforced Learning Approach for Feasible Decision-Making

Incremental adaptive optimal control for nonlinear systems with disturbance and input time-delay

Event-Triggered bundled target traversing path planning using a dynamic elliptical guidance region for unmanned surface vehicles

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