Trajectory Planning for Autonomous High-Speed Overtaking in Structured Environments Using Robust MPC
Shilp Dixit,Umberto Montanaro,Mehrdad Dianati,David Oxtoby,Tom Mizutani,Alexandros Mouzakitis,Saber Fallah +6 more
TLDR
A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway and motorway) is presented in this paper.Abstract:
Automated vehicles are increasingly getting main-streamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, and overtake) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway and motorway) is presented in this paper. A combination of a potential field like function and reachability sets of a vehicle are used to identify safe zones on a road that the vehicle can navigate towards. These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: 1) it is free from non-convex collision avoidance constraints; 2) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion; and 3) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for high-speed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment.read more
Citations
More filters
Journal ArticleDOI
A Survey of Deep Learning Applications to Autonomous Vehicle Control
TL;DR: The strengths and limitations of available deep learning methods are identified through comparative analysis and the research challenges in terms of computation, architecture selection, goal specification, generalisation, verification and validation, as well as safety are discussed.
Posted Content
A Survey of Deep Learning Applications to Autonomous Vehicle Control
TL;DR: In this article, a wide range of research works reported in the literature which aim to control a vehicle through deep learning methods are surveyed, focusing on vehicle control rather than the wider perception problem which includes tasks such as semantic segmentation and object detection.
Journal ArticleDOI
Robust Set-Invariance Based Fuzzy Output Tracking Control for Vehicle Autonomous Driving Under Uncertain Lateral Forces and Steering Constraints
TL;DR: Though the robust set-invariance property and Lyapunov-based arguments, the physical constraints on the steering input saturation and the vehicle state can be taken into account in the control design to improve the driving safety and comfort.
Journal ArticleDOI
Recent advances in motion and behavior planning techniques for software architecture of autonomous vehicles: A state-of-the-art survey
TL;DR: In this paper, the authors present an exhaustive and critical review of the existing approaches on motion and behavior planning for AVs in terms of their feasibility, capability in handling dynamic constraints and obstacles, and optimality of motion for comfort.
Proceedings ArticleDOI
Autonomous Overtaking in Gran Turismo Sport Using Curriculum Reinforcement Learning
TL;DR: In this article, a new learning-based method is proposed to tackle the autonomous overtaking problem in the popular car racing game Gran Turismo Sport, which is known for its detailed modeling of various cars and tracks.
References
More filters
Book
Predictive Control With Constraints
TL;DR: A standard formulation of Predictive Control is presented, with examples of step response and transfer function formulations, and a case study of robust predictive control in the context of MATLAB.
Journal ArticleDOI
Model predictive control: theory and practice—a survey
TL;DR: The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and ∞-norm formulations of the performance objective are discussed.
Book
Fundamentals of Vehicle Dynamics
TL;DR: In this article, the authors attempt to find a middle ground by balancing engineering principles and equations of use to every automotive engineer with practical explanations of the mechanics involved, so that those without a formal engineering degree can still comprehend and use most of the principles discussed.
Journal ArticleDOI
Invariant approximations of the minimal robust positively Invariant set
TL;DR: This note provides results on approximating the minimal robust positively invariant (mRPI) set (also known as the 0-reachable set) of an asymptotically stable discrete-time linear time-invariant system.
Proceedings ArticleDOI
Kinematic and dynamic vehicle models for autonomous driving control design
TL;DR: Experimental results show the effectiveness of the proposed approach at various speeds on windy roads, and it is shown that it is less computationally expensive than existing methods which use vehicle tire models.