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Safe Hierarchical Model Predictive Control and Planning for Autonomous Systems [article]

Markus Koegel, Mohamed Ibrahim, Christian Kallies, Rolf Findeisen
2022 arXiv   pre-print
Planning and control for autonomous vehicles usually are hierarchical separated.  ...  A cyclic horizon tube-based model predictive controller guarantees constraint satisfaction for different control modes and disturbances.  ...  In 13 , a stable hierarchical model predictive control (MPC) is introduced using an inner loop reference model and contracting constraint sets for guaranteeing the overall stability.  ... 
arXiv:2203.14269v1 fatcat:x35mp3kmwbdptfj2hvu6b5mboa

SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for Autonomous Driving in Urban Environments [article]

Arec Jamgochian, Etienne Buehrle, Johannes Fischer, Mykel J. Kochenderfer
2022 arXiv   pre-print
Designing a safe and human-like decision-making system for an autonomous vehicle is a challenging task.  ...  However, to scale to complex settings, many autonomous driving systems combine fixed, safe, optimization-based low-level controllers with high-level decision-making logic that selects the appropriate task  ...  ACKNOWLEDGMENT The authors acknowledge Kunal Menda for early work on the Interaction simulator.  ... 
arXiv:2204.01922v1 fatcat:4pa6ic7osrfhnpktjt7uwcm4ra

Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation [article]

Yunho Kim, Chanyoung Kim, Jemin Hwangbo
2022 arXiv   pre-print
For autonomous quadruped robot navigation in various complex environments, a typical SOTA system is composed of four main modules -- mapper, global planner, local planner, and command-tracking controller  ...  In this work, we propose a learning-based fully autonomous navigation framework composed of three innovative elements: a learned forward dynamics model (FDM), an online sampling-based model-predictive  ...  For autonomous navigation, the robot should be able to plan a safe and efficient plan.  ... 
arXiv:2204.08647v3 fatcat:swudz2xdkngtdp46hx6apqjopq

Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning [article]

Kasra Rezaee, Peyman Yadmellat, Masoud S. Nosrati, Elmira Amirloo Abolfathi, Mohammed Elmahgiubi, Jun Luo
2021 arXiv   pre-print
This paper proposes a design for autonomous multi-lane cruising by combining a hierarchical reinforcement learning framework with a novel state-action space abstraction.  ...  While the proposed solution follows the classical hierarchy of behavior decision, motion planning and control, it introduces a key intermediate abstraction within the motion planner to discretize the state-action  ...  structure in the design of planning systems for autonomous driving.  ... 
arXiv:2110.00650v1 fatcat:jhsf2uxo5ndzfjg5l6dy6ofzli

Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement Learning [article]

Kaleb Ben Naveed, Zhiqian Qiao, John M. Dolan
2020 arXiv   pre-print
Planning safe trajectories under uncertain and dynamic conditions makes the autonomous driving problem significantly complex.  ...  To address these problems and in order to ensure a robust framework, we propose a Hierarchical Reinforcement Learning (HRL) structure combined with a Proportional-Integral-Derivative (PID) controller for  ...  for the support and providing resources for remote research.  ... 
arXiv:2011.04752v1 fatcat:3gh2zmhw2vfhnoygr62pdtipxm


Alaa Bensaid, Adil Sayouti, Hicham Medromi
2020 Zenodo  
architecture is a family of intelligent control systems, hybrid and decomposed into flexible autonomous subsystems, its containing elements of sensory processing, world modeling, localization, Mission  ...  planning & high-level Expert system, and action processes to achieve or maintain its goals.  ...  Thus hierarchical control is seemingly well suited for structured and highly predictable environments, but is inappropriate for dynamic environments which require timely responses. 279 Two of the most  ... 
doi:10.5281/zenodo.3694723 fatcat:6oh5jxy5rraphljyjfckvz7aiq

Hierarchical Control of Trajectory Planning and Trajectory Tracking for Autonomous Parallel Parking

Duoyang Qiu, Duoli Qiu, Bing Wu, Man Gu, Maofei Zhu
2021 IEEE Access  
algorithm and model predictive control.  ...  For the parallel parking problem in narrow space, this paper proposes a trajectory tracking control method with a novel trajectory planning layer for autonomous parallel parking based on a numerical optimization  ...  This paper proposes a GPM-MPC based hierarchical control method for autonomous parking trajectory planning and tracking according to the above architecture.  ... 
doi:10.1109/access.2021.3093930 fatcat:eg45uj54dzcevei47o3gnvrsby

Hierarchical Game-Theoretic Planning for Autonomous Vehicles [article]

Jaime F. Fisac, Eli Bronstein, Elis Stefansson, Dorsa Sadigh, S. Shankar Sastry, Anca D. Dragan
2018 arXiv   pre-print
In this paper, we introduce a novel game-theoretic trajectory planning algorithm for autonomous driving, that enables real-time performance by hierarchically decomposing the underlying dynamic game into  ...  , quantitatively accounting for the autonomous vehicle and the human driver's ability and incentives to influence each other.  ...  DYNAMIC GAME FORMULATION We consider a single 1 human driver H and a single autonomous system A in control of their respective vehicles.  ... 
arXiv:1810.05766v1 fatcat:f6hpqdsnbjbbdiaqqtl4jsafou

Developing and testing of control software framework for autonomous ground vehicle

Maradona Rodrigues, Andrew McGordon, Graham Gest, James Marco
2017 2017 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)  
Path Planning Implementation A three-level hierarchical path planning framework was developed to facilitate the development of an adaptive control capability for the autonomous vehicle.  ...  The real-world driving scenario can either be predictable or unpredictable at different times and for an autonomous vehicle to be successful in generating a collision free motion plan, it needs an autonomous  ... 
doi:10.1109/icarsc.2017.7964044 dblp:conf/icarsc/RodriguesMGM17 fatcat:qjduyydxu5dk7o4auxwpeegygm

Towards Safe, Explainable, and Regulated Autonomous Driving [article]

Shahin Atakishiyev, Mohammad Salameh, Hengshuai Yao, Randy Goebel
2022 arXiv   pre-print
However, as demonstrated by recent traffic accidents, autonomous driving technology is not mature for safe deployment.  ...  We propose a framework that integrates autonomous control, explainable AI, and regulatory compliance to address this issue and validate the framework with a critical analysis in a case study.  ...  ACKNOWLEDGMENTS We acknowledge support from the Alberta Machine Intelligence Institute (Amii), from the Computing Science Department of the University of Alberta, and the Natural Sciences and Engineering  ... 
arXiv:2111.10518v3 fatcat:topadg7bp5enhflk7yqm6j27ga

Exploring applications of deep reinforcement learning for real-world autonomous driving systems [article]

Victor Talpaert, Ibrahim Sobh, B Ravi Kiran, Patrick Mannion, Senthil Yogamani, Ahmad El-Sallab, Patrick Perez
2019 arXiv   pre-print
We first provide an overview of the tasks in autonomous driving systems, reinforcement learning algorithms and applications of DRL to AD systems.  ...  It has been successfully deployed in commercial vehicles like Mobileye's path planning system.  ...  Background The software architecture for Autonomous Driving systems comprises of the following high level tasks: Sensing, Perception, Planning and Control.  ... 
arXiv:1901.01536v3 fatcat:y3gck5rznjglvim4gem5dvb2ue

Decision-Making Technology for Autonomous Vehicles Learning-Based Methods, Applications and Future Outlook [article]

Qi Liu, Xueyuan Li, Shihua Yuan, Zirui Li
2021 arXiv   pre-print
This article proposes a brief review on learning-based decision-making technology for autonomous vehicles since it is significant for safer and efficient performance of autonomous vehicles.  ...  Autonomous vehicles have a great potential in the application of both civil and military fields, and have become the focus of research with the rapid development of science and economy.  ...  Model Predictive Control (MPC) is one of the feasible methods for decision-making.  ... 
arXiv:2107.01110v1 fatcat:ohffatmrmfbzdlihgvywryzupa

Multistage Stochastic Model Predictive Control for Urban Automated Driving [article]

Tommaso Benciolini, Tim Brüdigam, Marion Leibold
2021 arXiv   pre-print
Hence, we propose to use Stochastic Model Predictive Control for vehicle control in urban driving, allowing to efficiently plan the vehicle trajectory, while maintaining the risk probability sufficiently  ...  For motion optimization, we propose to use a two-stage hierarchical structure that plans the trajectory and the maneuver separately.  ...  In [10] , [11] a detailed model is used for short-term predictions, and a simplified one for long-term predictions.  ... 
arXiv:2107.00529v1 fatcat:tdtkgclg55exlmp3s32gtt73yq

A Survey of Cooperative Driving between Auxiliary Autonomous System and Human Driver

Qijie Zou, Haoyu Li, Rubo Zhang, Tengda Pei, X. Lei, S.P. Kozaitis, C.-C. Ku
2018 MATEC Web of Conferences  
This paper provides insights into the scope of decision-making and motion planning for cooperative driving, as well as the shortcomings and tendencies.  ...  According to their respective advantages, the cooperative driving between human and autonomous system can have new synergies.  ...  To limit the scope of this survey, we focus on the aspects of decision making, motion planning, driver modelling and collaboration controlling for auxiliary autonomous system.  ... 
doi:10.1051/matecconf/201816005001 fatcat:wml3stfq25ghdehlj5a2i4coye

Special Issue on HMI and Autonomous Driving

Fang Chen
2020 Automotive Innovation  
Collision Avoidance System Design Based on Model Predictive Control with Varying Sampling Time," based on the hierarchical control framework, an improved MPC controller is proposed, featuring varying  ...  Finally, in the paper "Robust Cooperative Control of Multiple Autonomous Vehicles for Platoon Formation Considering Parameter Uncertainties," a robust cooperative vehicle control framework to achieve safe  ... 
doi:10.1007/s42154-020-00094-1 fatcat:4wvx6tmncjegbpms6gothxgq2q
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