Autonomy and Unmanned Vehicles Augmented Reactive Mission-Motion
Planning Architecture for Autonomous Vehicles
- URL: http://arxiv.org/abs/2007.09563v1
- Date: Sun, 19 Jul 2020 02:34:48 GMT
- Title: Autonomy and Unmanned Vehicles Augmented Reactive Mission-Motion
Planning Architecture for Autonomous Vehicles
- Authors: Somaiyeh MahmoudZadeh, David MW Powers, Reza Bairam Zadeh
- Abstract summary: This book aims to provide a comprehensive survey of UVs autonomy and its related properties in internal and external situation awareness.
An advance level of intelligence is essential to minimize the reliance on the human supervisor.
A self-controlled system needs a robust mission management strategy to push the boundaries towards autonomous structures.
- Score: 3.2013172123155615
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Advances in hardware technology have facilitated more integration of
sophisticated software toward augmenting the development of Unmanned Vehicles
(UVs) and mitigating constraints for onboard intelligence. As a result, UVs can
operate in complex missions where continuous trans-formation in environmental
condition calls for a higher level of situational responsiveness and autonomous
decision making. This book is a research monograph that aims to provide a
comprehensive survey of UVs autonomy and its related properties in internal and
external situation awareness to-ward robust mission planning in severe
conditions. An advance level of intelligence is essential to minimize the
reliance on the human supervisor, which is a main concept of autonomy. A
self-controlled system needs a robust mission management strategy to push the
boundaries towards autonomous structures, and the UV should be aware of its
internal state and capabilities to assess whether current mission goal is
achievable or find an alternative solution. In this book, the AUVs will become
the major case study thread but other cases/types of vehicle will also be
considered. In-deed the research monograph, the review chapters and the new
approaches we have developed would be appropriate for use as a reference in
upper years or postgraduate degrees for its coverage of literature and
algorithms relating to Robot/Vehicle planning, tasking, routing, and trust.
Related papers
- Exploring the Causality of End-to-End Autonomous Driving [57.631400236930375]
We propose a comprehensive approach to explore and analyze the causality of end-to-end autonomous driving.
Our work is the first to unveil the mystery of end-to-end autonomous driving and turn the black box into a white one.
arXiv Detail & Related papers (2024-07-09T04:56:11Z) - Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers? [60.51287814584477]
This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs.
We develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios.
arXiv Detail & Related papers (2024-05-14T09:42:21Z) - Forging Vision Foundation Models for Autonomous Driving: Challenges,
Methodologies, and Opportunities [59.02391344178202]
Vision foundation models (VFMs) serve as potent building blocks for a wide range of AI applications.
The scarcity of comprehensive training data, the need for multi-sensor integration, and the diverse task-specific architectures pose significant obstacles to the development of VFMs.
This paper delves into the critical challenge of forging VFMs tailored specifically for autonomous driving, while also outlining future directions.
arXiv Detail & Related papers (2024-01-16T01:57:24Z) - Interactive Autonomous Navigation with Internal State Inference and
Interactivity Estimation [58.21683603243387]
We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework.
These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents.
Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
arXiv Detail & Related papers (2023-11-27T18:57:42Z) - LLM4Drive: A Survey of Large Language Models for Autonomous Driving [62.10344445241105]
Large language models (LLMs) have demonstrated abilities including understanding context, logical reasoning, and generating answers.
In this paper, we systematically review a research line about textitLarge Language Models for Autonomous Driving (LLM4AD).
arXiv Detail & Related papers (2023-11-02T07:23:33Z) - Neurosymbolic Meta-Reinforcement Lookahead Learning Achieves Safe
Self-Driving in Non-Stationary Environments [17.39580032857777]
This study introduces an algorithm for online meta-reinforcement learning, employing lookahead symbolic constraints based on emphNeurosymbolic Meta-Reinforcement Lookahead Learning (NUMERLA)
Experimental results demonstrate NUMERLA confers the self-driving agent with the capacity for real-time adaptability, leading to safe and self-adaptive driving under non-stationary urban human-vehicle interaction scenarios.
arXiv Detail & Related papers (2023-09-05T15:47:40Z) - Assurance for Autonomy -- JPL's past research, lessons learned, and
future directions [56.32768279109502]
Autonomy is required when a wide variation in circumstances precludes responses being pre-planned.
Mission assurance is a key contributor to providing confidence, yet assurance practices honed over decades of spaceflight have relatively little experience with autonomy.
Researchers in JPL's software assurance group have been involved in the development of techniques specific to the assurance of autonomy.
arXiv Detail & Related papers (2023-05-16T18:24:12Z) - Survey of Deep Learning for Autonomous Surface Vehicles in the Marine
Environment [15.41166179659646]
Within the next several years, there will be a high level of autonomous technology that will be available for widespread use.
This paper surveys the existing work regarding the implementation of deep learning (DL) methods in ASV-related fields.
arXiv Detail & Related papers (2022-10-16T08:46:17Z) - Towards Safe, Explainable, and Regulated Autonomous Driving [11.043966021881426]
We propose a framework that integrates autonomous control, explainable AI (XAI), and regulatory compliance.
We describe relevant XAI approaches that can help achieve the goals of the framework.
arXiv Detail & Related papers (2021-11-20T05:06:22Z) - Current Advancements on Autonomous Mission Planning and Management
Systems: an AUV and UAV perspective [0.43036809606968096]
This paper serves as an introduction to UVs mission planning and management systems.
A comprehensive survey over autonomy assessment of UVs has been provided in this study.
The paper separately explains the humanoid and autonomous system's performance and highlights the role and impact of a human in UVs operations.
arXiv Detail & Related papers (2020-07-10T05:56:34Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.