Learning data-driven decision-making policies in multi-agent environments for autonomous systems

Hook, Joosep; El-Sedky, Seif; Silva, Varuna De; Kondoz, A.M.

doi:10.1016/j.cogsys.2020.09.006

Cited by 16 publications

(6 citation statements)

References 19 publications

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“…In collision avoidance decision-making, optimization methods simultaneously defeat decentralization effects 142 . They use longitudinal and lateral constraints (LLC) to optimize a cost function concerning a collection of states and the input 155 . Several real-time motion planning issues are non-convex, optimization problems may become stuck in local minima and become computationally inefficient.…”

Section: Longitudinal and Lateral Constrains (Llc)mentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

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Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

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Section: Longitudinal and Lateral Constrains (Llc)mentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Various types of expert system, such as knowledge-based system, knowledge graph techniques and case-based reasoning, attempted to mimic human behaviours in automated decision-making [85][86][87][88]. Multi-agent system (MASs) are the promising research areas in the field of automated business process and workflow [89]. MASs aim to operate the automated decision-making process by a group of distributed AI via agent-communication language in a flexible manner [90][91][92].…”

Section: Paradigms Changes Of Intelligent Automationmentioning

confidence: 99%

A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives

Chen

Lee

et al. 2021

Advanced Engineering Informatics

142

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“…However, negotiating at the intersection is the task of the leader vehicle. A follower vehicle does not need to negotiate at an intersection unless it is separated from the leader vehicle by traffic signals [59], [60]. Fig.…”

Section: F Able To Negotiate In Intersectionmentioning

confidence: 99%

Autonomous Convoying: A Survey on Current Research and Development

et al. 2022

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Convoying or platooning with a fleet of autonomous vehicles, which is denoted as autonomous convoying in this paper, has attracted increasing attention from the research communities, governments, and private sectors in recent years. Autonomous convoying offers immense opportunities due to its potential in enhancing logistical efficiency as well as reducing road incidents/accidents by eliminating human errors due to stress and fatigue. While humans can make complex decisions, involving humans in decision-making processes often causes delays as compared with those of automated machines. Indeed, human errors cause approximately 90% of road accidents and fatalities. Efficient platooning techniques can also reduce fuel consumption and carbon footprints. This paper presents a concise survey on current research and development initiatives in autonomous convoying while critically discussing the underlying techniques and technologies developed in this domain. Implications of autonomous convoying toward different industries are also analyzed and discussed.

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Learning data-driven decision-making policies in multi-agent environments for autonomous systems

Cited by 16 publications

References 19 publications

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

A systematic literature review on intelligent automation: Aligning concepts from theory, practice, and future perspectives

Autonomous Convoying: A Survey on Current Research and Development

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