A study on al-based approaches for high-level decision making in highway autonomous driving

Claussmann, Laurène; Revilloud, Marc; Glaser, Sébastien; Gruyer, Dominique

doi:10.1109/smc.2017.8123203

Cited by 19 publications

(7 citation statements)

References 24 publications

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“…The AI-based algorithms are introduced in route planning due to the abilities of thinking, reasoning, and memory, and they can be categorized as follows [95]: (a) knowledge-based algorithm; (b) heuristic algorithm; (c) approximate logic algorithm; (d) cognitive algorithm. The comparisons of the above algorithms in real-world scenarios are shown in Table 6 [96], where the evaluation was indicated with a number scale.…”

Section: Ai-based Decision-making Algorithmsmentioning

confidence: 99%

Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey

Zhang¹,

Chen²,

Guo³

2022

CMES

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Automated Guided Vehicles (AGVs) have been introduced into various applications, such as automated warehouse systems, flexible manufacturing systems, and container terminal systems. However, few publications have outlined problems in need of attention in AGV applications comprehensively. In this paper, several key issues and essential models are presented. First, the advantages and disadvantages of centralized and decentralized AGVs systems were compared; second, warehouse layout and operation optimization were introduced, including some omitted areas, such as AGVs fleet size and electrical energy management; third, AGVs scheduling algorithms in chessboardlike environments were analyzed; fourth, the classical route-planning algorithms for single AGV and multiple AGVs were presented, and some Artificial Intelligence (AI)-based decision-making algorithms were reviewed. Furthermore, a novel idea for accelerating route planning by combining Reinforcement Learning (RL) and Dijkstra's algorithm was presented, and a novel idea of the multi-AGV route-planning method of combining dynamic programming and Monte-Carlo tree search was proposed to reduce the energy cost of systems.

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Section: Ai-based Decision-making Algorithmsmentioning

confidence: 99%

Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey

Zhang¹,

Chen²,

Guo³

2022

CMES

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“…Automation with inherent artificial intelligence (AI) [15] Current and major algorithms in the specific field of artificial intelligence for autonomous vehicles. Such systems are particularly suited for high-level decision making since they must, by definition, be able to perceive and react to their environment in order to reach given objectives [16] Internet of Intelligent Things Artificial intelligence techniques employed to create such intelligence, and network solutions to exploit the benefits brought by this capability [17] Mobile Cloud Computing MCC…”

Section: Autonomous Vehicles and Medical Assistance Devicesmentioning

confidence: 99%

Artificial intelligence in mobile communication: A Survey

Mao

Pranolo

Hernandez³

et al. 2022

IOP Conf. Ser.: Mater. Sci. Eng.

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In this paper, we elaborate on artificial intelligence (AI) techniques used to improve the performance of mobile communication. This article describes brief AI approaches in mobile communication, several classics AI techniques, and the current AI approaches in wireless communication. The techniques contain fuzzy logic, neural networks, reinforcement learning, and AI techniques implemented on mobile communication. Some keys or terms challenges between AI and future mobile communication, not only 5G generation issues but also how the sixth generation (6G) of mobile networks will be driven to give stable networks and service types on huge mobile devices and data.

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“…Several surveys focusing on different research topics in AD have been published, including computer vision [53], object detection [3,31], DL based scene understanding [71,36,33,49], DL based vehicle behavior prediction [78], deep reinforcement learning [61], lane detection [118,79,99], semantic segmentation [65,73], and planning and decision making [28,82,94,8,18]. More recently, ontologies and KGs have gained interest for knowledge-infused learning approaches.…”

Section: Knowledge Representation Learningmentioning

confidence: 99%

A Survey on Knowledge Graph-based Methods for Automated Driving

Luettin¹,

Monka²,

Henson³

et al. 2022

Preprint

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Automated driving is one of the most active research areas in computer science. Deep learning methods have made remarkable breakthroughs in machine learning in general and in automated driving (AD) in particular. However, there are still unsolved problems to guarantee reliability and safety of automated systems, especially to effectively incorporate all available information and knowledge in the driving task. Knowledge graphs (KG) have recently gained significant attention from both industry and academia for applications that benefit by exploiting structured, dynamic, and relational data. The complexity of graph-structured data with complex relationships and inter-dependencies between objects has posed significant challenges to existing machine learning algorithms. However, recent progress in knowledge graph embeddings and graph neural networks allows to applying machine learning to graph-structured data. Therefore, we motivate and discuss the potential benefit of KGs applied to the main tasks of AD including 1) ontologies 2) perception, 3) scene understanding, 4) motion planning, and 5) validation. Then, we survey, analyze and categorize ontologies and KG-based approaches for AD. We discuss current research challenges and propose promising future research directions for KG-based solutions for AD.

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A study on al-based approaches for high-level decision making in highway autonomous driving

Cited by 19 publications

References 24 publications

Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey

Application of Automated Guided Vehicles in Smart Automated Warehouse Systems: A Survey

Artificial intelligence in mobile communication: A Survey

A Survey on Knowledge Graph-based Methods for Automated Driving

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