A Survey on Knowledge Graph-Based Methods for Automated Driving

Luettin, Juergen; Monka, Sebastian; Henson, Cory; Halilaj, Lavdim

doi:10.1007/978-3-031-21422-6_2

Cited by 8 publications

(2 citation statements)

References 88 publications

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“…For example, after receiving the ball, they need to quickly judge whether there is a scoring opportunity [20]. By using deep learning models to predict the performance of players under different spatiotemporal characteristics, optimize game tactics and training plans, and even provide real-time data support and suggestions [21]. Through this approach, we can better utilize spatiotemporal characteristics to interpret basketball game data, providing strong support for improving team competitiveness and player performance.…”

Section: ) Spatiotemporal Featuresmentioning

confidence: 99%

A Spatiotemporal Deep Learning-Based Multisource Data Analytics Framework for Basketball Game

Lin,

Bao,

Kang

2024

IEEE Access

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Data analytics has been an important business demand for basketball game. Conventionally, it was implemented with use of statistical approaches, yet neglecting the perception of spatiotemporal characteristics data. To deal with the problem, we introduce deep learning to reveal potential spatiotemporal features from multisource data. Therefore, a spatiotemporal deep learning-based multisource data analytics framework for basketball game, is proposed in this paper. Firstly, advanced deep learning models under spatiotemporal characteristics are elaborated, and a back propagation-based convolution neural network structure is utilized to extract the spatiotemporal features. Then, a systematic data collection and preprocessing method under spatiotemporal characteristics is defined, and thus formulating the spatiotemporal deep learning-based data analytics framework. Furthermore, we conduct a case study to make empirical analysis for the proposed technical framework. The results show that the proposal can well make prediction for some key factors of basketball game, and that it can provide some predictive information for the game preparation from the technical dimension.INDEX TERMS spatiotemporal deep learning; data analytics; technical forecasting; convolution neural network

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Section: ) Spatiotemporal Featuresmentioning

confidence: 99%

A Spatiotemporal Deep Learning-Based Multisource Data Analytics Framework for Basketball Game

Lin,

Bao,

Kang

2024

IEEE Access

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“…A knowledge graph is a technological means capable of mining, organizing, and efficiently managing knowledge from massive data, providing qualitative improvement for information services, and offering users more intelligent and personalized services [6]. In various fields, knowledge graphs have shown a wide range of application prospects, including search engine optimization in medical care [7][8][9][10][11][12] intelligent recommendation systems [13][14][15], intelligent transportation and urban planning, financial risk management [16][17][18][19][20][21], etc. Knowledge graphs can integrate scattered information within the industrial chain, establish comprehensive entity relationships, achieve efficient sharing and management of information, and thereby break information silos.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Knowledge Graph for Industrial Chain Based on Deep Learning

Li,

Lei,

Yan

et al. 2024

Electronics

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This paper aims to structure and semantically describe the information within the industrial chain by constructing an Industry Chain Knowledge Graph (ICKG), enabling more efficient and intelligent information management and analysis. In more detail, this paper constructs a multi-domain industrial chain dataset and proposes a method that combines the top-down establishment of a semantic expression framework with the bottom-up establishment of a data layer to build an ICKG. In the data layer, a deep learning algorithm based on BERT-BiLSTM-CRF is used to extract industry chain entities from relevant literature and reports. The results indicate that the model can effectively identify industry chain entities. These entities and relationships populate a Neo4j graph database, creating a large-scale ICKG for visual display and aiding cross-domain applications.

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Lanelet2 for nuScenes: Enabling Spatial Semantic Relationships and Diverse Map-based Anchor Paths

Naumann,

Hertlein,

Grimm

et al. 2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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A Survey on Knowledge Graph-Based Methods for Automated Driving

Cited by 8 publications

References 88 publications

A Spatiotemporal Deep Learning-Based Multisource Data Analytics Framework for Basketball Game

A Spatiotemporal Deep Learning-Based Multisource Data Analytics Framework for Basketball Game

Design and Development of Knowledge Graph for Industrial Chain Based on Deep Learning

Lanelet2 for nuScenes: Enabling Spatial Semantic Relationships and Diverse Map-based Anchor Paths

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