A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal Data Article Swipe
Xinyu Tian
,
Qinghe Zheng
,
Zhiguo Yu
,
Mingqiang Yang
,
Yao Ding
,
Abdussalam Elhanashi
,
Sergio Saponara
,
Kidiyo Kpalma
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc7030131
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/bdcc7030131
At present, the design of modern vehicles requires improving driving performance while meeting emission standards, leading to increasingly complex power systems. In autonomous driving systems, accurate, real-time vehicle speed prediction is one of the key factors in achieving automated driving. Accurate prediction and optimal control based on future vehicle speeds are key strategies for dealing with ever-changing and complex actual driving environments. However, predicting driver behavior is uncertain and may be influenced by the surrounding driving environment, such as weather and road conditions. To overcome these limitations, we propose a real-time vehicle speed prediction method based on a lightweight deep learning model driven by big temporal data. Firstly, the temporal data collected by automotive sensors are decomposed into a feature matrix through empirical mode decomposition (EMD). Then, an informer model based on the attention mechanism is designed to extract key information for learning and prediction. During the iterative training process of the informer, redundant parameters are removed through importance measurement criteria to achieve real-time inference. Finally, experimental results demonstrate that the proposed method achieves superior speed prediction performance through comparing it with state-of-the-art statistical modelling methods and deep learning models. Tests on edge computing devices also confirmed that the designed model can meet the requirements of actual tasks.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bdcc7030131
- https://www.mdpi.com/2504-2289/7/3/131/pdf?version=1689415103
- OA Status
- gold
- Cited By
- 10
- References
- 58
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4384558143
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4384558143Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/bdcc7030131Digital Object Identifier
- Title
-
A Real-Time Vehicle Speed Prediction Method Based on a Lightweight Informer Driven by Big Temporal DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-07-15Full publication date if available
- Authors
-
Xinyu Tian, Qinghe Zheng, Zhiguo Yu, Mingqiang Yang, Yao Ding, Abdussalam Elhanashi, Sergio Saponara, Kidiyo KpalmaList of authors in order
- Landing page
-
https://doi.org/10.3390/bdcc7030131Publisher landing page
- PDF URL
-
https://www.mdpi.com/2504-2289/7/3/131/pdf?version=1689415103Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2504-2289/7/3/131/pdf?version=1689415103Direct OA link when available
- Concepts
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Computer science, Key (lock), Process (computing), Automotive industry, Inference, Deep learning, Enhanced Data Rates for GSM Evolution, Artificial intelligence, Machine learning, Big data, Real-time computing, Data mining, Engineering, Computer security, Aerospace engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
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2024: 8, 2023: 2Per-year citation counts (last 5 years)
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58Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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