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Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning
Abstract
In this paper, we propose a visualization method for driving behavior that helps people to recognize distinctive driving behavior patterns in continuous driving behavior data. Driving behavior can be measured using various types of sensors connected to a control area network. The measured multi-dimensional time series data are called driving behavior data. In many cases, each dimension of the time series data is not independent of each other in a statistical sense. For example, accelerator opening rate and longitudinal acceleration are mutually dependent. We hypothesize that only a small number of hidden features that are essential for driving behavior are generating the multivariate driving behavior data. Thus, extracting essential hidden features from measured redundant driving behavior data is a problem to be solved to develop an effective visualization method for driving behavior. In this paper, we propose using deep sparse autoencoder (DSAE) to extract hidden features for visualization of driving behavior. Based on the DSAE, we propose a visualization method called adriving color mapby mapping the extracted 3-D hidden feature to the red green blue (RGB) color space. A driving color map is produced by placing the colors in the corresponding positions on the map. The subjective experiment shows that feature extraction method based on the DSAE is effective for visualization. In addition, its performance is also evaluated numerically by using pattern recognition method. We a…
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Route planning is formulated as finding the point-to-point shortest path in a directed graph, and conventional methods are examined under four categories.
“Route planning is formulated as finding the point-to-point shortest path in a directed graph, and conventional methods are examined under four categories in [248].”
From A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2020) · cites this paper as [248]
HL is the fastest query time algorithm for route planning, at the expense of high storage usage.
“HL is the fastest query time algorithm for route planning [248], in the expense of high storage usage.”
From A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2020) · cites this paper as [248]
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A Survey of Autonomous Driving: Common Practices and Emerging Technologies
Yurtsever, E., Lambert, J., Carballo, A., & Takeda, K. (2020) · IEEE Access · doi:10.1109/access.2020.2983149 · Cited by 1,889
“Similarly, driving behavior was encoded in a 3-channel RGB space with a deep sparse autoencoder to visualize individual driving styles [241].”
Method
Metadata
- Authors
- Liu, H., Taniguchi, T., Tanaka, Y., Takenaka, K., & Bando, T.
- Affiliations
- Ritsumeikan University (JP)
- Denso (Japan) (JP)
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2017-09
- Volume
- 18
- Issue
- 9
- Pages
- 2477-2489
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2586457790
- Cited by
- 141
- References
- 2
- Citations by year
- 2026: 3 · 2025: 4 · 2024: 6 · 2023: 10 · 2022: 22 · 2021: 22 · 2020: 29 · 2019: 23 · 2018: 16 · 2017: 6
- FWCI
- 8.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Intelligent Transportation Systems (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (16J08577, 16H06569)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Data Visualization and Analytics
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Traffic and Road Safety
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Keywords
- deep sparse autoencoder, multivariate time series, pattern recognition, driver behavior recognition
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