Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93342
DC Field | Value | Language |
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dc.contributor | Department of Aeronautical and Aviation Engineering | en_US |
dc.contributor | Department of Mechanical Engineering | en_US |
dc.creator | Fang, W | en_US |
dc.creator | Li, H | en_US |
dc.creator | Dang, S | en_US |
dc.creator | Huang, H | en_US |
dc.creator | Peng, L | en_US |
dc.creator | Hsu, LT | en_US |
dc.creator | Wen, W | en_US |
dc.date.accessioned | 2022-06-20T07:48:33Z | - |
dc.date.available | 2022-06-20T07:48:33Z | - |
dc.identifier.isbn | 978-1-7281-6092-4 (Electronic ISBN) | en_US |
dc.identifier.isbn | 978-1-7281-6093-1 (Print on Demand(PoD) ISBN) | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93342 | - |
dc.language.iso | en | en_US |
dc.publisher | IEEE | en_US |
dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | en_US |
dc.rights | The following publication W. Fang et al., "Combining Deep Gaussian Process and Rule-Based Method for Decision-Making in Self-Driving Simulation with Small Data," 2019 15th International Conference on Computational Intelligence and Security (CIS), 2019, pp. 267-271 is available at https://dx.doi.org/10.1109/CIS.2019.00063. | en_US |
dc.subject | Decision-making | en_US |
dc.subject | Gaussian process | en_US |
dc.subject | Kernel function | en_US |
dc.subject | Rule-based | en_US |
dc.title | Combining deep gaussian process and rule-based method for decision-making in self-driving simulation with small data | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 267 | en_US |
dc.identifier.epage | 271 | en_US |
dc.identifier.doi | 10.1109/CIS.2019.00063 | en_US |
dcterms.abstract | Self-driving vehicle is a popular and promising field in artificial intelligence. Conventional architecture consists of multiple sensors, which work collaboratively to sense the units on road to yield a precise and safe driving strategy. Besides the precision and safety, the quickness of decision is also a major concern. In order to react quickly, the vehicle need to predict its next possible action, such as acceleration, brake and steering angle, according to its latest few actions and status. In this paper, we treat this decision-making problem as a regression problem and use deep gaussian process to predict its next action. The regression model is trained using simulation data sets and accurately captures the most significant features. Combined with rule-based method, it can be used in Torcs simulation engine to realize successful loop trip on virtual roads. The proposed method outperforms the existing reinforcement learning methods on the performance indicators of time consumption and the size of data volume. It may be useful for real road tests in the future. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | 2019 15th International Conference on Computational Intelligence and Security (CIS), Macao, China, 13-16 December 2019, p. 267-271 | en_US |
dcterms.issued | 2019-12 | - |
dc.identifier.scopus | 2-s2.0-85082301153 | - |
dc.relation.conference | International Conference on Computational Intelligence and Security [CIS] | en_US |
dc.description.validate | 202206 bckw | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AAE-0104 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Shenzhen S&T Funding | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 23859100 | - |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Hsu_Combining_Deep_Gaussian.pdf | Pre-Published version | 334.21 kB | Adobe PDF | View/Open |
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