Enhancing Dimensionality Reduction in Driving Behavior Learning: Integrating SENet with VAE
This study addresses a common limitation of conventional Variational Autoencoder (VAE)-based methods in dimensionality reduction for state representation learning, especially in autonomous driving, by integrating Squeeze-and-Excitation Networks (SENet) into the VAE framework. While traditional VAE a...
Saved in:
| Published in | International Journal of Networking and Computing Vol. 15; no. 2; pp. 138 - 152 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
IJNC Editorial Committee
2025
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 2185-2839 2185-2847 2185-2847 |
| DOI | 10.15803/ijnc.15.2_138 |
Cover
| Abstract | This study addresses a common limitation of conventional Variational Autoencoder (VAE)-based methods in dimensionality reduction for state representation learning, especially in autonomous driving, by integrating Squeeze-and-Excitation Networks (SENet) into the VAE framework. While traditional VAE approaches effectively handle high-dimensional data with reduced computational costs, they often struggle to adequately capture complex features in certain tasks. To overcome this challenge, we propose the SENet-VAE model, which incorporates SENet into the VAE architecture, and evaluate its performance in driving behavior learning using deep reinforcement learning. Our experiments compare three setups: raw image data, conventional VAE, and SENet-VAE. Furthermore, we examine how the placement and number of SE-Blocks affect performance. The results demonstrate that SENet-VAE surpasses the limitations of conventional VAE and achieves superior accuracy in learning. This work highlights the potential of SENet-VAE as a robust dimensionality reduction solution for state representation learning. |
|---|---|
| AbstractList | This study addresses a common limitation of conventional Variational Autoencoder (VAE)-based methods in dimensionality reduction for state representation learning, especially in autonomous driving, by integrating Squeeze-and-Excitation Networks (SENet) into the VAE framework. While traditional VAE approaches effectively handle high-dimensional data with reduced computational costs, they often struggle to adequately capture complex features in certain tasks. To overcome this challenge, we propose the SENet-VAE model, which incorporates SENet into the VAE architecture, and evaluate its performance in driving behavior learning using deep reinforcement learning. Our experiments compare three setups: raw image data, conventional VAE, and SENet-VAE. Furthermore, we examine how the placement and number of SE-Blocks affect performance. The results demonstrate that SENet-VAE surpasses the limitations of conventional VAE and achieves superior accuracy in learning. This work highlights the potential of SENet-VAE as a robust dimensionality reduction solution for state representation learning. |
| Author | Yuta Uehara Susumu Matsumae |
| Author_xml | – sequence: 1 givenname: Yuta surname: Uehara fullname: Uehara, Yuta – sequence: 2 givenname: Susumu surname: Matsumae fullname: Matsumae, Susumu |
| BookMark | eNqFkE9vwjAMxaOJSWOM6879AoWkJWqywyQG3YaENmn_LjtUbnBoUElRGkB8-7Uwod3mi5_t9_PhXZOOrSwScsvogHFB46FZWdXIQZSxWFyQbsQEDyMxSjpnHcsr0q_rFW0qSSSN4i75Tm0BVhm7DKZmjbY2lYXS-EPwhout8s0YGBtMndm1ngcsYGcqF8wRnG02d8HMelw68O35PX1BH-yNL4KvcXpDLjWUNfZ_e498PqYfk-dw_vo0m4znoYooFeFCwwI5l8hBRAo1MKW1kjDSXHPOE5aDSmKUMhEyj6hWWjHJaJ4IofOGjXtkePq7tRs47KEss40za3CHjNHsGE_WxtPI7BhPQwxOhHJVXTvU_wP3J2BVe1ji2Q7OG1XiXzdvgfNBFeAytPEPI9KC8Q |
| Cites_doi | 10.1007/s10596-022-10159-1 10.1109/CVPR.2018.00745 10.1109/CANDARW64572.2024.00069 10.1109/ICRA40945.2020.9197465 10.1109/ICRA.2019.8793742 10.1109/CANDARW60564.2023.00064 10.3389/fnbot.2023.1301785 10.3390/s20215991 |
| ContentType | Journal Article |
| Copyright | 2025 International Journal of Networking and Computing |
| Copyright_xml | – notice: 2025 International Journal of Networking and Computing |
| DBID | AAYXX CITATION ADTOC UNPAY |
| DOI | 10.15803/ijnc.15.2_138 |
| DatabaseName | CrossRef Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2185-2847 |
| EndPage | 152 |
| ExternalDocumentID | 10.15803/ijnc.15.2_138 10_15803_ijnc_15_2_138 article_ijnc_15_2_15_138_article_char_en |
| GroupedDBID | 7.U ALMA_UNASSIGNED_HOLDINGS JSF JSH KQ8 KWQ OK1 RJT RZJ AAYXX CITATION ISHAI ADTOC UNPAY |
| ID | FETCH-LOGICAL-c2008-dfade559e5a82cefa1cffc9a4f5f55571bac73e99789b20fcfc1910b788fbfad3 |
| IEDL.DBID | UNPAY |
| ISSN | 2185-2839 2185-2847 |
| IngestDate | Sun Sep 07 10:48:35 EDT 2025 Wed Oct 01 05:46:01 EDT 2025 Wed Sep 03 06:30:38 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2008-dfade559e5a82cefa1cffc9a4f5f55571bac73e99789b20fcfc1910b788fbfad3 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://doi.org/10.15803/ijnc.15.2_138 |
| PageCount | 15 |
| ParticipantIDs | unpaywall_primary_10_15803_ijnc_15_2_138 crossref_primary_10_15803_ijnc_15_2_138 jstage_primary_article_ijnc_15_2_15_138_article_char_en |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2025 2025-00-00 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – year: 2025 text: 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | International Journal of Networking and Computing |
| PublicationTitleAlternate | IJNC |
| PublicationYear | 2025 |
| Publisher | IJNC Editorial Committee |
| Publisher_xml | – name: IJNC Editorial Committee |
| References | [12] S. Song, F. Yu, X. Jiang, J. Zhu, W. Cheng, and X. Fang. Loop closure detection of visual SLAM based on variationalautoencoder. Frontiers in Neurorobotics, 17, 2024. doi: 10.3389/fnbot.2023.1301785. [5] A. Kendall, J. Hawke, D. Janz, P. Mazur, D. Reda, J.-M. Allen, V.-D. Lam,A. Bewley, and A. Shah. Learning to drive in a day. In 2019 International Conference on Robotics and Automation(ICRA), pages 8248–8254, 2019. [7] Y. Uehara and S. Matsumae. Dimensionality reduction methods using VAE for deep reinforcementlearning of autonomous driving. In International Workshop on Advances in Networking andComputing (WANC), CANDARW, 2023. [13] T. Zhang, Y. Yang, and A. Zhang. 3D reconstruction of porous media using a batch normalizedvariational auto-encoder. Computational Geosciences, 26:1261–1278, 2022. doi: 10.1007/s10596-022-10159-1. [6] A. Gupta, A. S. Khwaja, A. Anpalagan, L. Guan, and B. Venkatesh. Policy-gradient and actor-critic based state representation learningfor safe driving of autonomous vehicles. Sensors, 20(21):5991, 2020. [14] T. Kramer. OpenAI Gym environments for Donkey Car [source code]. https://github.com/tawnkramer/gym-donkeycar.git, 2018. [8] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. [9] J. Hu, L. Shen, and G. Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision andPattern Recognition (CVPR), 2018. [15] A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann. Stable-baselines3: Reliable reinforcement learning implementations. The Journal of Machine Learning Research, 22(1):12348–12355,2021. [3] B. Balaji, S. Mallya, S. Genc, S. Gupta, L. Dirac, et al. DeepRacer: Autonomous racing platform for experimentation with Sim2Real reinforcement learning. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 2746–2754, Paris, France, 2020. doi: 10.1109/ICRA40945.2020.9197465. [1] Q. Zhang, T. Du, and C. Tian. Self-driving scale car trained by deep reinforcement learning. arXiv preprint arXiv:1909.03467, 2019. [10] Y. Uehara and S. Matsumae. Effect of integrating variational autoencoder with SENet ondimensionality reduction in driving behavior learning. In International Workshop on Advances in Networking andComputing (WANC), CANDARW, 2024. [2] S. Wang, D. Jia, and X. Weng. Deep reinforcement learning for autonomous driving. arXiv preprint arXiv:1811.11329, 2018. [11] Donkey Car – Home. https://www.donkeycar.com/. [4] D. P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. 11 12 13 14 15 1 2 3 4 5 6 7 8 9 10 |
| References_xml | – reference: [3] B. Balaji, S. Mallya, S. Genc, S. Gupta, L. Dirac, et al. DeepRacer: Autonomous racing platform for experimentation with Sim2Real reinforcement learning. In 2020 IEEE International Conference on Robotics and Automation (ICRA), pages 2746–2754, Paris, France, 2020. doi: 10.1109/ICRA40945.2020.9197465. – reference: [9] J. Hu, L. Shen, and G. Sun. Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision andPattern Recognition (CVPR), 2018. – reference: [15] A. Raffin, A. Hill, A. Gleave, A. Kanervisto, M. Ernestus, and N. Dormann. Stable-baselines3: Reliable reinforcement learning implementations. The Journal of Machine Learning Research, 22(1):12348–12355,2021. – reference: [10] Y. Uehara and S. Matsumae. Effect of integrating variational autoencoder with SENet ondimensionality reduction in driving behavior learning. In International Workshop on Advances in Networking andComputing (WANC), CANDARW, 2024. – reference: [7] Y. Uehara and S. Matsumae. Dimensionality reduction methods using VAE for deep reinforcementlearning of autonomous driving. In International Workshop on Advances in Networking andComputing (WANC), CANDARW, 2023. – reference: [8] J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017. – reference: [4] D. P. Kingma and M. Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013. – reference: [2] S. Wang, D. Jia, and X. Weng. Deep reinforcement learning for autonomous driving. arXiv preprint arXiv:1811.11329, 2018. – reference: [6] A. Gupta, A. S. Khwaja, A. Anpalagan, L. Guan, and B. Venkatesh. Policy-gradient and actor-critic based state representation learningfor safe driving of autonomous vehicles. Sensors, 20(21):5991, 2020. – reference: [13] T. Zhang, Y. Yang, and A. Zhang. 3D reconstruction of porous media using a batch normalizedvariational auto-encoder. Computational Geosciences, 26:1261–1278, 2022. doi: 10.1007/s10596-022-10159-1. – reference: [5] A. Kendall, J. Hawke, D. Janz, P. Mazur, D. Reda, J.-M. Allen, V.-D. Lam,A. Bewley, and A. Shah. Learning to drive in a day. In 2019 International Conference on Robotics and Automation(ICRA), pages 8248–8254, 2019. – reference: [11] Donkey Car – Home. https://www.donkeycar.com/. – reference: [14] T. Kramer. OpenAI Gym environments for Donkey Car [source code]. https://github.com/tawnkramer/gym-donkeycar.git, 2018. – reference: [12] S. Song, F. Yu, X. Jiang, J. Zhu, W. Cheng, and X. Fang. Loop closure detection of visual SLAM based on variationalautoencoder. Frontiers in Neurorobotics, 17, 2024. doi: 10.3389/fnbot.2023.1301785. – reference: [1] Q. Zhang, T. Du, and C. Tian. Self-driving scale car trained by deep reinforcement learning. arXiv preprint arXiv:1909.03467, 2019. – ident: 2 – ident: 13 doi: 10.1007/s10596-022-10159-1 – ident: 4 – ident: 1 – ident: 11 – ident: 9 doi: 10.1109/CVPR.2018.00745 – ident: 10 doi: 10.1109/CANDARW64572.2024.00069 – ident: 3 doi: 10.1109/ICRA40945.2020.9197465 – ident: 14 – ident: 15 – ident: 5 doi: 10.1109/ICRA.2019.8793742 – ident: 7 doi: 10.1109/CANDARW60564.2023.00064 – ident: 12 doi: 10.3389/fnbot.2023.1301785 – ident: 8 – ident: 6 doi: 10.3390/s20215991 |
| SSID | ssj0000779023 |
| Score | 2.2809634 |
| Snippet | This study addresses a common limitation of conventional Variational Autoencoder (VAE)-based methods in dimensionality reduction for state representation... |
| SourceID | unpaywall crossref jstage |
| SourceType | Open Access Repository Index Database Publisher |
| StartPage | 138 |
| SubjectTerms | Autonomous driving Deep reinforcement learning Dimensionality reduction Squeeze-and-excitation network Variational autoencoder |
| Title | Enhancing Dimensionality Reduction in Driving Behavior Learning: Integrating SENet with VAE |
| URI | https://www.jstage.jst.go.jp/article/ijnc/15/2/15_138/_article/-char/en https://doi.org/10.15803/ijnc.15.2_138 |
| UnpaywallVersion | publishedVersion |
| Volume | 15 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | International Journal of Networking and Computing, 2025, Vol.15(2), pp.138-152 |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 2185-2847 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0000779023 issn: 2185-2847 databaseCode: KQ8 dateStart: 20110101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3dT8IwEL8ImPjktxGjpg8m-jIc29ptvhEFUSPxC4PxYWlLiyCZhkCM_vX2WCGoD_jWpLe2ufbS-3V3vwM4oAyBtGSOFwnmBAwfmhgLnNCTTPsGFHnjIJrrBqs3g8sWbdkAWcyFmf1_TyPXP-72jJ2UaclLyn6Ug4IZjbl5KDQbN5UnrBxnLhwz0bhkmG0HoWVn_DvAj9tnsWccMMysXxql7_zzg_f7M3dLbQXOJ6vKQkpeS6OhKMmvX4SN85e9CsvWvSSV7DyswYJK12FlUrqBWEvegOdq-oJMG2mHnCG_f8bNYTxycodcrrhbpJuSs0EXHxyIZVEcEEvH2jkhF5ZnArvvqw01JPikSx4r1U1o1qoPp3XH1llw5Dj6oa15WxlkoSiPPKk0L0utZcwDTTWlNCwLLkNfxQZwxsJztdTSoDxXGPSshfnW34J8-paqbSBt5TLOYh-9skBwzY0LxyMeqEjEEXNFEQ4n-k_eMzqNBGEI6ixBnZlmMtZZEcJse6Zy1pRmxShKTjswT80YexGOpvs5Z5Kd_4vuQn44GKk944IMxT7krm6jfXsKvwEo19tf |
| linkProvider | Unpaywall |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3dS8MwEA-6CT45P3GikgdBXzr7lbT1bbjOKThEnUx8KEmWzM1Rx-gQ_evNtdmY-jDfArkm4ZIj90vvfofQCaEApAW13JBTy6fw0ESpbwWuoMrToMjNg2hu27TV8W-6pGsCZCEXZvH_PQlt73ww1HbikJqbOF64isp6NGqXULnTvqs_Q-U4feHoifKSYabtB4ad8e8AP26ftaF2wCCzfn2ajtnnBxuNFu6WZgVdzVZVhJS81aYZr4mvX4SNy5e9iTaMe4nrxXnYQisy3UaVWekGbCx5B73E6SswbaR93AB-_4KbQ3vk-B64XGG38CDFjckAHhywYVGcYEPH2r_A14ZnArof4rbMMDzp4qd6vIs6zfjxsmWZOguWyKMfeor1pEYWkrDQFVIxRyglIuYrogghgcOZCDwZacAZcddWQgmN8myu0bPi-ltvD5XS91TuI9yTNmU08sAr8zlTTLtwLGS-DHkUUptX0elM_8m4oNNIAIaAzhLQmW4muc6qKCi2Zy5nTGlRjIDkvAPy1LSxV9HZfD-XTHLwf9FDVMomU3mkXZCMH5vz9w0KDdpq |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Enhancing+Dimensionality+Reduction+in+Driving+Behavior+Learning%3A+Integrating+SENet+with+VAE&rft.jtitle=International+Journal+of+Networking+and+Computing&rft.au=Susumu+Matsumae&rft.au=Yuta+Uehara&rft.date=2025&rft.pub=IJNC+Editorial+Committee&rft.issn=2185-2839&rft.eissn=2185-2847&rft.volume=15&rft.issue=2&rft.spage=138&rft.epage=152&rft_id=info:doi/10.15803%2Fijnc.15.2_138&rft.externalDocID=article_ijnc_15_2_15_138_article_char_en |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2185-2839&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2185-2839&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2185-2839&client=summon |