Publication
2025
松澤力, Naren Bao, Ehsan Javanmardi, 塚田 学 , "V2X通信における占有格子地図の共有とAutowareを用いた検証", マルチメディア，分散，協調とモバイル(DICOMO2025)シンポジウム, 福島、母畑温泉, 2025.Conference | BibTeX
@conference{松澤力2025,
title = {V2X通信における占有格子地図の共有とAutowareを用いた検証},
author = {松澤力 and Naren Bao and Ehsan Javanmardi and 塚田 学 },
year = {2025},
date = {2025-06-25},
booktitle = {マルチメディア，分散，協調とモバイル(DICOMO2025)シンポジウム},
address = {福島、母畑温泉},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
2024
Dongyang Li, Ehsan Javanmardi, Naren Bao, Manabu Tsukada, "Cross-Attention Enhanced Imitation Learning for End-to-end Autonomous Driving in Unprotected Turns", In: International Conference on Intelligent Computing and its Emerging Applications (ICEA2024), Tokyo, Japan, 2024, (Best Paper Award (Silver)).Proceedings Article | Abstract | BibTeX
@inproceedings{Li2024,
title = {Cross-Attention Enhanced Imitation Learning for End-to-end Autonomous Driving in Unprotected Turns},
author = {Dongyang Li and Ehsan Javanmardi and Naren Bao and Manabu Tsukada},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
booktitle = {International Conference on Intelligent Computing and its Emerging Applications (ICEA2024)},
address = {Tokyo, Japan},
abstract = {Performing an unprotected turn in the intersection is a complex scenario for autonomous vehicles. It not only requires a comprehensive understanding of the surrounding environment but also highly relies on the ego vehicle’s current state to make safe decisions. A conventional way to learn end-to-end autonomous driving is imitation learning, which is learning from expert demonstrations. While most imitation learning methods focus on imitating the expert action, they often fail to imitate a complex policy efficiently when the ego vehicle’s states are crucial to the scenario because there might be arbitrary optimal actions under different states. To address this issue and investigate how vehicle states affect autonomous driving, we present a novel cross-attention enhanced imitation learning approach for end-to-end autonomous driving in unprotected turns, focusing on capturing the relationships between the ego vehicle’s states and its perception of the environment. We evaluate our model in AWSIM, an open-source autonomous driving
simulator, and the results demonstrate that our model outperformed conventional imitation learning-based baselines in performing unprotected turn scenarios, showcasing its ability to imitate a complex policy efficiently.},
note = {Best Paper Award (Silver)},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Performing an unprotected turn in the intersection is a complex scenario for autonomous vehicles. It not only requires a comprehensive understanding of the surrounding environment but also highly relies on the ego vehicle’s current state to make safe decisions. A conventional way to learn end-to-end autonomous driving is imitation learning, which is learning from expert demonstrations. While most imitation learning methods focus on imitating the expert action, they often fail to imitate a complex policy efficiently when the ego vehicle’s states are crucial to the scenario because there might be arbitrary optimal actions under different states. To address this issue and investigate how vehicle states affect autonomous driving, we present a novel cross-attention enhanced imitation learning approach for end-to-end autonomous driving in unprotected turns, focusing on capturing the relationships between the ego vehicle’s states and its perception of the environment. We evaluate our model in AWSIM, an open-source autonomous driving
simulator, and the results demonstrate that our model outperformed conventional imitation learning-based baselines in performing unprotected turn scenarios, showcasing its ability to imitate a complex policy efficiently.
simulator, and the results demonstrate that our model outperformed conventional imitation learning-based baselines in performing unprotected turn scenarios, showcasing its ability to imitate a complex policy efficiently.
Muhammad Asad, Saima Shaukat, Ehsan Javanmardi, Jin Nakazato, Naren Bao, Manabu Tsukada, "Secure and Efficient Blockchain-based Federated Learning Approach For VANETs", In: IEEE Internet of Things Journal, vol. 11, iss. 5, pp. 9047-9055, 2024, ISSN: 2327-4662.Journal Article | Abstract | BibTeX | Links:
@article{Asad2024,
title = {Secure and Efficient Blockchain-based Federated Learning Approach For VANETs},
author = {Muhammad Asad and Saima Shaukat and Ehsan Javanmardi and Jin Nakazato and Naren Bao and Manabu Tsukada},
doi = {10.1109/JIOT.2023.3322221},
issn = {2327-4662},
year = {2024},
date = {2024-03-01},
urldate = {2023-10-05},
journal = {IEEE Internet of Things Journal},
volume = {11},
issue = {5},
pages = {9047-9055},
abstract = {The rapid increase in the number of connected vehicles on roads has made Vehicular Ad-hoc Networks (VANETs) an attractive target for malicious actors. As a result, VANETs require secure data transmission to maintain the network’s integrity. Federated Learning (FL) has been proposed as a secure data-sharing method for VANETs, but it is limited in its ability to protect sensitive data. This paper proposes integrating Blockchain technology into FL to provide an additional layer of security for VANETs. In particular, we propose a Secure and Efficient Blockchain-based FL (SEBFL) approach to ensure communication efficiency and data privacy in VANETs. To this end, we use the FL model for VANETs, where computation tasks are decomposed from a base station to individual vehicles. This effectively reduces the congestion delay and communication overhead. Integrating blockchain with the FL model provides a reliable and secure data communication system between vehicles, roadside units, and a cloud server. Additionally, we use a Homomorphic Encryption System (HES) that effectively preserves the confidentiality and credibility of vehicles. Besides, the proposed SEBFL leverages the asynchronous FL model, minimizing the long delay while avoiding possible threats and attacks using HES. The experiment results show the proposed SEBFL achieves 0.87% accuracy while a model inversion attack and 0.86% accuracy while a membership inference attack.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The rapid increase in the number of connected vehicles on roads has made Vehicular Ad-hoc Networks (VANETs) an attractive target for malicious actors. As a result, VANETs require secure data transmission to maintain the network’s integrity. Federated Learning (FL) has been proposed as a secure data-sharing method for VANETs, but it is limited in its ability to protect sensitive data. This paper proposes integrating Blockchain technology into FL to provide an additional layer of security for VANETs. In particular, we propose a Secure and Efficient Blockchain-based FL (SEBFL) approach to ensure communication efficiency and data privacy in VANETs. To this end, we use the FL model for VANETs, where computation tasks are decomposed from a base station to individual vehicles. This effectively reduces the congestion delay and communication overhead. Integrating blockchain with the FL model provides a reliable and secure data communication system between vehicles, roadside units, and a cloud server. Additionally, we use a Homomorphic Encryption System (HES) that effectively preserves the confidentiality and credibility of vehicles. Besides, the proposed SEBFL leverages the asynchronous FL model, minimizing the long delay while avoiding possible threats and attacks using HES. The experiment results show the proposed SEBFL achieves 0.87% accuracy while a model inversion attack and 0.86% accuracy while a membership inference attack.
2023
Naren Bao, Jin Nakazato, Muhammad Asad, Ehsan Javanmardi, Manabu Tsukada, "Towards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification", The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things, Nagoya, Japan, 2023.Workshop | Abstract | BibTeX | Links:
@workshop{Bao2023b,
title = {Towards a Trusted Inter-Reality: Exploring System Architectures for Digital Identification},
author = {Naren Bao and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},
doi = {10.1145/3627050.3631566},
year = {2023},
date = {2023-11-07},
urldate = {2023-11-07},
booktitle = {The 1st International Workshop on Internet of Realities (IoR-WS 2023) at International Conference on the Internet of Things},
address = {Nagoya, Japan},
abstract = {The concept of a trusted inter-reality, where physical and virtual worlds seamlessly converge, represents a paradigm shift in how digital identities are formed and managed. This paper explores the complex landscape of system architectures designed to enable secure and user-centric digital identification within interconnected realities. Our survey focuses on user-centric security, recognizing the prevalence of wearable devices and immersive technologies in inter-reality environments. We advocate for user-friendly authentication methods and privacy-preserving techniques that prioritize user control within the trust model. Furthermore, we delve into the influence of social and cultural factors, particularly age and gender, on the shaping of digital identity within interconnected realities. We argue in favor of adaptable system architectures that respect generational and gender diversity. In conclusion, we emphasize the alignment of system architectures with these principles to promote a secure, user-centric, and culturally sensitive digital identity experience. This research contributes to the ongoing discourse on digital identification in interconnected realities, providing actionable guidance for stakeholders in the evolving landscape of trusted inter-reality.
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
The concept of a trusted inter-reality, where physical and virtual worlds seamlessly converge, represents a paradigm shift in how digital identities are formed and managed. This paper explores the complex landscape of system architectures designed to enable secure and user-centric digital identification within interconnected realities. Our survey focuses on user-centric security, recognizing the prevalence of wearable devices and immersive technologies in inter-reality environments. We advocate for user-friendly authentication methods and privacy-preserving techniques that prioritize user control within the trust model. Furthermore, we delve into the influence of social and cultural factors, particularly age and gender, on the shaping of digital identity within interconnected realities. We argue in favor of adaptable system architectures that respect generational and gender diversity. In conclusion, we emphasize the alignment of system architectures with these principles to promote a secure, user-centric, and culturally sensitive digital identity experience. This research contributes to the ongoing discourse on digital identification in interconnected realities, providing actionable guidance for stakeholders in the evolving landscape of trusted inter-reality.
Naren Bao, Alexander Carballo, Manabu Tsukada, Kazuya Takeda, "Personalized Causal Factor Generalization for Subjective Risky Scene Understanding with Vision Transformer", In: The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023), Bilbao, Bizkaia, Spain, 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Bao2023,
title = {Personalized Causal Factor Generalization for Subjective Risky Scene Understanding with Vision Transformer},
author = {Naren Bao and Alexander Carballo and Manabu Tsukada and Kazuya Takeda},
doi = {10.1109/ITSC57777.2023.10422148},
year = {2023},
date = {2023-09-24},
urldate = {2023-09-24},
booktitle = {The 26th edition of the IEEE International Conference on Intelligent Transportation Systems (ITSC 2023)},
address = {Bilbao, Bizkaia, Spain},
abstract = {This paper presents a framework to understanding subjective driving scene perception by Vision Transformer for Environmental Feature Extraction within a Causal Modeling Analysis method. By leveraging vision transformer models, informative features are extracted from video camera images capturing the surrounding environment. Through the causal analysis, the causal effects of these variables on subjective risk perception are explored, shedding light on the factors influencing individuals' perception of driving risk. The findings demonstrate understanding of environmental features and individual difference on risk perception, providing a deeper understanding of risky scene perception. The paper concludes with this approach unifies selective attentional phenomena can improve the scene understanding for subjective perception in real-world driving scenarios aiming to enhance driving safety based on the identified causal factors. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a framework to understanding subjective driving scene perception by Vision Transformer for Environmental Feature Extraction within a Causal Modeling Analysis method. By leveraging vision transformer models, informative features are extracted from video camera images capturing the surrounding environment. Through the causal analysis, the causal effects of these variables on subjective risk perception are explored, shedding light on the factors influencing individuals' perception of driving risk. The findings demonstrate understanding of environmental features and individual difference on risk perception, providing a deeper understanding of risky scene perception. The paper concludes with this approach unifies selective attentional phenomena can improve the scene understanding for subjective perception in real-world driving scenarios aiming to enhance driving safety based on the identified causal factors.
