Publication
2024
Ye Tao, Hongyi Wu, Ehsan Javanmardi, Manabu Tsukada, Hiroshi Esaki, "Zero-Knowledge Proof of Distinct Identity: a Standard-compatible Sybil-resistant Pseudonym Extension for C-ITS", In: 35th IEEE Intelligent Vehicles Symposium (IV2024), Jeju Island, Korea, 2024.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Tao2024b,
title = {Zero-Knowledge Proof of Distinct Identity: a Standard-compatible Sybil-resistant Pseudonym Extension for C-ITS},
author = {Ye Tao and Hongyi Wu and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2403.14020},
year = {2024},
date = {2024-06-02},
booktitle = {35th IEEE Intelligent Vehicles Symposium (IV2024)},
address = {Jeju Island, Korea},
abstract = {Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Pseudonyms are widely used in Cooperative Intelligent Transport Systems (C-ITS) to protect the location privacy of vehicles. However, the unlinkability nature of pseudonyms also enables Sybil attacks, where a malicious vehicle can pretend to be multiple vehicles at the same time. In this paper, we propose a novel protocol called zero-knowledge Proof of Distinct Identity (zk-PoDI,) which allows a vehicle to prove that it is not the owner of another pseudonym in the local area, without revealing its actual identity. Zk-PoDI is based on the Diophantine equation and zk-SNARK, and does not rely on any specific pseudonym design or infrastructure assistance. We show that zk-PoDI satisfies all the requirements for a practical Sybil-resistance pseudonym system, and it has low latency, adjustable difficulty, moderate computation overhead, and negligible communication cost. We also discuss the future work of implementing and evaluating zk-PoDI in a realistic city-scale simulation environment.
Ye Tao , "Let the Truth Tell: Zero-Knowledge Proof Mechanisms to Realize Fact-Based Cooperative ITS", Ph.D Thesis, The University of Tokyo (Esaki Lab), 2024.PhD Thesis | Abstract | BibTeX
@phdthesis{Tao2024,
title = {Let the Truth Tell: Zero-Knowledge Proof Mechanisms to Realize Fact-Based Cooperative ITS},
author = {Ye Tao },
year = {2024},
date = {2024-03-31},
school = {Ph.D Thesis, The University of Tokyo (Esaki Lab)},
abstract = {The Cooperative Intelligent Transportation System (C-ITS) represents a communication-based technological framework designed to facilitate the ex- change of information among vehicles. This exchange encompasses details pertaining to their presence, observations, and intentions, thereby contribut- ing to the realization of shared objectives, primarily safety and efficiency. Functioning as a distributed and ad-hoc system, C-ITS relies on both direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication protocols.
Given its dynamic topology and intermittent global connectivity, C-ITS is susceptible to various cybersecurity threats. Threats such as data ma- nipulation, the creation of ghost vehicles, and the abuse of pseudonymous identities pose significant challenges. These threats can potentially lead to erroneous decision-making by the affected entities.
Misbehavior detection (MBD) is an endeavor to detect and mitigate cyber-attacks in C-ITS. MBD is categorized into two types: data-centric approaches leverage plausibility and consistency to identify potential cyber threats; node-based approaches rely on the past behavior and reputation of a vehicle to assess its likelihood of engaging in dishonest activities. However, both approaches exhibit limitations. Data-centric approaches are constrained to detecting predefined misbehaviors and are susceptible to false positives and negatives. On the other hand, node-centric approaches suffer from high latency in establishing and updating trust assessments, including blocking ongoing attacks.
In contrast, emerging technologies such as blockchain and cryptocurren- cies establish consensus through verifiable facts and employ game theory to discourage dishonest behavior. These fact-based approaches differ markedly from traditional C-ITS methods, which often assume a prevailing honesty among individuals. Demonstrating efficacy across various industries, includ- ing healthcare, education, logistics, and government, fact-based approaches exhibit notable potential for enhancing C-ITS. Several works proposed in this dissertation leverage these fact-based methodologies.
In chapter 3, we introduced Flowsim, a novel simulator tailored for eval- uating Connected and Autonomous Vehicles (CAV) behavior and data flow in large-scale scenarios. The primary objective of Flowsim is to expedite the development and assessment of cybersecurity measures and protocols for CAVs, ensuring their safe and secure deployment. Beyond its focus on CAV evaluation, Flowsim demonstrates versatility in various use cases, including protocol evaluation, cybersecurity assessment, traffic optimization, and pol- icy development. The simulator’s extensible and playable open environment encourages the exploration of innovative algorithms and strategies for CAVs, fostering collaboration and innovation in research communities. Flowsim shows its significance in addressing the limitations of existing simulators for CAV behavior evaluation, specifically for its modularity, extensibility, and performance.
In chapter 4, we introduced a novel deterministic cross-verification method named zero-knowledge Proof of Traffic (zk-PoT) within the context of cooper- ative perception. The zk-PoT protocol utilizes zero-knowledge proofs (ZKPs) to enable vehicles to independently prove their observations’ existence, allow- ing remote parties to cross-verify these observations without relying on the ground truth. The zero-knowledge property ensures that no additional in- formation is disclosed during this process, thereby safeguarding the location privacy of observed vehicles. Integration into existing cooperative perception standards, such as ISO and ETSI, requires minimal architectural changes and ensures backward compatibility. The study concludes with quantitative simu- lation analyses based on Flowsim in the scenario of a full-scaled Luxemburg city, demonstrating zk-PoT’s high success rate, low decision latency, and moderate resource assumption. Threat analyses indicate resilience against common attacks, making it a valuable standalone method or complementary to existing standards and trust management models.
In chapter 5, we addressed the challenge of a type of Sybil attacks in C-ITS, caused by simultaneous usage of pseudonyms. We introduced a novel cryptographic protocol called zero-knowledge Proof of Distinct Identity (zk- PoDI). Based on the ”locality assumption,” wherein a vehicle need only prove its distinction from nearby vehicles rather than every vehicle globally, our proposed protocol zk-PoDI ensures that only a vehicle itself can prove it is not the owner of a given pseudonym locally, preventing Sybil attacks while pre- serving pseudonym unlinkability. The protocol’s practicality is highlighted by its zero latency, moderate computation overhead, and negligible commu- nication costs. zk-PoDI operates independently, without reliance on specific pseudonym system designs or additional knowledge. This feature together with the other practicality makes zk-PoDI ready to be integrated into C-ITS systems with any possible pseudonym designs.
Looking ahead, the exploration of fact-based approaches in C-ITS is poised to continue. Empowered by innovations like zero-knowledge proofs and other cutting-edge techniques, we can anticipate the resolution of nu- merous challenges, the validation of truth, and the safeguarding of privacy within this domain.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
The Cooperative Intelligent Transportation System (C-ITS) represents a communication-based technological framework designed to facilitate the ex- change of information among vehicles. This exchange encompasses details pertaining to their presence, observations, and intentions, thereby contribut- ing to the realization of shared objectives, primarily safety and efficiency. Functioning as a distributed and ad-hoc system, C-ITS relies on both direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication protocols.
Given its dynamic topology and intermittent global connectivity, C-ITS is susceptible to various cybersecurity threats. Threats such as data ma- nipulation, the creation of ghost vehicles, and the abuse of pseudonymous identities pose significant challenges. These threats can potentially lead to erroneous decision-making by the affected entities.
Misbehavior detection (MBD) is an endeavor to detect and mitigate cyber-attacks in C-ITS. MBD is categorized into two types: data-centric approaches leverage plausibility and consistency to identify potential cyber threats; node-based approaches rely on the past behavior and reputation of a vehicle to assess its likelihood of engaging in dishonest activities. However, both approaches exhibit limitations. Data-centric approaches are constrained to detecting predefined misbehaviors and are susceptible to false positives and negatives. On the other hand, node-centric approaches suffer from high latency in establishing and updating trust assessments, including blocking ongoing attacks.
In contrast, emerging technologies such as blockchain and cryptocurren- cies establish consensus through verifiable facts and employ game theory to discourage dishonest behavior. These fact-based approaches differ markedly from traditional C-ITS methods, which often assume a prevailing honesty among individuals. Demonstrating efficacy across various industries, includ- ing healthcare, education, logistics, and government, fact-based approaches exhibit notable potential for enhancing C-ITS. Several works proposed in this dissertation leverage these fact-based methodologies.
In chapter 3, we introduced Flowsim, a novel simulator tailored for eval- uating Connected and Autonomous Vehicles (CAV) behavior and data flow in large-scale scenarios. The primary objective of Flowsim is to expedite the development and assessment of cybersecurity measures and protocols for CAVs, ensuring their safe and secure deployment. Beyond its focus on CAV evaluation, Flowsim demonstrates versatility in various use cases, including protocol evaluation, cybersecurity assessment, traffic optimization, and pol- icy development. The simulator’s extensible and playable open environment encourages the exploration of innovative algorithms and strategies for CAVs, fostering collaboration and innovation in research communities. Flowsim shows its significance in addressing the limitations of existing simulators for CAV behavior evaluation, specifically for its modularity, extensibility, and performance.
In chapter 4, we introduced a novel deterministic cross-verification method named zero-knowledge Proof of Traffic (zk-PoT) within the context of cooper- ative perception. The zk-PoT protocol utilizes zero-knowledge proofs (ZKPs) to enable vehicles to independently prove their observations’ existence, allow- ing remote parties to cross-verify these observations without relying on the ground truth. The zero-knowledge property ensures that no additional in- formation is disclosed during this process, thereby safeguarding the location privacy of observed vehicles. Integration into existing cooperative perception standards, such as ISO and ETSI, requires minimal architectural changes and ensures backward compatibility. The study concludes with quantitative simu- lation analyses based on Flowsim in the scenario of a full-scaled Luxemburg city, demonstrating zk-PoT’s high success rate, low decision latency, and moderate resource assumption. Threat analyses indicate resilience against common attacks, making it a valuable standalone method or complementary to existing standards and trust management models.
In chapter 5, we addressed the challenge of a type of Sybil attacks in C-ITS, caused by simultaneous usage of pseudonyms. We introduced a novel cryptographic protocol called zero-knowledge Proof of Distinct Identity (zk- PoDI). Based on the ”locality assumption,” wherein a vehicle need only prove its distinction from nearby vehicles rather than every vehicle globally, our proposed protocol zk-PoDI ensures that only a vehicle itself can prove it is not the owner of a given pseudonym locally, preventing Sybil attacks while pre- serving pseudonym unlinkability. The protocol’s practicality is highlighted by its zero latency, moderate computation overhead, and negligible commu- nication costs. zk-PoDI operates independently, without reliance on specific pseudonym system designs or additional knowledge. This feature together with the other practicality makes zk-PoDI ready to be integrated into C-ITS systems with any possible pseudonym designs.
Looking ahead, the exploration of fact-based approaches in C-ITS is poised to continue. Empowered by innovations like zero-knowledge proofs and other cutting-edge techniques, we can anticipate the resolution of nu- merous challenges, the validation of truth, and the safeguarding of privacy within this domain.
Given its dynamic topology and intermittent global connectivity, C-ITS is susceptible to various cybersecurity threats. Threats such as data ma- nipulation, the creation of ghost vehicles, and the abuse of pseudonymous identities pose significant challenges. These threats can potentially lead to erroneous decision-making by the affected entities.
Misbehavior detection (MBD) is an endeavor to detect and mitigate cyber-attacks in C-ITS. MBD is categorized into two types: data-centric approaches leverage plausibility and consistency to identify potential cyber threats; node-based approaches rely on the past behavior and reputation of a vehicle to assess its likelihood of engaging in dishonest activities. However, both approaches exhibit limitations. Data-centric approaches are constrained to detecting predefined misbehaviors and are susceptible to false positives and negatives. On the other hand, node-centric approaches suffer from high latency in establishing and updating trust assessments, including blocking ongoing attacks.
In contrast, emerging technologies such as blockchain and cryptocurren- cies establish consensus through verifiable facts and employ game theory to discourage dishonest behavior. These fact-based approaches differ markedly from traditional C-ITS methods, which often assume a prevailing honesty among individuals. Demonstrating efficacy across various industries, includ- ing healthcare, education, logistics, and government, fact-based approaches exhibit notable potential for enhancing C-ITS. Several works proposed in this dissertation leverage these fact-based methodologies.
In chapter 3, we introduced Flowsim, a novel simulator tailored for eval- uating Connected and Autonomous Vehicles (CAV) behavior and data flow in large-scale scenarios. The primary objective of Flowsim is to expedite the development and assessment of cybersecurity measures and protocols for CAVs, ensuring their safe and secure deployment. Beyond its focus on CAV evaluation, Flowsim demonstrates versatility in various use cases, including protocol evaluation, cybersecurity assessment, traffic optimization, and pol- icy development. The simulator’s extensible and playable open environment encourages the exploration of innovative algorithms and strategies for CAVs, fostering collaboration and innovation in research communities. Flowsim shows its significance in addressing the limitations of existing simulators for CAV behavior evaluation, specifically for its modularity, extensibility, and performance.
In chapter 4, we introduced a novel deterministic cross-verification method named zero-knowledge Proof of Traffic (zk-PoT) within the context of cooper- ative perception. The zk-PoT protocol utilizes zero-knowledge proofs (ZKPs) to enable vehicles to independently prove their observations’ existence, allow- ing remote parties to cross-verify these observations without relying on the ground truth. The zero-knowledge property ensures that no additional in- formation is disclosed during this process, thereby safeguarding the location privacy of observed vehicles. Integration into existing cooperative perception standards, such as ISO and ETSI, requires minimal architectural changes and ensures backward compatibility. The study concludes with quantitative simu- lation analyses based on Flowsim in the scenario of a full-scaled Luxemburg city, demonstrating zk-PoT’s high success rate, low decision latency, and moderate resource assumption. Threat analyses indicate resilience against common attacks, making it a valuable standalone method or complementary to existing standards and trust management models.
In chapter 5, we addressed the challenge of a type of Sybil attacks in C-ITS, caused by simultaneous usage of pseudonyms. We introduced a novel cryptographic protocol called zero-knowledge Proof of Distinct Identity (zk- PoDI). Based on the ”locality assumption,” wherein a vehicle need only prove its distinction from nearby vehicles rather than every vehicle globally, our proposed protocol zk-PoDI ensures that only a vehicle itself can prove it is not the owner of a given pseudonym locally, preventing Sybil attacks while pre- serving pseudonym unlinkability. The protocol’s practicality is highlighted by its zero latency, moderate computation overhead, and negligible commu- nication costs. zk-PoDI operates independently, without reliance on specific pseudonym system designs or additional knowledge. This feature together with the other practicality makes zk-PoDI ready to be integrated into C-ITS systems with any possible pseudonym designs.
Looking ahead, the exploration of fact-based approaches in C-ITS is poised to continue. Empowered by innovations like zero-knowledge proofs and other cutting-edge techniques, we can anticipate the resolution of nu- merous challenges, the validation of truth, and the safeguarding of privacy within this domain.
2023
Ye Tao, Ehsan Javanmardi, Pengfei Lin, Yuze Jiang, Jin Nakazato, Manabu Tsukada, Hiroshi Esaki, "Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data", In: IEEE Access, vol. 11, pp. 142846-142861, 2023, ISSN: 2169-3536.Journal Article | Abstract | BibTeX | Links:
@article{Tao2023b,
title = {Zero-Knowledge Proof of Traffic: A Deterministic and Privacy-Preserving Cross Verification Mechanism for Cooperative Perception Data},
author = {Ye Tao and Ehsan Javanmardi and Pengfei Lin and Yuze Jiang and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2312.07948},
doi = {10.1109/ACCESS.2023.3343405},
issn = {2169-3536},
year = {2023},
date = {2023-12-17},
urldate = {2023-12-17},
journal = {IEEE Access},
volume = {11},
pages = {142846-142861},
abstract = {Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT’s cross-verification ratio ranges between 80 % to 96 %, and 90 % of the verification is achieved in 5 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Cooperative perception is crucial for connected automated vehicles in intelligent transportation systems (ITSs); however, ensuring the authenticity of perception data remains a challenge as the vehicles cannot verify events that they do not witness independently. Various studies have been conducted on establishing the authenticity of data, such as trust-based statistical methods and plausibility-based methods. However, these methods are limited as they require prior knowledge such as previous sender behaviors or predefined rules to evaluate the authenticity. To overcome this limitation, this study proposes a novel approach called zero-knowledge Proof of Traffic (zk-PoT), which involves generating cryptographic proofs to the traffic observations. Multiple independent proofs regarding the same vehicle can be deterministically cross-verified by any receivers without relying on ground truth, probabilistic, or plausibility evaluations. Additionally, no private information is compromised during the entire procedure. A full on-board unit software stack that reflects the behavior of zk-PoT is implemented within a specifically designed simulator called Flowsim. A comprehensive experimental analysis is then conducted using synthesized city-scale simulations, which demonstrates that zk-PoT’s cross-verification ratio ranges between 80 % to 96 %, and 90 % of the verification is achieved in 5 s, with a protocol overhead of approximately 25 %. Furthermore, the analyses of various attacks indicate that most of the attacks could be prevented, and some, such as collusion attacks, can be mitigated. The proposed approach can be incorporated into existing works, including the European Telecommunications Standards Institute (ETSI) and the International Organization for Standardization (ISO) ITS standards, without disrupting the backward compatibility.
Ye Tao, Ehsan Javanmardi, Jin Nakazato, Manabu Tsukada, Hiroshi Esaki, "Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles", 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{Tao2023c,
title = {Flowsim: A Modular Simulation Platform for Microscopic Behavior Analysis of City-Scale Connected Autonomous Vehicles},
author = {Ye Tao and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},
url = {https://arxiv.org/abs/2306.05738},
doi = {10.1109/ITSC57777.2023.10421900},
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 = {As connected autonomous vehicles (CAVs) become increasingly prevalent, there is a growing need for simulation platforms that can accurately evaluate CAV behavior in large-scale environments. In this paper, we propose Flowsim, a novel simulator specifically designed to meet these requirements. Flowsim offers a modular and extensible architecture that enables the analysis of CAV behaviors in large-scale scenarios. It provides researchers with a customizable platform for studying CAV interactions, evaluating communication and networking protocols, assessing cybersecurity vulnerabilities, optimizing traffic management strategies, and developing and evaluating policies for CAV deployment. Flowsim is implemented in pure Python in approximately 1,500 lines of code, making it highly readable, understandable, and easily modifiable. We verified the functionality and performance of Flowsim via a series of experiments based on realistic traffic scenarios. The results show the effectiveness of Flowsim in providing a flexible and powerful simulation environment for evaluating CAV behavior and data flow. Flowsim is a valuable tool for researchers, policymakers, and industry professionals who are involved in the development, evaluation, and deployment of CAVs. The code of Flowsim is publicly available on GitHub under the MIT license. },
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
As connected autonomous vehicles (CAVs) become increasingly prevalent, there is a growing need for simulation platforms that can accurately evaluate CAV behavior in large-scale environments. In this paper, we propose Flowsim, a novel simulator specifically designed to meet these requirements. Flowsim offers a modular and extensible architecture that enables the analysis of CAV behaviors in large-scale scenarios. It provides researchers with a customizable platform for studying CAV interactions, evaluating communication and networking protocols, assessing cybersecurity vulnerabilities, optimizing traffic management strategies, and developing and evaluating policies for CAV deployment. Flowsim is implemented in pure Python in approximately 1,500 lines of code, making it highly readable, understandable, and easily modifiable. We verified the functionality and performance of Flowsim via a series of experiments based on realistic traffic scenarios. The results show the effectiveness of Flowsim in providing a flexible and powerful simulation environment for evaluating CAV behavior and data flow. Flowsim is a valuable tool for researchers, policymakers, and industry professionals who are involved in the development, evaluation, and deployment of CAVs. The code of Flowsim is publicly available on GitHub under the MIT license.
Pengfei Lin, Ehsan Javanmardi, Ye Tao, Vishal Chauhan, Jin Nakazato, Manabu Tsukada, "Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles", In: 2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023), Anchorage, Alaska, USA, 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Lin2023,
title = {Time-To-Collision-Aware Lane-Change Strategy Based on Potential Field and Cubic Polynomial for Autonomous Vehicles},
author = {Pengfei Lin and Ehsan Javanmardi and Ye Tao and Vishal Chauhan and Jin Nakazato and Manabu Tsukada},
url = {https://arxiv.org/abs/2306.06981},
year = {2023},
date = {2023-06-04},
urldate = {2023-06-04},
booktitle = {2023 IEEE Intelligent Vehicles Symposium (IEEE IV 2023)},
address = {Anchorage, Alaska, USA},
abstract = {Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.
},
keywords = {},
pubstate = {published},
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}
Making safe and successful lane changes (LCs) is one of the many vitally important functions of autonomous vehicles (AVs) that are needed to ensure safe driving on expressways. Recently, the simplicity and real-time performance of the potential field (PF) method have been leveraged to design decision and planning modules for AVs. However, the LC trajectory planned by the PF method is usually lengthy and takes the ego vehicle laterally parallel and close to the obstacle vehicle, which creates a dangerous situation if the obstacle vehicle suddenly steers. To mitigate this risk, we propose a time-to-collision-aware LC (TTCA-LC) strategy based on the PF and cubic polynomial in which the TTC constraint is imposed in the optimized curve fitting. The proposed approach is evaluated using MATLAB/Simulink under high-speed conditions in a comparative driving scenario. The simulation results indicate that the TTCA-LC method performs better than the conventional PF-based LC (CPF-LC) method in generating shorter, safer, and smoother trajectories. The length of the LC trajectory is shortened by over 27.1%, and the curvature is reduced by approximately 56.1% compared with the CPF-LC method.
Ye Tao, Yuze Jiang, Pengfei Lin, Manabu Tsukada, Hiroshi Esaki, "zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception", In: 2023 IEEE 20th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2023.Proceedings Article | Abstract | BibTeX | Links:
@inproceedings{Tao2023,
title = {zk-PoT: Zero-Knowledge Proof of Traffic for Privacy Enabled Cooperative Perception},
author = {Ye Tao and Yuze Jiang and Pengfei Lin and Manabu Tsukada and Hiroshi Esaki},
url = {http://arxiv.org/abs/2211.07875},
doi = {10.1109/CCNC51644.2023.10059601},
year = {2023},
date = {2023-01-08},
urldate = {2023-01-08},
booktitle = {2023 IEEE 20th Annual Consumer Communications & Networking Conference (CCNC)},
address = {Las Vegas, NV, USA},
abstract = {Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70% to 80% at rush hours.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Cooperative perception is an essential and widely discussed application of connected automated vehicles. However, the authenticity of perception data is not ensured, because the vehicles cannot independently verify the event they did not see. Many methods, including trust-based (i.e., statistical) approaches and plausibility-based methods, have been proposed to determine data authenticity. However, these methods cannot verify data without a priori knowledge. In this study, a novel approach of constructing a self-proving data from the number plate of target vehicles was proposed. By regarding the pseudonym and number plate as a shared secret and letting multiple vehicles prove they know it independently, the data authenticity problem can be transformed to a cryptography problem that can be solved without trust or plausibility evaluations. Our work can be adapted to the existing works including ETSI/ISO ITS standards while maintaining backward compatibility. Analyses of common attacks and attacks specific to the proposed method reveal that most attacks can be prevented, whereas preventing some other attacks, such as collusion attacks, can be mitigated. Experiments based on realistic data set show that the rate of successful verification can achieve 70% to 80% at rush hours.
2017
Ye Tao, Manabu Tsukada, Hiroshi Esaki, "Positioning and Perception in cooperative ITS application simulator", In: The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017), pp. 54-59, Nice, France, 2017.Proceedings Article | BibTeX | Links:
@inproceedings{Tao2017b,
title = {Positioning and Perception in cooperative ITS application simulator},
author = {Ye Tao and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01879102/document?.pdf},
year = {2017},
date = {2017-07-20},
booktitle = {The Sixth International Conference on Advances in Vehicular Systems, Technologies and Applications (VEHICULAR 2017)},
pages = {54-59},
address = {Nice, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ye Tao, Xin Li, Manabu Tsukada, Hiroshi Esaki, "Reliable Overlay Networking on ETSI GeoNetworking Standards", In: International Journal of Intelligent Transportation Systems Research, vol. 16, no. 2, pp. 98-111, 2017, ISBN: 1348-8503.Journal Article | BibTeX | Links:
@article{Tao2017,
title = {Reliable Overlay Networking on ETSI GeoNetworking Standards},
author = {Ye Tao and Xin Li and Manabu Tsukada and Hiroshi Esaki},
url = {http://rdcu.be/qyX5},
doi = {https://doi.org/10.1007/s13177-017-0141-7},
isbn = {1348-8503},
year = {2017},
date = {2017-04-20},
journal = {International Journal of Intelligent Transportation Systems Research},
volume = {16},
number = {2},
pages = {98-111},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2016
Ye Tao, Xin Li, Manabu Tsukada, Hiroshi Esaki, "DUPE: Duplicated Unicast Packet Encapsulation in Position-Based Routing VANET", In: 9th IFIP Wireless and Mobile Networking Conference (WMNC 2016), Colmar, France, 2016.Proceedings Article | BibTeX | Links:
@inproceedings{Tao2016,
title = {DUPE: Duplicated Unicast Packet Encapsulation in Position-Based Routing VANET},
author = {Ye Tao and Xin Li and Manabu Tsukada and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01321553/document?.pdf},
doi = {10.1109/WMNC.2016.7543979},
year = {2016},
date = {2016-07-20},
booktitle = {9th IFIP Wireless and Mobile Networking Conference (WMNC 2016)},
address = {Colmar, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Ye Tao, "DUPE: Duplicated Unicasting Packet Encapsulation in Position Based Routing VANET", Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Esaki, Ochiai Lab), 2016.Masters Thesis | BibTeX
@mastersthesis{Ye2016,
title = {DUPE: Duplicated Unicasting Packet Encapsulation in Position Based Routing VANET},
author = {Ye Tao},
year = {2016},
date = {2016-03-31},
urldate = {2016-03-31},
school = {Master Thesis, Graduate School of Information Science and Technology, The University of Tokyo (Esaki, Ochiai Lab)},
keywords = {},
pubstate = {published},
tppubtype = {mastersthesis}
}
2015
Ye Tao, Manabu Tsukada, Xin Li, Masatoshi Kakiuchi, Hiroshi Esaki, "Reproducing and Extending Real Testbed Evaluation of GeoNetworking Implementation in Simulated Networks", In: The 10th International Conference on Future Internet Technologies (CFI 2015), Seoul, Korea, 2015.Proceedings Article | BibTeX | Links:
@inproceedings{Tao2015,
title = {Reproducing and Extending Real Testbed Evaluation of GeoNetworking Implementation in Simulated Networks},
author = {Ye Tao and Manabu Tsukada and Xin Li and Masatoshi Kakiuchi and Hiroshi Esaki},
url = {https://hal.archives-ouvertes.fr/hal-01317104/document?.pdf},
doi = {10.1145/2775088.2775092},
year = {2015},
date = {2015-06-20},
booktitle = {The 10th International Conference on Future Internet Technologies (CFI 2015)},
address = {Seoul, Korea},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}