Publication

@inproceedings{Jiang2023,

title = {Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles},

author = {Yuze Jiang and Ehsan Javanmard and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},

url = {https://arxiv.org/abs/2311.07913},

year  = {2023},

date = {2023-12-14},

urldate = {2023-12-14},

booktitle = {ACM Intelligent Computing and its Emerging Applications (ICEA 2023)},

abstract = {Advancements in LiDAR technology have led to more cost-effective production while simultaneously improving precision and resolution. As a result, LiDAR has become integral to vehicle localization, achieving centimeter-level accuracy through techniques like Normal Distributions Transform (NDT) and other advanced 3D registration algorithms. Nonetheless, these approaches are reliant on high-definition 3D point cloud maps, the creation of which involves significant expenditure. When such maps are unavailable or lack sufficient features for 3D registration algorithms, localization accuracy diminishes, posing a risk to road safety. To address this, we proposed to use LiDAR-equipped roadside unit and Vehicle-to-Infrastructure (V2I) communication to accurately estimate the connected vehicle's position and help the vehicle when its self-localization is not accurate enough. Our simulation results indicate that this method outperforms traditional NDT scan matching-based approaches in terms of localization accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Matsumoto2023b,

title = {Localizability Estimation for Autonomous Driving: A Deep Learning-Based Place Recognition Approach},

author = {Kazuto Matsumoto and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},

year  = {2023},

date = {2023-12-11},

booktitle = {IEEE Robotic Computing 2023},

address = {California, USA},

abstract = {In recent years, research and development aimed at the societal implementation of autonomous driving have attracted increasing attention. Localization, which involves obtaining in- formation about the surrounding environment from sensor data and estimating the vehicle’s position, is necessary for realizing autonomous driving. Localization is commonly performed with 3D LiDAR as a sensor owing to its high measurement accuracy and immunity to ambient light conditions, which allow for precise localization. However, localization accuracy may decrease when the surrounding area does not have distinctive features. In this study, we proposed a method based on deep learning to estimate localization accuracy for autonomous driving. The overall localization accuracy can be improved by estimating the accuracy of localization using other sensors, such as GNSS and IMU, or pavement markings in areas with poor accuracy. We created a dataset for estimating localization accuracy using an open-source autonomous driving simulator. In an experiment, we applied the proposed method to the created dataset. Estimations with low MSE were obtained. The results indicate that the proposed method can accurately estimate localization accuracy.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Furuta2023,

title = {Web-Based BIM Platform for Building Digital Twin},

author = {Satoru Furuta and Jin Nakazato and Manabu Tsukada},

year  = {2023},

date = {2023-11-07},

urldate = {2023-11-07},

booktitle = {3rd Annual IEEE International Conference on Digital Twins and Parallel Intelligence (DTPI 2023)},

address = {Florida, USA},

abstract = {Building digital twin (BDT) is utilized throughout the lifecycle of a building. It serves for efficient operations during the design and construction phases, and during the operational and maintenance phases, it’s used for asset management and as field maps for robots. Building Information Modeling (BIM), which contains both semantics and geometry data of building elements, holds promise as a data source for BDT. We have extracted four key technical challenges of the digital twin, particularly vital during the operational and maintenance phases: software, visualization, update, and real-scene reconstruction. Due to the exclusive and static nature of BIM, these challenges also pose significant issues in the context of BDT. To address these challenges, we designed and implemented a Web-based BIM platform. The implemented application shows not only geometry data but also semantics data, enables easy overlay with the latest indoor conditions, and provides updating functionality. The developed system is essential for the continuous operation of BDT in dynamic indoor environments. We evaluated the application through a questionnaire survey. },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lin2023c,

title = {Potential Field-based Path Planning with Interactive Speed Optimization for Autonomous Vehicles},

author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},

url = {https://arxiv.org/abs/2306.06987},

year  = {2023},

date = {2023-10-16},

urldate = {2023-10-16},

booktitle = {49th Annual Conference of the IEEE Industrial Electronics Society (IECON 2023)},

abstract = {Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle’s PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to- vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs’ PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Chauhan2023,

title = {Keep Calm and Cross: Smart Pole Interaction Unit for Easing Pedestrian Cognitive Load},

author = {Vishal Chauhan and Chia-Ming Chang and Ehsan Javanmardi and Jin Nakazato and Koki Toda and Pengfei Lin and Takeo Igarashi and Manabu Tsukada},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374582122_Keep_Calm_and_Cross_Smart_Pole_Interaction_Unit_for_Easing_Pedestrian_Cognitive_Load/links/6525681eb32c91681fb2e1b5/Keep-Calm-and-Cross-Smart-Pole-Interaction-Unit-for-Easing-Pedestrian-Cognitive-Load.pdf},

year  = {2023},

date = {2023-10-12},

urldate = {2023-10-12},

booktitle = {The 9th IEEE World Forum on Internet of Things (IEEE WFIoT2023)},

address = {Aveiro, Portugal},

abstract = {Recently, there has been a growing emphasis on autonomous vehicles (AVs), and as they coexist with pedestrians, ensuring pedestrian safety at crosswalks has become paramount. While AVs exhibit commendable performance on traditional roads with established traffic infrastructure, their interaction in different environments, such as shared spaces lacking traffic lights or sign rules (also known as naked streets), can present significant challenges, including right-of-way and accessibility concerns. To address these challenges, this study proposes a novel approach to enhance pedestrian safety in shared spaces, focusing on the proposed smart pole interaction unit (SPIU) combined with an external human-machine interface (eHMI). By evaluating the proposal of SPIU developed by a virtual reality system, we explore its usability and effectiveness in facilitating vehicle-to-pedestrian (V2P) interactions at crosswalks. Our findings from this study showed that SPIU facilitates safe, quicker decision-making to stop and pass at crosswalks in shared space and reduces cognitive load compared to scenarios where an SPIU is absent for pedestrians and reduce the need for eHMI to see on multiple AVs. The SPIU addition with the eHMI in vehicles yields a noteworthy 21 % improvement in response time, enhancing efficiency during pedestrian stops. In both scenarios, whether with a single AV (1-way) or multiple AVs (2-way), SPIU has a positive impact on interaction dynamics and statistically demonstrates a significant improvement (p = 0.001). },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{nokey,

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},

year  = {2023},

date = {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}

}

@inproceedings{Lin2023b,

title = {Occlusion-Aware Path Planning for Collision Avoidance: Leveraging Potential Field Method with Responsibility-Sensitive Safety},

author = {Pengfei Lin and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada},

url = {https://arxiv.org/abs/2306.06981},

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)},

series = {Bilbao, Bizkaia, Spain},

abstract = {Collision avoidance (CA) has always been the foremost task for autonomous vehicles (AVs) under safety criteria. And path planning is directly responsible for generating a safe path to accomplish CA while satisfying other commands. Due to the real-time computation and simple structure, the potential field (PF) has emerged as one of the mainstream path-planning algorithms. However, the current PF is primarily simulated in ideal CA scenarios, assuming complete obstacle information while disregarding occlusion issues where obstacles can be partially or entirely hidden from the AV's sensors. During the occlusion period, the occluded obstacles do not possess a PF. Once the occlusion is over, these obstacles can generate an instantaneous virtual force that impacts the ego vehicle. Therefore, we propose an occlusion-aware path planning (OAPP) with the responsibility-sensitive safety (RSS)-based PF to tackle the occlusion problem for non-connected AVs. We first categorize the detected and occluded obstacles, and then we proceed to the RSS violation check. Finally, we can generate different virtual forces from the PF for occluded and non-occluded obstacles. We compare the proposed OAPP method with other PF-based path planning methods via MATLAB/Simulink. The simulation results indicate that the proposed method can eliminate instantaneous lateral oscillation or sway and produce a smoother path than conventional PF methods.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yosodipuro2023,

title = {Mixed-traffic Intersection Management using Traffic-load-responsive Reservation and V2X-enabled Speed Coordination},

author = {Nicholaus Danispadmanaba Yosodipuro and Ehsan Javanmardi and Jin Nakazato and Yasumasa Tamura and Xavier Defago and Manabu Tsukada},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/374470825_Mixed-traffic_Intersection_Management_using_Traffic-load-responsive_Reservation_and_V2X-enabled_Speed_Coordination/links/651ee8d63ab6cb4ec6bde79a/Mixed-traffic-Intersection-Management-using-Traffic-load-responsive-Reservation-and-V2X-enabled-Speed-Coordination.pdf},

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 = {Vehicle-to-everything (V2X) communication enables connected autonomous vehicles (CAVs) to share information and generate optimal decisions. The networking abilities of CAVs have led to the development of unsignalized autonomous intersection management (AIM) methods that leverage CAVs to significantly improve traffic flows. However, AIM methods assume 100% CAV market penetration, which is currently unrealistic owing to the gradual adoption of CAVs. Therefore, CAVs must share road usage with nonconnected vehicles (NCVs). Thus, we propose a mixed-traffic intersection management method that considers NCVs while ensuring high traffic flow, called traffic-load-responsive reservation for intersection management (TLRRIM). In TLRRIM, the roadside unit (RSU) first classifies vehicles and groups them into clusters before selecting a reservation cluster to cross an intersection. The reservation cluster selection considers both traffic load and crossing urgency. In addition, the RSU utilizes V2X-enabled speed coordination (VESC) for CAVs within the reservation cluster to further improve traffic flow, while utilizing traffic lights to guide NCVs. Simulation-based experiments using OpenCDA and CARLA showed that TLRRIM can increase throughput and reduce waiting time by up to 89.63% and 60.71%, respectively, compared with the fixed-time signaling method. Moreover, adding VESC can increase throughput by 12.21% and reduce waiting time by 10.80%, further enhancing traffic flow.  },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Wang2023,

title = {Overcoming Environmental Challenges in CAVs Through MEC-Based Federated Learning},

author = {Zekun Wang and Jin Nakazato and Muhammad Asad and Ehsan Javanmardi and Manabu Tsukada},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371685830_Overcoming_Environmental_Challenges_in_CAVs_through_MEC-based_Federated_Learning/links/64a420ea8de7ed28ba7465c7/Overcoming-Environmental-Challenges-in-CAVs-through-MEC-based-Federated-Learning.pdf},

doi = {10.1109/ICUFN57995.2023.10200688},

year  = {2023},

date = {2023-07-04},

urldate = {2023-07-04},

booktitle = {14th International Conference on Ubiquitous and Future Networks (ICUFN2023)},

pages = {1-6},

address = {Paris, France},

abstract = {Connected autonomous vehicles (CAVs), through vehicle-to-everything communication and computing resources, enable the vital exchange of information. Although deep learning is crucial in this landscape, it requires extensive and intricate datasets covering all potential scenarios. Furthermore, this situation poses a hazard, as the likelihood of accidents associated with imbalanced datasets increases, particularly in scenarios where processing analysis is compromised due to fluctuating weather conditions. We propose a Federated Learning (FL) framework undergirded by Multi-Access Edge Computing (MEC) to counter these challenges. This local device-focused framework enhances task-specific models' caching and continual updating across various conditions. In a more specific sense, edge nodes (ENs) operate as MEC, each caching multiple dedicated models and serving as the aggregator as part of the FL process. Additionally, we have engineered two innovative algorithms that categorize various states into multiple classes, thereby ensuring the efficient utilization of computing resources in ENs. Simulation results substantiate the effectiveness of our approach, showing that the proposed dedicated model consistently outperforms a general model designed for all situations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Inokuchi2023b,

title = {Semantic Digital Twin for interoperability and comprehensive management of data assets},

author = {Kazuma Inokuchi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371903091_Semantic_digital_twin_for_interoperability_and_Comprehensive_Management_of_Data_Assets/links/649b13318de7ed28ba5ca665/Semantic-digital-twin-for-interoperability-and-Comprehensive-Management-of-Data-Assets.pdf},

doi = {10.1109/MetaCom57706.2023.00049},

year  = {2023},

date = {2023-06-26},

urldate = {2023-06-26},

booktitle = {IEEE International Conference on Metaverse Computing, Networking and Applications (IEEE MetaCom 2023)},

address = {Kyoto, Japan},

abstract = {Fusion of the real and virtual worlds is essential for applying digital technology to the infrastructure of human life. A digital twin is one of the technologies that aim to integrate real and virtual space. It creates a digital world with high fidelity to reality by accumulating exhaustive information from sensors to improve simulation and prediction accuracy. However, traditional digital twins have data asset management challenges owing to the physical, temporal, and structural heterogeneity of their objects. In this paper, we propose two metadata schemas that leverage semantics to construct a designer-oriented digital twin. Moreover, we implemented a viewer that reproduced the office-like demonstration field to verify the application of the proposed ontology. The proposed method enables a generic description of the dynamic behaviors of any entity by integrating physical twins faithful to the real world with virtual models expected by designers. We compared the proposed ontologies with existing techniques, conducted user evaluations, and discussed possible approaches for further enhancements for widespread use.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Asabe2023b,

title = {AutowareV2X: Reliable V2X Communication and Collective Perception for Autonomous Driving},

author = {Yu Asabe and Ehsan Javanmardi and Jin Nakazato and Manabu Tsukada and Hiroshi Esaki},

url = {https://www.researchgate.net/profile/Jin-Nakazato/publication/371903099_AutowareV2X_Reliable_V2X_Communication_and_Collective_Perception_for_Autonomous_Driving/links/649b1457c41fb852dd36b04d/AutowareV2X-Reliable-V2X-Communication-and-Collective-Perception-for-Autonomous-Driving.pdf

https://github.com/tlab-wide/AutowareV2X

https://www.youtube.com/watch?v=57fx3-gUNxU},

doi = {10.1109/VTC2023-Spring57618.2023.10199425},

year  = {2023},

date = {2023-06-20},

urldate = {2023-06-20},

booktitle = {The 2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)},

address = {Florence, Italy},

abstract = {For cooperative intelligent transport systems (C-ITS), vehicle-to-everything (V2X) communication is utilized to allow autonomous vehicles to share critical information with each other. We propose AutowareV2X, an implementation of a V2X communication module that is integrated into the autonomous driving (AD) software, Autoware. AutowareV2X provides external connectivity to the entire AD stack, enabling the end-to-end (E2E) experimentation and evaluation of connected autonomous vehicles (CAV). The Collective Perception Service was also implemented, allowing the transmission of Collective Perception Messages (CPMs). A dual-channel mechanism that enables wireless link redundancy on the critical object information shared by CPMs is also proposed. Performance evaluation in field experiments has indicated that the E2E latency of perception information is around 30 ms, and shared object data can be used by the AD software to conduct collision avoidance maneuvers. Dual-channel delivery of CPMs enabled the CAV to dynamically select the best CPM from CPMs received from different links, depending on the freshness of their information.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@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.

},

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pubstate = {published},

tppubtype = {inproceedings}

}