@inproceedings{Lin2024c,

title = {A Rule-Compliance Path Planner for Lane-Merge Scenarios Based on Responsibility-Sensitive Safety},

author = {Pengfei Lin and Ehsan Javanmardi and Yuze Jiang and Manabu Tsukada},

year  = {2024},

date = {2024-12-12},

urldate = {2024-12-12},

booktitle = {2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV)},

address = {Dubai, UAE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Grosset2024,

title = {Generation of V2X messages from Carla Simulator for cooperative perception: Application to pedestrian safety},

author = {Juliette Grosset and Jean-Marie Bonnin and Alain-Jérôme Fougères and Manabu Tsukada and Moise Djoko-Kouam},

year  = {2024},

date = {2024-10-07},

booktitle = {The IEEE 100th Vehicular Technology Conference (VTC2024-Fall)},

address = {Washington DC, USA},

abstract = {Despite advancements in connected and autonomous vehicles (CAVs), vulnerable road users (VRUs) face a challenge as they lack Communication-Intelligent Transport System (C-ITS) equipment. This deficiency impedes their interaction with CAVs. We underscore the significance of Vehicle-to-Everything (V2X) communication in enhancing road safety with VRUs by facilitating information exchange between CAVs and the infrastructure. This communication is pivotal for reintegrating VRUs into the environmental awareness of CAVs. The Carla Simulator, used for autonomous vehicle training, currently lacks comprehensive V2X communication capabilities. In response, we propose an architecture for Carla, integrating OpenCDA and ROS2 to establish a simulated V2X network communication system for CAVs and roadside units (RSUs) within the Carla environment. This setup allows for the generation of V2X datasets and the refinement of algorithms for Advanced Driver Assistance Systems (ADAS). To illustrate and assess our proposed architecture, we present a scenario involving a pedestrian concealed in a blind spot for a connected vehicle.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Yun2024,

title = {Large Language Models for Human-like Autonomous Driving Decision Making: A Survey},

author = {Li Yun and Kai Katsumata and Ehsan Javanmardi and Manabu Tsukada},

year  = {2024},

date = {2024-09-24},

urldate = {2024-09-24},

booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},

address = {Edmonton, Canada},

abstract = {Large Language Models (LLMs), AI models trained on massive text corpora with remarkable language understanding and generation capabilities, are transforming the field of Autonomous Driving (AD). As AD systems evolve from rule-based and optimization-based methods to learning-based techniques like deep reinforcement learning, they are now poised to embrace a third and more advanced category: knowledge-based AD empowered by LLMs. This shift promises to bring AD closer to human-like AD. However, integrating LLMs into AD systems poses challenges in real-time inference, safety assurance, and deployment costs. This survey provides a comprehensive and critical review of recent progress in leveraging LLMs for AD, focusing on their applications in modular AD pipelines and end- to-end AD systems. We highlight key advancements, identify pressing challenges, and propose promising research directions to bridge the gap between LLMs and AD, thereby facilitating the development of more human-like AD systems. The survey first introduces LLMs’ key features and common training schemes, then delves into their applications in modular AD pipelines and end-to-end AD, respectively, followed by discussions on open challenges and future directions. Through this in-depth analysis, we aim to provide insights and inspiration for researchers and practitioners working at the intersection of AI and autonomous vehicles, ultimately contributing to safer, smarter, and more human-centric AD technologies.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Jiang2024b,

title = {Accurate Cooperative Localization Utilizing LiDAR-equipped Roadside Infrastructure for Autonomous Driving},

author = {Yuze Jiang and Ehsan Javanmardi and Manabu Tsukada and Hiroshi Esaki},

url = {https://arxiv.org/abs/2407.08384 },

year  = {2024},

date = {2024-09-24},

urldate = {2024-09-24},

booktitle = {27th IEEE International Conference on Intelligent Transportation Systems (ITSC 2024)},

address = {Edmonton, Canada},

abstract = {Recent advancements in LiDAR technology have significantly lowered costs and improved both its precision and resolution, thereby solidifying its role as a critical component in autonomous vehicle localization. Using sophisticated 3D reg- istration algorithms, LiDAR now facilitates vehicle localization with centimeter-level accuracy. However, these high-precision techniques often face reliability challenges in environments devoid of identifiable map features. To address this limitation, we propose a novel approach that utilizes road side units (RSU) with vehicle-to-infrastructure (V2I) communications to assist vehicle self-localization. By using RSUs as stationary reference points and processing real-time LiDAR data, our method enhances localization accuracy through a cooperative localization framework. By placing RSUs in critical areas, our proposed method can improve the reliability and precision of vehicle localization when the traditional vehicle self-localization technique falls short. Evaluation results in an end-to-end autonomous driving simulator AWSIM show that the proposed method can improve localization accuracy by up to 80% under vulnerable environments compared to traditional localization methods. Additionally, our method also demonstrates robust resistance to network delays and packet loss in heterogeneous network environments.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}