@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.},

note = {Best paper award (Silver)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}