  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
registration 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.
