# Roadside LiDAR Assisted Cooperative Localization for Connected Autonomous Vehicles

arXiv: https://arxiv.org/abs/2311.07913 · HTML: https://mlsanity.com/paper/2311.07913/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2023.

## Built on by (most cited first, 3 of 3)

- [End-to-End Autonomous Driving through V2X Cooperation](https://mlsanity.com/paper/2404.00717/) (2024)
- [CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting](https://mlsanity.com/paper/2507.18473/) (2025)
- [Accurate Cooperative Localization Utilizing LiDAR-equipped Roadside Infrastructure for Autonomous Driving](https://mlsanity.com/paper/2407.08384/) (2024)

## Abstract (arXiv)

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 autonomous 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.