[Paper Review] CV2X-LOCA: Roadside Unit-Enabled Cooperative Localization Framework for Autonomous Vehicles

Zilin Huang, Sikai Chen+4 more|arXiv (Cornell University)|Apr 3, 2023

Indoor and Outdoor Localization Technologies4 citations

TL;DR

CV2X-LOCA is a roadside unit (RSU)-enabled cooperative localization framework that leverages C-V2X channel state information to achieve lane-level positioning accuracy in GNSS-denied urban environments. By integrating data processing, coarse positioning, environment correction, and trajectory filtering modules, it delivers state-of-the-art performance under high-speed, sparse RSU, and noisy conditions, offering a low-cost, sensor-free alternative to GNSS/INS or SLAM-based systems.

ABSTRACT

An accurate and robust localization system is crucial for autonomous vehicles (AVs) to enable safe driving in urban scenes. While existing global navigation satellite system (GNSS)-based methods are effective at locating vehicles in open-sky regions, achieving high-accuracy positioning in urban canyons such as lower layers of multi-layer bridges, streets beside tall buildings, tunnels, etc., remains a challenge. In this paper, we investigate the potential of cellular-vehicle-to-everything (C-V2X) wireless communications in improving the localization performance of AVs under GNSS-denied environments. Specifically, we propose the first roadside unit (RSU)-enabled cooperative localization framework, namely CV2X-LOCA, that only uses C-V2X channel state information to achieve lane-level positioning accuracy. CV2X-LOCA consists of four key parts: data processing module, coarse positioning module, environment parameter correcting module, and vehicle trajectory filtering module. These modules jointly handle challenges present in dynamic C-V2X networks. Extensive simulation and field experiments show that CV2X-LOCA achieves state-of-the-art performance for vehicle localization even under noisy conditions with high-speed movement and sparse RSUs coverage environments. The study results also provide insights into future investment decisions for transportation agencies regarding deploying RSUs cost-effectively.

Motivation & Objective

• To address the critical challenge of accurate vehicle localization in GNSS-denied urban canyon environments such as tunnels, multi-layer bridges, and dense urban areas.
• To explore the potential of C-V2X wireless communication signals as a reliable positioning alternative when GNSS is unavailable or degraded.
• To develop a cooperative localization framework that leverages roadside units (RSUs) and only uses C-V2X channel state information, avoiding reliance on on-board perception sensors.
• To optimize RSU deployment strategies by identifying the effective connectivity range and coverage density for robust localization performance.
• To provide a low-complexity, scalable solution that can be seamlessly integrated with existing GNSS/INS or map-matching systems.

Proposed method

• The framework processes C-V2X channel state information (e.g., RSS, TOA, TDOA) from multiple RSUs to estimate vehicle position without relying on on-board sensors.
• A data processing module extracts and normalizes raw C-V2X measurements to reduce noise and improve consistency.
• The coarse positioning module uses a non-convex optimization approach based on minimizing the log-ratio of estimated to measured distances, formulated as a max-min problem.
• An environment parameter correcting module adjusts for multipath and non-line-of-sight (NLOS) errors by modeling signal propagation anomalies.
• A vehicle trajectory filtering module applies a moving window-based Kalman filter to smooth position estimates and reduce jitter.
• The framework is designed to handle dynamic network conditions, including high-speed movement and sparse RSU deployment, through adaptive parameter tuning.

Experimental results

Research questions

• RQ1 Can C-V2X channel state information alone achieve lane-level localization accuracy in GNSS-denied urban environments?
• RQ2 How does the proposed CV2X-LOCA framework perform under high-speed vehicle motion and sparse RSU deployment?
• RQ3 What is the optimal RSU connectivity range that minimizes localization error and network volatility?
• RQ4 How does CV2X-LOCA compare to existing GNSS/INS, map-matching, or SLAM-based localization methods in terms of accuracy and robustness?
• RQ5 Can the framework be effectively integrated with existing localization systems to enhance overall performance?

Key findings

• CV2X-LOCA achieves a median localization error of 0.83 meters in simulation and 1.12 meters in field experiments, demonstrating lane-level accuracy in GNSS-denied urban canyons.
• The framework maintains robust performance under high-speed conditions (up to 60 km/h) and with sparse RSU deployment, outperforming conventional methods in challenging environments.
• The average computation time per localization update is 8.26ms for the data processing module, 9.42ms for coarse positioning, and 11.28ms for trajectory filtering, indicating reasonable real-time feasibility.
• The study identifies an optimal RSU connectivity range of approximately 150–200 meters, which minimizes network topology volatility and maximizes positioning accuracy.
• CV2X-LOCA reduces dependency on costly on-board perception sensors and can be seamlessly integrated with existing GNSS/INS or HD map systems to enhance overall localization robustness.
• Field experiments confirm the framework’s practical viability, showing consistent performance across diverse urban road types and signal conditions.