Enhancing Autonomous Vehicle Localization Through Cooperative LiDAR and Smart Infrastructure Integration

Autonomous driving systems rely on precise localization to ensure safety and performance in dynamic environments. However, standalone vehicle localization methods using onboard sensors often fail to deliver sufficient accuracy in adverse environments, such as tunnels or areas with limited distinguishable features. To address these challenges, we propose a novel cooperative localization framework that leverages roadside LiDAR-equipped infrastructure and vehicle-to-infrastructure (V2I) communication. In our approach, roadside LiDARs detect and estimate vehicle positions using a refined L-shape fitting algorithm, complemented by the vehicle’s geometric data shared over the V2I network. This method mitigates errors associated with partial point cloud data and improves localization precision significantly. By integrating this infrastructure-based positioning data with onboard localization modules via sensor fusion, our framework enhances overall accuracy and robustness in real-time autonomous driving scenarios. Experimental results in a digital twin environment demonstrate that our system achieves over 70% improvement in localization accuracy compared to self-localization methods. Our work highlights the potential of smart infrastructure to enhance localization, reduce onboard sensor dependency, and improve autonomous driving reliability under challenging environment for self-localization.