LiDAR-Based Cooperative Localization Model for Leader-Follower Cooperative Driving Automation System

Abstract: The Leader-Follower Cooperative Driving Automation (LF-CDA) system, crucial for applications such as truck platooning and off-road vehicle convoys, relies on automation and communication technologies to virtually link multiple vehicles and has become a core focus in the automated vehicle industry. Accurate relative positioning is critical for LF-CDA operations, yet GNSS can be unreliable in challenging environments. Asymmetric architecture is common in many LF-CDA systems, making direct application of localization models either infeasible or both computationally and communication intensive. This manuscript presents a lightweight LiDAR-based cooperative localization model that leverages the unique characteristics of asymmetric LF-CDA systems, termed “asynchronous view repetition”. This means that the follower vehicle, operating in a vehicle-following mode, consistently receives similar visual and spatial information as the leader vehicle, albeit with a time delay. To capitalize on such system characteristics, an asynchronous view repetition-based graph optimization model is formulated to minimize the positional errors of both leader and follower vehicles. To provide input to and solve the graph optimization model, a lightweight cooperative localization framework with multiple submodules is established, allowing the system to function independently of environmental constraints. A comprehensive set of experiments was conducted in the CARLA simulation environment, using CT-ICP and KISS-ICP as benchmarks, given their strong performance in single-vehicle settings. The results indicate that, under the LF-CDA scenario, our proposed model demonstrates greater suitability by achieving higher localization accuracy while maintaining comparable or even superior computational efficiency.