Continuous Error Map-Aided Adaptive Multisensor Integration for Connected Autonomous Vehicles in Urban Scenarios

Precise multisensor integrated positioning is essential for autonomous vehicles (AVs) in urban environments. One of the key challenges in multisensor fusion is accurately estimating the weights of heterogeneous sensor data. With the emergence of cellular vehicle-to-everything (C-V2X) technology and smart roadside infrastructure (RSI), these systems can collaborate to provide enhanced and reliable services to connected vehicles. Motivated by this, our research explores the use of sensor error maps for heterogeneous sensor measurements under varying environmental conditions to improve the positioning accuracy of connected AVs (CAVs) in complex urban areas. We propose a multisensor integrated positioning system that utilizes error map information generated by sensor-rich CAVs. This error information is shared with RSIs and then distributed to nearby CAVs within the same region. Sensors with higher estimated errors are assigned lower weights, as determined by the error maps. To validate the proposed approach, we conducted experiments both day and night in a realistic simulation environment as well as in the Hong Kong C-V2X testbed. The results demonstrate that the use of continuous error maps significantly enhances the performance of multisensor integrated positioning.