A Vehicle-Infrastructure Collaborative Environment Perception Approach Based on Sparse BEV Features

Overcoming the limitations of individual-vehicle line-of-sight (LOS) sensing holds significant importance for guaranteeing the safety of driving. With a wider perception field, vehicle-infrastructure (VI) collaborative perception can provide vehicles with more comprehensive perception assistance, which has received widespread attention in recent years. However, the perception data fusion between infrastructure and vehicles is still impeded by issues such as large data volume and complex processing procedures, constituting a threat to driving safety. To deal with these issues, this paper proposes a VI collaborative environment perception approach based on sparse bird's eye view (BEV) features. By leveraging the representation of sparse BEV, features can be fused within a unified perspective in a lightweight manner, thereby enhancing the efficiency of feature fusion and reducing redundancy. Additionally, we present a solution for processing the overlapping features between the EGO-vehicle and road side unit (RSU) by taking the union of the coordinate points. Finally, the applicable vehicle and RSU datasets are collected through Carla. The experimental results demonstrate that the proposed approach can effectively mitigate the limitations of individual-vehicle perception by compensating for occluded information and provide a more comprehensive perception field.