Collaborative Perception Method Based on Multisensor Fusion
This paper proposes a novel multimodal collaborative perception framework to enhance the situational awareness of autonomous vehicles.First,a multimodal fusion baseline system is built that effectively integrates Light Detection and Ranging(LiDAR)point clouds and camera images.This system provides a comparable benchmark for subsequent research.Second,various well-known feature fusion strategies are investigated in the context of collaborative scenarios,including channel-wise concatenation,element-wise summation,and transformer-based methods.This study aims to seamlessly integrate intermediate representations from different sensor modalities,facilitating an exhaustive assessment of their effects on model performance.Extensive experiments were conducted on a large-scale open-source simulation dataset,i.e.,OPV2V.The results showed that attention-based multimodal fusion outperforms alternative solutions,delivering more precise target localization during complex traffic scenarios,thereby enhancing the safety and reliability of autonomous driving systems.
Autonomous drivingCollaborative perception3D object detectionMultimodal fusionIntelligent transportation systems
万方数据
维普
