Due to the lack of real multi-agent data and timeconsuming of labeling, existing multi-agent cooperative perception algorithms usually select the simulated sensor data for training and validating. However, the perception performance is degraded when these simulation-trained models are deployed to the real world, due to the significant domain gap between the simulated and real data. In this paper, we propose the first Simulation-to-Reality transfer learning framework for multiagent cooperative perception using a novel Vision Transformer, named as S2R-ViT, which considers both the Implementation Gap and Feature Gap between simulated and real data. We investigate the effects of these two types of domain gaps and propose a novel uncertainty-aware vision transformer to effectively relief the Implementation Gap and an agentbased feature adaptation module with inter-agent and egoagent discriminators to reduce the Feature Gap. Our intensive experiments on the public multi-agent cooperative perception datasets OPV2V and V2V4Real demonstrate that the proposed S2R-ViT can effectively bridge the gap from simulation to reality and outperform other methods significantly for point cloud-based 3D object detection.
Medium/distant maritime rescue is significantly important in the development of maritime business. For typical medium/distant maritime rescue, the range limitation of helicopters and many difficulties between helicopter and ship cooperation lead to unsatisfactory rescue results. Compared to helicopters and ships, amphibious aircrafts could effectively solve the problems faced by helicopters and ships and meet the medium/distant maritime rescue demands with their long cruise range, high speed, high rescue capability and surface landing capability. Therefore, a time-domain planning method (TPM) based on the k-means* clustering algorithm and the genetic algorithm* is proposed in this study for the surface rescue process (SRP) of amphibious aircrafts in medium/distant maritime rescue. To simulate the SRP of amphibious aircrafts, an agent-based simulation environment of medium/distant maritime rescue was constructed based on the Python platform. Finally, a case study was carried out to verify its effectiveness and applicability. The results show that the TPM exhibits satisfactory rescue results for the SRP of the amphibious aircraft and that less than 1 h of delay time is recommended for the amphibious aircraft to rescue the persons in distress by using TPM.
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