Vehicle-to-vehicle (V2V) communications have greatly enhanced the perception capabilities of connected and automated vehicles (CAVs) by enabling information sharing to "see through the occlusions", resulting in significant performance improvements. However, developing and training complex multi-agent perception models from scratch can be expensive and unnecessary when existing single-agent models show remarkable generalization capabilities. In this paper, we propose a new framework termed MACP, which equips a single-agent pre-trained model with cooperation capabilities. We approach this objective by identifying the key challenges of shifting from single-agent to cooperative settings, adapting the model by freezing most of its parameters and adding a few lightweight modules. We demonstrate in our experiments that the proposed framework can effectively utilize cooperative observations and outperform other state-of-the-art approaches in both simulated and real-world cooperative perception benchmarks while requiring substantially fewer tunable parameters with reduced communication costs. Our source code is available at https://github.com/PurdueDigitalTwin/MACP.

该论文提出了一种名为MACP的新框架，通过将单一代理预训练模型配备合作能力，以实现来自单一代理的转变，并通过冻结其大部分参数并添加轻量级模块来适应模型。实验证明该框架能够有效利用合作性观测，并在模拟和真实合作感知基准测试中表现优异，同时要求较少的可调参数和降低的通信成本。

MACP：高效的合作感知模型适应