# MACP: Efficient Model Adaptation for Cooperative Perception Source: [MACP: Efficient Model Adaptation for Cooperative Perception]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:02.230Z Authors: Yunsheng Ma, Juanwu Lu, Can Cui, Sicheng Zhao, Xu Cao, Wenqian Ye, Ziran Wang Published: 2023-10-25T14:24:42.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [3D Object Detection on V2XSet adapted from OPV2V](): 3D Object Detection; V2XSet adapted from OPV2V; AP@IoU=50%: 93. - [Collaborative 3D Object Detection on DAIR-V2X (10% labeled data ratio)](): Collaborative 3D Object Detection; DAIR-V2X 10% labeled data ratio; AP@50: 59.7. - [Collaborative 3D Object Detection on DAIR-V2X (20% labeled data ratio)](): Collaborative 3D Object Detection; DAIR-V2X 20% labeled data ratio; AP@50: 62.3. - [3D Object Detection on DAIR-V2X adapted from OPV2V](): 3D Object Detection; DAIR-V2X adapted from OPV2V; AP@50: 53.3. - [Collaborative 3D Object Detection on DAIR-V2X (1% subset)](): Collaborative 3D Object Detection; DAIR-V2X 1% labeled data ratio; AP@50: 45.5. - [Collaborative 3D Object Detection on DAIR-V2X (2% labeled data ratio)](): Collaborative 3D Object Detection; DAIR-V2X 2% labeled data ratio; AP@50: 51.3. - [Collaborative 3D Object Detection on DAIR-V2X (5% subset)](): Collaborative 3D Object Detection; DAIR-V2X 5% labeled data ratio; AP@50: 57.5. ## Original sources and implementation [Original paper]() [PDF]()