Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty
This addresses robustness issues in multi-agent perception for applications like autonomous driving, though it appears incremental by refining existing frameworks with modality-level uncertainty handling.
The paper tackles the problem of multi-agent systems with heterogeneous multimodal sensors by proposing A2MAML, an uncertainty-aware, modality-level collaboration approach, resulting in up to 18.7% higher accident detection rate in autonomous driving scenarios.
Multi-agent systems are increasingly equipped with heterogeneous multimodal sensors, enabling richer perception but introducing modality-specific and agent-dependent uncertainty. Existing multi-agent collaboration frameworks typically reason at the agent level, assume homogeneous sensing, and handle uncertainty implicitly, limiting robustness under sensor corruption. We propose Active Asymmetric Multi-Agent Multimodal Learning under Uncertainty (A2MAML), a principled approach for uncertainty-aware, modality-level collaboration. A2MAML models each modality-specific feature as a stochastic estimate with uncertainty prediction, actively selects reliable agent-modality pairs, and aggregates information via Bayesian inverse-variance weighting. This formulation enables fine-grained, modality-level fusion, supports asymmetric modality availability, and provides a principled mechanism to suppress corrupted or noisy modalities. Extensive experiments on connected autonomous driving scenarios for collaborative accident detection demonstrate that A2MAML consistently outperforms both single-agent and collaborative baselines, achieving up to 18.7% higher accident detection rate.