BlindMap: Explicit Blind-Area Prediction and Request-Free Communication for Efficient Cooperative Perception

The proliferation of autonomous vehicles necessitates advanced environmental understanding through collaborative perception, yet achieving high accuracy while maintaining communication efficiency and strict latency remains a challenge. Existing region selection strategies, often relying on ego-centric uncertainty, lead to redundant data transmission or the omission of critical information, and their inherent request-response protocols introduce significant latency bottlenecks that hinder real-time deployment. To address these critical limitations and ensure timely, high-value information exchange, we propose BlindMap, a novel collaborative perception framework. BlindMap introduces an interpretable, request-free, and deadline-aware communication paradigm where collaborators proactively infer the ego vehicle’s occluded regions from a third-person perspective, selectively transmitting only critical, unobservable features. This decoupled approach enables parallel processing, significantly reducing system latency. Our framework incorporates a Spatial-Temporal BlindMap Predictor for accurate, proactive blind-area identification and a Blind-Aware Multi-Scale Fusion module that robustly integrates features under noise and misalignment. Extensive experiments on DAIR-V2X-C, OPV2V, and V2XSet demonstrate that BlindMap achieves a state-of-the-art perception accuracy improvement of up to 9.45% under a 1MB bandwidth constraint, while drastically reducing collaboration latency to 21 ms—a 78% decrease compared to existing methods. Even under tight latency budgets, BlindMap consistently maintains high accuracy and stable throughput, proving its potential for robust, real-time, deadline-constrained collaborative perception in dynamic autonomous driving environments. The code is available at: https://github.com/AlexZhu2000/BlindMap