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

Abstract

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