ParCon: Noise-Robust Collaborative Perception via Multi-module Parallel Connection
This work addresses the challenge of robust perception in autonomous vehicles under noisy conditions, representing an incremental improvement over existing collaborative methods.
The paper tackles the problem of improving perception performance for autonomous vehicles by introducing ParCon, a collaborative perception architecture with parallel module connections, which achieves state-of-the-art accuracy with a 6.91% increase in detection accuracy in noisy environments and reduces computational cost by 11.46% in FLOPs.
In this paper, we investigate improving the perception performance of autonomous vehicles through communication with other vehicles and road infrastructures. To this end, we introduce a novel collaborative perception architecture, called ParCon, which connects multiple modules in parallel, as opposed to the sequential connections used in most other collaborative perception methods. Through extensive experiments, we demonstrate that ParCon inherits the advantages of parallel connection. Specifically, ParCon is robust to noise, as the parallel architecture allows each module to manage noise independently and complement the limitations of other modules. As a result, ParCon achieves state-of-the-art accuracy, particularly in noisy environments, such as real-world datasets, increasing detection accuracy by 6.91%. Additionally, ParCon is computationally efficient, reducing floating-point operations (FLOPs) by 11.46%.