Cooperative 3D Multi-Object Tracking for Connected and Automated Vehicles with Complementary Data Association

Cooperative perception has attracted sustained attention, promising groundbreaking contributions to transportation safety and efficiency. It enables vehicles to share environmental information in addressing limited visibility, thus improving individual perception performance. However, most related studies only focus on detection, and ways to explicitly enhance object tracking capabilities through multi-vehicle cooperation still lack sufficient exploration. In this paper, we propose a cooperative 3D multi-object tracking (MOT) system that leverages complementary information from multiple vehicles to alleviate the problem of temporary tracking failures. Specifically, we design a data association module to assist the ego vehicle in leveraging received information to promptly compensate for its missed objects. To avoid erroneous associations, we maintain an object ID mapping set for each communication link to discover the correspondence between objects tracked by different vehicles. We conduct experiments on the V2V4Real dataset and utilize the official pre-trained network checkpoints to generate detection candidates as inputs. Experimental results demonstrate that the proposed method performs favorably against the baseline without bringing a communication burden, as well as its generalizability for various detectors.