Correlation-aware measurement fusion for reliable cooperative 3D target tracking in connected autonomous vehicles

Abstract

Abstract Connected autonomous vehicles can improve target tracking by sharing distributed measurements of surrounding traffic participants. However, cooperative measurement fusion is reliable only when both measurement uncertainty and inter-vehicle dependence are properly modeled. Sequential Kalman filtering provides a communication-efficient late-fusion solution for cooperative target tracking, but its standard formulation relies on statistically independent and temporally aligned measurements. In practical connected-vehicle systems, this assumption can be violated by shared detection algorithms, common environmental conditions, and communication delay. As a result, correlated or temporally misaligned measurements may be repeatedly counted, leading to underestimated covariance and overconfident target-state estimates. This paper proposes ReCoTrack, a correlation-aware measurement fusion framework for reliable cooperative 3D target tracking. ReCoTrack preserves the association advantages of sequential Kalman filtering while selectively applying Covariance Intersection to targets jointly observed by multiple vehicles. This design enables conservative state fusion under unknown inter-vehicle measurement correlations. To improve uncertainty propagation, a trajectory-based process-noise estimator is further introduced to learn motion-dependent covariance from historical target states. An innovation-consistency objective is also designed to jointly calibrate observation and process noise covariances. Experiments on the V2V4Real dataset show that the proposed method improves AMOTA by 2.8%, increases mostly tracked trajectories by 4.5%, and reduces mostly lost trajectories by 10.9% under the same late-fusion communication setting, while evaluation on OPV2V under a unified tracking protocol shows that ReCoTrack achieves the highest AMOTA among the compared methods and outperforms DMSTrack by 1.05 percentage points. The framework requires only 0.0073 MB per CAV per frame. Additional tests under controlled cross-CAV dependence and stochastic communication degradation demonstrate that the proposed method improves both tracking accuracy and covariance reliability.