Robust Radar and Vision Association for Measurement Uncertainty and Loss Especially in the Distant Region

Association is crucial but easy to be neglected in radar and vision object-level fusion. Measurement uncertainty, consisting of complementary random errors of hetero- geneous sensors and unpredictable deviation error due to monocular vision ranging, impedes a powerful radar and vision association. This article proposes a robust radar and vision association method for measurement uncertainty and loss. First, radar and vision measurements are obtained by data preprocessing. Then, the cost matrix is initialized using the weighted Euclidean distance based on measurement random errors and road lane constraints. Next, the initial cost matrix is adaptively modified based on vision measurement calibration model to respond to vision deviation error. Finally, Hungarian algorithm is enhanced to tackle the unbalanced assignment problem caused by measurement loss. Experiments under different environmental conditions (clear weather, rain, and nighttime) show the average total correct association rate of the proposed method is at least 5.24% higher than comparative methods, indicating a more reliable association performance, especially in the distant region.