Scenarios such as vehicle platooning urgently require high-precision object tracking algorithms with strong ID consistency. However, current state-of-the-art (SOTA) 3D tracking methods often suffer from ID switches. To address this limitation, this paper proposes a LiDAR-UWB Object Tracking (LUOT) algorithm designed to achieve centimeter-level (1σ) 3-D object tracking with zero ID switches. The method fuses high-precision LiDAR-based bounding boxes (BBox) with the ID consistency of ultra-wideband (UWB). Considering the high precision of 3D BBoxes at positions densely populated by point clouds, LUOT replaces the conventional association center point with coordinates transformed to the vehicle coordinate frame through the vehicle’s rear-end frame, where point clouds are denser, thereby minimizing errors arising from center point detection inaccuracies. To mitigate the destructive impact of occasional UWB outliers, this study pioneers the introduce of a Mahalanobis-distance-based Chi-square test, integrating multiple UWB ranges to comprehensively detect and eliminate outliers. Finally, real-world vehicle experiments demonstrate that LUOT achieves zero ID switches with a tracking accuracy of 0.0868 m (1σ), fully satisfying centimeter-level precision requirements for vehicle autonomous driving. These results establish LUOT as a state-of-the-art solution for vehicle-to-vehicle global object tracking and fill an important gap in LiDAR-UWB fusion methods. The source code is publicly available at: https://github.com/ly3106/LUOT.