Robust 3D Bounding Box Detection and State Estimation of Dynamic Objects for Autonomous Navigation
Bibliographic record
Abstract
Accurate state estimation of dynamic objects is critical for safe and reliable autonomous navigation in dynamic urban settings. Real-world scenarios may include occlusions, drifting in pose estimation, data sparsity, or abrupt appearances, representing complexity in multi-object tracking. This paper addresses the challenges of estimating the states of non-ego vehicles during autonomous navigation using multimodal visual-LiDAR perception, without any need to global navigation systems. The proposed estimation framework integrates detection and tracking through an optimal variance filter. The first stage incorporates instance segmentation and point cloud association, followed by an L-shape fitting method to estimate 3D Bounding Boxes. Object tracking is then performed using a modified multiobject tracking algorithm augmented by the initialization of motion models. This enables estimation of consistent surrounding vehicle velocities and positions while keeping track of them in the 3D space, enhancing situational awareness for motion planning, and supporting collision avoidance using predictive models. Comprehensive experiments on the KITTI dataset demonstrate the effectiveness and accuracy of the proposed framework compared to current benchmarks and state-of-the-art methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".