Toward Land Vehicle Ego-Velocity Estimation Using Deep Learning and Automotive Radars
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a deep learning framework for the estimation of land vehicle ego-velocity using Frequency Modulated Continuous Wave (FMCW) automotive radars, addressing the challenges of data sparsity and noise without the need for extrinsic radar calibration. By structuring radar scans into image-based and voxel-based networks, our approach demonstrates robust ego-velocity estimation across multiple sensor configurations and orientations. Experimental results from three distinct datasets—RadarScenes, NavINST, and MSC-RAD4R—validate the framework’s effectiveness, showing superior performance over traditional methods. The models’ adaptability to various sensor specifications and their computational efficiency highlight their potential for real-time applications. We made our implementation open-source at: https://github.com/paaraujo/deep-ego-velocity.
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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 | 0.001 |
| 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 it