Proprioceptive Observer Design for Speed Estimation in Automated Driving Systems
Bibliographic record
Abstract
A state observer, robust to road surface conditions, is designed to estimate the longitudinal speed (and slip) which is essential for controls and safety-critical decision making in autonomous driving. The novel approach estimates slip at each wheel, and can be integrated with the existing visual-inertial navigation systems. The wheel-level observer, which uses proprioceptive sensor data, fuses vehicle kinematic states, tire internal states, and the wheel dynamics to estimate the speed at each tire, without any information of the road surface friction or global navigation satellite systems (GNSS). Then, a wheel-vehicle dynamical model, which augments estimates at each tire with the vehicle dynamics, is developed to design an integrated slip-aware framework for speed estimation. The stability of the augmented error dynamics is studied and the mean square estimation error is proved to be uniformly bounded. Experimental tests have been conducted to validate the proposed framework in pure- and combined-slip driving scenarios on various surface friction conditions. As confirmed by several road experiments, the designed observer provides consistent and accurate speed (and slip) estimates at each tire for high-slip scenarios, which are essential for safe navigation, motion planning, and path following in automated driving systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".