Inter-Vehicle Position Estimation For NLOS Condition In The Persistence Of GPS Outages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main focus of thesis work addresses one of the functional key points of Cooperative Collision Warning application which is an accurate estimation of the range data of neighboring vehicles during persistent GPS outages under both line-of-sight (LOS) and non-line-of-sight (NLOS) situations. Cooperative Collision Warning, based on vehicle-to-vehicle radio communications and GPS systems, is one promising active safety application that has attracted considerable research interest. One of the severe estimation error is due to NLOS that can be mitigated by applying biased Kalman filter on range measurements. For our algorithm these inter-vehicle distances are measured from using one of the radio-based ranging techniques. Main objective is to establish an accurate map of positions for neighboring vehicles in the persistance of GPS outages. GPS outages can be possible in multipath environments where NLOS component is introduced to the true range measurements. These position estimates mainly depend on two factors: (i) Preprocessed inter-vehicle distances (range data is processed from biased Kalman filter); (ii) Road constraints (the vehicle uncertainty is more in the direction of road than the uncertainty in the direction opposite the road); This thesis suggests smoothing and mitigating the NLOS for radio-based ranging measurements under multipath conditions. In order to find accurate positions of neighboring vehicles an extended Kalman filter is implemented along with road constraints. Unbiased Kalman filter, biased Kalman filter and extended Kalman filter performances are experimentally verified using Matlab simulation tool with random number of vehicles at unknown random distinct positions in some physical region along a section of road for vehicular environment.
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.
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 | 0.001 | 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 it