Enabling High-Precision 5G mmWave-Based Positioning for Autonomous Vehicles in Dense Urban Environments
Bibliographic record
Abstract
Autonomous vehicles (AVs) have the potential to transform the transportation industry by altering conventional modes of travel, enhancing road safety measures, and mitigating traffic congestion and greenhouse gas emissions. An accurate, continuous, and robust positioning solution is required for AVs to operate safely in dense urban environments. In this thesis, we tackle the problem of AV positioning by utilizing the emerging 5G New Radio (NR) millimeter Wave (mmWave) technology along with AV's OBMS, including accelerometers, gyroscopes, and speed measurements. To achieve this, three novel contributions are introduced. The first contribution introduces a high-precision standalone 5G mmWave-based positioning method that utilizes a novel non-line-of-sight (NLoS) detection scheme that can effectively mitigate the detrimental effects of multipath and shadowing, which can be significant in dense urban environments. The second contribution of this research is an ensemble-learning-based OoRI method, which is essential to facilitate robust multipath positioning in dense urban environments. The proposed classifier is crucial for the realization of many multipath positioning schemes which have been limited to working with single-bounce reflections (SBRs). Lastly, an integrated positioning solution based on an unscented Kalman filter as a multi-system fusion engine is developed to integrate the 5G line-of-sight (LoS) and multipath signals with the AV’s OBMS to achieve an uninterrupted positioning solution at high precision. The methodology also features a measurement exclusion scheme and an additional validation stage for NLoS measurements using motion constraints. To validate the proposed methodologies, quasi-real 5G measurements were collected using a commercially available ray-tracing tool that incorporates 3D map scans of downtown Toronto (ON, Canada), allowing for realistic road test scenarios in dense urban environments involving realistic multipath challenges. Additionally, for the same road tests, real OBMS data were collected from the test vehicle moving in downtown Toronto at various motion dynamics. The results of this work demonstrate that the proposed system is capable of maintaining a level of accuracy below 30 cm for approximately 97% of the time, which is superior to the accuracy level achieved when multipath signals and OBMS are not considered, which is only around 91% of the time.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".