Adaptive Navigation Strategy for Low‐Cost IMU‐Assisted Vehicles in GNSS‐Denied Traffic Environment
Bibliographic record
Abstract
In the context of increasingly complex and diverse traffic environments, where intelligent and connected vehicles (ICVs) coexist with conventional human‐driven vehicles, maintaining reliable navigation under global navigation satellite system (GNSS) outages is crucial for supporting adaptive driving strategies and ensuring operational safety. Low‐cost inertial measurement units (IMUs) offer promising solutions due to their low computational load and high self‐sufficiency, yet error accumulation remains a persistent challenge, particularly in real‐world mixed traffic scenarios. This study introduces a dilated convolutional neural network (DCN)–driven framework to directly estimate vehicle forward velocity and IMU error parameters from raw IMU measurements, addressing the reliance on hardware‐based odometry by extending nonholonomic constraints (NHC) into three‐dimensional velocity constraints. By dynamically optimizing IMU error parameters through integration with an error model, the proposed method mitigates the adverse effects of inherent noise in low‐cost IMUs, enabling robust navigation in GNSS‐denied environments. Validation using a GNSS/INS dataset demonstrates that the approach accurately estimates vehicle position while significantly suppressing error accumulation, which is pivotal for maintaining reliable navigation in heterogeneous traffic flows where autonomous and human‐driven vehicles coexist. This contributes to the development of robust vehicle autonomy and enhanced safety in mixed‐traffic ecosystems, enabling more adaptive and resilient driving strategies.
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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".