Dynamic Collision Avoidance for Maritime Navigation Using UKF Based Predictive Probability and Velocity Obstacle Framework
Bibliographic record
Abstract
Abstract This study proposes a collision avoidance algorithm based on predictive probability using the Unscented Kalman Filter (UKF) to address the challenges of dynamic and uncertain maritime navigation. Previous UKF-based approaches relied on fixed time steps to extrapolate future positions from a single application, leading to progressively increasing uncertainties. In contrast, the proposed algorithm integrates ship dynamics into the UKF framework, allowing real-time recalibration and tailoring prediction horizon based on the ship’s physical characteristics and speed, ensuring a relevant and physically meaningful duration for enhanced accuracy and reliability regarding future positions. The algorithm integrates predictive probabilities into a Velocity Obstacle (VO) framework, ensuring adherence to maritime navigation rules (COLREGs) while enabling optimal path planning via a Nonlinear Model Predictive Controller (NMPC). By accurately calculating the Time to Closest Point of Approach (TCPA) and Distance to Closest Point of Approach (DCPA), it effectively prevents unexpected maneuvers, facilitating smoother, safer, and more reliable navigation in dynamic maritime environments. The effectiveness of the proposed method is demonstrated through comprehensive simulations, showcasing its ability to plan optimal paths and accurately track trajectories across various collision scenarios, such as head-on, overtaking, and crossing. The results emphasize the Unscented Kalman Filter’s robustness in estimating ship positions with precision, maintaining computational stability, and effectively mitigating collision risks ensuring enhanced safety and reliability in dynamic maritime environments.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".