Evidence map›Paper›PMID 42884287›Full record

ArticleSensors (Basel, Switzerland)2026

Application of Radar Measurements in Neural-Dynamic and Game-Based Safe Ship Controls in Multi-Object Passing Situations.

Józef Lisowski

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Józef LisowskiDepartment of Autonomous Systems, Faculty of Computer Science, Gdynia Maritime University, 81-225 Gdynia, Poland.ORCID 0000-0001-9281-376X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Radar sensors, which enable the identification of the navigational situation and a preliminary assessment of collision risk, form the basis for safe ship control. This paper presents the author's quantitative methods for assessing collision risk in the context of multi-ship navigation, and then synthesizes safe control methods. The author proposes four neural domain variants and three mathematical models of collision risk. Proprietary methods are presented: neural-dynamic control (NDC) and game-based control (GC). An experimental comparison of these control methods is conducted using data from real-world navigation scenarios. Therefore, under favorable traffic conditions, the NDC method proved to be the most effective, while the GC method facilitated effective cooperative and non-cooperative management in situations of restricted ship traffic. The safe control methods proposed in this study will contribute to increased navigation safety, particularly in situations of high ship traffic density and challenging environmental conditions.

Indexed as

artificial intelligencecollision riskgame controlsafe controlsensing in safety navigation development

Identifiers

PMID42884287
PMCPMC13667614

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.