Evidence map›Paper›PMID 40657211›Full record

ArticleMethodsX2025

Method for detecting rough road index using iot sensor fusion and v2v interaction for efficient road infrastructure management.

Akhilesh Kumar Singh, Damodar Reddy Edla

Abstract read
In one paragraph

Article in MethodsX, 2025. 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

2 authors.

Akhilesh Kumar SinghCSE, NIT Goa, Cuncolim, Goa, India.
Damodar Reddy EdlaCSE, NIT Goa, Cuncolim, Goa, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The method proposed explores the integration of Internet of Things (IoT) technologies and sensor fusion techniques into modern transportation systems to enhance efficiency, safety, and road quality assessment. The study not only emphasizes the importance of V2V communication using DSRC Dedicated Short Range Communication, Threshold Based Algorithms (TBA) and Dynamic Time Warping (DTW) but also proposes a novel approach of IoT multi-sensor fusion hardware model that is designed to identify clean and rough surface road conditions. By deploying multi sensor module into proposed model, real-time road surface data is collected and processed to classify them into flat clean or bumpy rough categories based on their smoothness index. In order to monitor the road surface in real time, this study suggests a novel IoT-based multi-sensor fusion hardware architecture that combines LiDAR, accelerometer, and ultrasonic sensors.•To improve road safety and navigation, the suggested methodical system makes advantage of DSRC-enabled V2V communication, primarily for luxury and driverless cars.•The proposed method provides revolutionary possibilities for intelligent transportation systems and fills in gaps in the high-accuracy identification of road anomalies

Indexed as

Autonomous drivingEdge computingIoTIOT Based Multi Sensor Fusion Method for Rough Road DetectionRoad smoothnessSensor fusionV2V communication

Identifiers

PMID40657211
PMCPMC12246931

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.