Evidence map›Paper›PMID 35634117›Full record

ArticlePeerJ. Computer science2022

A longitudinal inter-vehicle distance controller application for autonomous vehicle platoons.

Alex Gunagwera, Aydin Tarik Zengin

Open access · goldAbstract read
In one paragraph

Article in PeerJ. Computer science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.7field-weighted citation impact, top 17% of its field
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

1 citing paper in PubMed, 15 citations in OpenAlex.

  1. Article
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 at 1 institution in 1 country.

Alex GunagweraDepartment of Computer Engineering, Istanbul Sabahattin Zaim University, Kucukcekmece, Istanbul, Turkey.
Aydin Tarik ZenginDepartment of Computer Engineering, Istanbul Sabahattin Zaim University, Kucukcekmece, Istanbul, Turkey.ORCID 0000-0002-0860-4509
İstanbul Sabahattin Zaim Üniversitesi · TR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autonomous vehicle platoons are a promising solution to road safety, efficient road utilization, emission reduction, among other problems facing today's transportation industry. However, consistently maintaining the desired inter-vehicle distance is one of the major problems facing autonomous vehicle platoons. In this study, we propose a proportional-integral-derivative (PID)-based cost-efficient algorithm to control the longitudinal inter-vehicle distance between successive members of an autonomous vehicle platoon. In our approach, calculations of the control algorithm are decentralized, and the data used in the control algorithm is obtained using one sensor per platoon member making the algorithm cost-efficient both computationally and financially. The proposed algorithm was implemented using the Robot Operating System (ROS) and applied to 3D vehicle models in simulations designed to mimic the natural environment in order to demonstrate and evaluate the suitability of the proposed algorithm for demanding and applicable scenarios. We performed meticulous simulations using the ROS framework in conjunction with the gazebo platform. In the proposed approach, the desired inter-vehicle distance between platoon members was successfully kept with a maximum absolute error of 5 m under any given scenario at any given time while maintaining platoon formation and ensuring that no collisions occur among platoon members.

Indexed as

Automated highway systemsAutonomous vehicle platoonsAutonomous vehiclesCommunicationIntelligent transport systemsLongitudinal platoon controlPID

Identifiers

PMID35634117
PMCPMC9137855
OpenAlexW4281296751

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.