Evidence map›Paper›PMID 38676119›Full record

ArticleSensors (Basel, Switzerland)2024

Optimizing Lane Departure Warning System towards AI-Centered Autonomous Vehicles.

Siwoo Jeong, Jonghyeon Ko, Sukki Lee, Jihoon Kang, Yeni Kim, Soon Yong Park, Sungchul Mun

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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, top 95% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

7 authors at 5 institutions in 1 country.

Siwoo JeongDepartment of Sports Rehabilitation Medicine, Kyungil University, Gyeongsan 38428, Republic of Korea.ORCID 0000-0001-7081-6900
Jonghyeon KoConvergence Institute of Human Data Technology, Jeonju University, Jeonju 55069, Republic of Korea.ORCID 0000-0002-8322-8056
Sukki LeeKorea Institute of Civil Engineering and Building Technology, Goyang 10223, Republic of Korea.
Jihoon KangDepartment of Electronic Engineering, Inha University, Incheon 22201, Republic of Korea.
Yeni KimDepartment of Industrial Engineering, Jeonju University, Jeonju 55069, Republic of Korea.
Soon Yong ParkDivision of Urban Transportation Research, Seoul Institute, Seoul 06756, Republic of Korea.
Sungchul MunConvergence Institute of Human Data Technology, Jeonju University, Jeonju 55069, Republic of Korea.ORCID 0000-0003-4596-9889
Jeonju University · KRInha University · KRKorea Institute of Civil Engineering and Building Technology · KRKyungil University · KRThe Seoul Institute · KR

Funding

Inha University Research grantKorean National Police Agency 092021C26S02000National Research Foundation of Korea RS-2023-00247958
6 · The paper itself

Abstract

The operational efficacy of lane departure warning systems (LDWS) in autonomous vehicles is critically influenced by the retro-reflectivity of road markings, which varies with environmental wear and weather conditions. This study investigated how changes in road marking retro-reflectivity, due to factors such as weather and physical wear, impact the performance of LDWS. The study was conducted at the Yeoncheon SOC Demonstration Research Center, where various weather scenarios, including rainfall and transitions between day and night lighting, were simulated. We applied controlled wear to white, yellow, and blue road markings and measured their retro-reflectivity at multiple stages of degradation. Our methods included rigorous testing of the LDWS's recognition rates under these diverse environmental conditions. Our results showed that higher retro-reflectivity levels significantly improve the detection capability of LDWS, particularly in adverse weather conditions. Additionally, the study led to the development of a simulation framework for analyzing the cost-effectiveness of road marking maintenance strategies. This framework aims to align maintenance costs with the safety requirements of autonomous vehicles. The findings highlight the need for revising current road marking guidelines to accommodate the advanced sensor-based needs of autonomous driving systems. By enhancing retro-reflectivity standards, the study suggests a path towards optimizing road safety in the age of autonomous vehicles.

Indexed as

autonomous vehicleenvironmental conditionsLDWSretro-reflectivityroad markingssimulation framework

Identifiers

PMID38676119
PMCPMC11054872
OpenAlexW4394809193

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.