Evidence map›Paper›PMID 40292958›Full record

ArticleSensors (Basel, Switzerland)2025

An Identification Method for Road Hypnosis Based on XGBoost-HMM.

Longfei Chen, Chenyang Jiao, Bin Wang, Xiaoyuan Wang, Jingheng Wang, Han Zhang, Junyan Han, Cheng Shen, Kai Feng, Quanzheng Wang and 1 more

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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

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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

11 authors.

Longfei ChenCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.ORCID 0000-0002-0065-1001
Chenyang JiaoCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Bin WangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Xiaoyuan WangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.ORCID 0000-0003-2418-7394
Jingheng WangDepartment of Mathematics, Ohio State University, Columbus, OH 43220, USA.
Han ZhangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Junyan HanCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.ORCID 0000-0003-2186-066X
Cheng ShenCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Kai FengCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.
Quanzheng WangCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.ORCID 0000-0002-5391-8857
Yi LiuCollege of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.

Funding

the New Generation of Information Technology Innovation Project of China University Innova-tion Fund of Ministry of Education No.2023IT209the New Generation of Information Technology Innovation Project of China University Innova-tion Fund of Ministry of Education No.2024HT024
6 · The paper itself

Abstract

Human factors are the most important factor in road traffic crashes. Human-caused traffic crashes can be reduced through the active safety system of vehicles. Road hypnosis is an unconscious driving state caused by the combination of external environmental factors and the driver's psychological state. When drivers fall into a state of road hypnosis, they cannot clearly perceive the surrounding environment and make various reactions in time to complete the driving task, and driving safety is greatly affected. Therefore, road hypnosis identification is of great significance for the active safety of vehicles. A road hypnosis identification model based on XGBoost-Hidden Markov is proposed in this study. Driver data and vehicle data related to road hypnosis are collected through the design and conduct of vehicle driving experiments. Driver data, including eye movement data and EEG data, are collected with eye movement sensors and EEG sensors. A mobile phone with AutoNavi navigation is used as an on-board sensor to collect vehicle speed, acceleration, and other information. Power spectrum density analysis, the sliding window method, and the point-by-point calculation method are used to extract the dynamic characteristics of road hypnosis, respectively. Through normalization and standardization, the key features of the three types of data are integrated into unified feature vectors. Based on XGBoost and the Hidden Markov algorithm, a road hypnotic identification model is constructed. The model is verified and evaluated through visual analysis. The results show that the road hypnosis state can be effectively identified by the model. The extraction of road hypnosis-related features is realized in non-fixed driving routes in this study. A new research idea for road hypnosis and a technical scheme reference for the development of intelligent driving assistance systems are provided, and the life identification ability of the vehicle intelligent cockpit is also improved. It is of great significance for the active safety of vehicles.

Indexed as

Automobile DrivingHypnosisAccidents, TrafficAlgorithmsBoosting Machine Learning AlgorithmsCell PhoneElectroencephalographyEye MovementsHumansMarkov ChainsdriverHMMroad hypnosisstate identificationvehicleXGBoost

Identifiers

PMID40292958
PMCPMC11946432

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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.