An interpretable prediction model of illegal running into the opposite lane on curve sections of two-lane rural roads from drivers’ visual perceptions

Li, He; Yu, Bo; Chen, Yu-Ren; Bao, Shan; Gao, Kun; Kong, You

doi:10.1016/j.aap.2023.107066

Cited by 13 publications

(3 citation statements)

References 41 publications

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“…Xiang used the satisfaction model of visual and auditory perception combined with principal component analysis, correlation analysis, and linear regression to realize the visual perception analysis of urban green space [15]. Li collected autonomous driving data from the visual perceptions of drivers and proposed an IROL interpretable prediction model for curved segments of two-lane rural roads [16]. With the continuous development of portable devices for collecting physiological electrical signals, the quantification of visual perception is gradually being carried out from the perspective of physiological electrical signals.…”

Section: Related Workmentioning

confidence: 99%

High-Density Functional Near-Infrared Spectroscopy and Machine Learning for Visual Perception Quantification

Xiao,

Li,

Zhou

et al. 2023

Sensors

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The main application scenario for wearable sensors involves the generation of data and monitoring metrics. fNIRS (functional near-infrared spectroscopy) allows the nonintrusive monitoring of human visual perception. The quantification of visual perception by fNIRS facilitates applications in engineering-related fields. This study designed a set of experimental procedures to effectively induce visible alterations and to quantify visual perception in conjunction with the acquisition of Hbt (total hemoglobin), Hb (hemoglobin), and HbO2 (oxygenated hemoglobin) data obtained from HfNIRS (high-density functional near-infrared spectroscopy). Volunteers completed the visual task separately in response to different visible changes in the simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visual difference, and the time point of the task change. This study consisted of one simulated scene, two visual variations, and four visual tasks. The simulation scene featured a car driving location. The visible change suggested that the brightness and saturation of the car operator interface would change. The visual task represented the completion of the layout, color, design, and information questions answered in response to the visible change. This study collected data from 29 volunteers. The volunteers completed the visual task separately in response to different visual changes in the same simulated scene. HfNIRS recorded the changes in Hbt, Hb, and HbO2 during the study, the time point of the visible difference, and the time point of the task change. The data analysis methods in this study comprised a combination of channel dimensionality reduction, feature extraction, task classification, and score correlation. Channel downscaling: This study used the data of 15 channels in HfNIRS to calculate the mutual information between different channels to set a threshold, and to retain the data of the channels that were higher than those of the mutual information. Feature extraction: The statistics derived from the visual task, including time, mean, median, variance, extreme variance, kurtosis, bias, information entropy, and approximate entropy were computed. Task classification: This study used the KNN (K-Nearest Neighbors) algorithm to classify different visual tasks and to calculate the accuracy, precision, recall, and F1 scores. Scoring correlation: This study matched the visual task scores with the fluctuations of Hbt, Hb, and HbO2 and observed the changes in Hbt, Hb, and HbO2 under different scoring levels. Mutual information was used to downscale the channels, and seven channels were retained for analysis under each visual task. The average accuracy was 96.3% ± 1.99%; the samples that correctly classified the visual task accounted for 96.3% of the total; and the classification accuracy was high. By analyzing the correlation between the scores on different visual tasks and the fluctuations of Hbt, Hb, and HbO2, it was found that the higher the score, the more obvious, significant, and higher the fluctuations of Hbt, Hb, and HbO2. Experiments found that changes in visual perception triggered changes in Hbt, Hb, and HbO2. HfNIRS combined with Hbt, Hb, and HbO2 recorded by machine learning algorithms can effectively quantify visual perception. However, the related research in this paper still needs to be further refined, and the mathematical relationship between HfNIRS and visual perception needs to be further explored to realize the quantitative study of subjective and objective visual perception supported by the mathematical relationship.

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Section: Related Workmentioning

confidence: 99%

High-Density Functional Near-Infrared Spectroscopy and Machine Learning for Visual Perception Quantification

Xiao,

Li,

Zhou

et al. 2023

Sensors

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“…As a typical technique in the domain of machine learning, the XGBoost model has gained recognition as a powerful algorithm for classification and regression tasks. In traffic safety research, XGBoost has been utilized for its efficiency and accuracy in predicting road accident severity and identifying critical incident-related features [15,16]. Additionally, it is worth noting that some previous studies have utilized the XGBoost model to evaluate highway crash injury severities, although these studies may not specifically focus on non-divided two-way highway and railway crashes.…”

Section: Introductionmentioning

confidence: 99%

Traffic Safety Assessment and Injury Severity Analysis for Undivided Two-Way Highway–Rail Grade Crossings

Ren,

Xu,

Zhou

et al. 2024

Mathematics

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The safety and reliability of undivided two-way highway–rail grade crossings (HRGCs) are of paramount importance in transportation systems. Utilizing crash data from the Federal Railroad Administration between 2020 and 2021, this study aims to predict crash injury severity outcomes and investigate various factors influencing injury severities. The χ2 test was first used to select variables that were significantly associated with injury outcomes. By employing the eXtreme Gradient Boosting (XGBoost) model and interpretable SHapley Additive exPlanations (SHAP), a cross-category safety assessment that offers an evidence-based hierarchy and statistical inference of risk factors associated with crashes, crossings, vehicles, drivers, and environment was provided for killed, injured, and uninjured outcomes. Some significant predictors overlapped between the killed and injured models, such as old driver, driver was in vehicle, main track, went around the gate, adverse crossing surface, and truck, while the other different significant factors revealed that the model could distinguish between different severity levels. Additionally, the results suggested that the model has varying performances in predicting different injury severities, with the killed model having the highest accuracy of 93.36%. The SHAP dependency plots for the top three features also ensure reliable predictions and inform potential interventions aimed at strengthening traffic safety and risk management practices, such as enhanced warning systems and targeted educational campaigns for older drivers.

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“…Emerging technological advancements in sustainable transportation, targeting intelligent and environmentally friendly urban traffic, are advancing globally to diminish air pollution, improve road safety, alleviate traffic congestion, and enhance travel convenience [1][2][3][4][5]. Automated Vehicles (AVs) represent one of these solutions, and they are purported to create new opportunities for achieving green, safe, and smart mobility initiatives [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

The Environmental Impacts of Automated Vehicles on Parking: A Systematic Review

Kong,

Ou,

Chen

et al. 2023

Sustainability

Self Cite

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Automated Vehicles (AVs) can drop off passengers at predetermined destinations and relocate to less expensive, remote parking facilities, which offers the potential to repurpose valuable urban land near activity centers for alternative uses beyond vehicle storage. While some researchers believe AVs are the core element to solving parking problems, relieving urban land use, and enabling low-emission travel, others contend that AVs could incentivize increased Vehicles Miles Traveled (VMT) and exacerbate congestion. To bridge these disparate perspectives, this study endeavors to elucidate the environmental ramifications of AVs on parking through a comprehensive literature review. Based on an initial sample of 299 retrieved papers, 52 studies were selected as the result of the selection criteria detailed in the paper. The selected papers were categorized into five gradual parts to answer the raised research questions. As a principal finding of this study, our research provides city planners, traffic operators, and scholars with full-picture insights and trustworthy guidance, emphasizing the pivotal role of AVs in deciphering the sustainable impact on the urban environment.

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An interpretable prediction model of illegal running into the opposite lane on curve sections of two-lane rural roads from drivers’ visual perceptions

Cited by 13 publications

References 41 publications

High-Density Functional Near-Infrared Spectroscopy and Machine Learning for Visual Perception Quantification

High-Density Functional Near-Infrared Spectroscopy and Machine Learning for Visual Perception Quantification

Traffic Safety Assessment and Injury Severity Analysis for Undivided Two-Way Highway–Rail Grade Crossings

The Environmental Impacts of Automated Vehicles on Parking: A Systematic Review

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