Accident; analysis and prevention最新文献

{"title":"A dynamic and stochastic perspective on time headway in traffic oscillations and its implications for traffic safety","authors":"Ying Luo ,&nbsp;Weijie Yu ,&nbsp;Jiaxin Lu ,&nbsp;Yanyan Chen ,&nbsp;Dong Ngoduy","doi":"10.1016/j.aap.2025.108146","DOIUrl":"10.1016/j.aap.2025.108146","url":null,"abstract":"<div><div>Traffic oscillations refer to the alternating patterns of vehicle deceleration and acceleration in congested conditions, which usually create significant safety concerns on freeways. Thus, it is imperative to understand the mechanisms of traffic oscillations and their underlying safety implications. This paper presents a novel approach to exploring the combined effects of dynamic time headway (DTH) and stochasticity on traffic oscillations during car-following. Using high-precision trajectory data, we demonstrate a strong correlation between DTH and stochasticity strength with the power functions of speed. We then extend the car-following model framework that considers both the dynamic characteristics and stochasticity of time headway to investigate the mechanisms of traffic oscillation. The model calibration and validation results demonstrate that our extended model outperforms the original model in terms of trajectory fitting accuracy, successfully replicating the asymmetric driving behavior and the concave growth pattern of speed standard deviation. Building upon this novel perspective, linear stability and safety evaluation are systematically conducted to understand the comprehensive influence of DTH and stochasticity. Our theoretical and numerical experiments show that DTH significantly increases the range of string instability in traffic flow, particularly at low-speed regimes. The influence of the stochasticity on the marginal stability of traffic flow shows a pattern of increasing followed by decreasing tendencies. Also, the combined effect of drivers’ DTH characteristics and stochasticity could expand the rear-end collision risks at low-speed regimes, showing a backward diffusion effect. Our findings further establish the interconnection of traffic oscillations with traffic stability and safety concerns.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108146"},"PeriodicalIF":5.7,"publicationDate":"2025-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144569826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trajectory-based indicators to determine the local character of intersection conflicts: A micro-spatial analysis","authors":"Andreas Hula ,&nbsp;Apostolos Ziakopoulos ,&nbsp;Ángel Losada ,&nbsp;Andrea Schaub ,&nbsp;Peter Saleh ,&nbsp;George Yannis","doi":"10.1016/j.aap.2025.108155","DOIUrl":"10.1016/j.aap.2025.108155","url":null,"abstract":"<div><div>Real world behaviour data is the most reliable reference to assess road safety in a specific road infrastructure context. However, its collection and implementation for road safety research in a rapid and portable manner is still challenging, facing data protection issues and the complexities to set up constant tracking mechanisms with their own power supply. To tackle these limitations, the Mobility Observation Box (MOB) provides a flexible data collection, to be used in subsequent video analysis. Object detection and tracking allow for the derivation of movement trajectories, which in turn allow to derive quantitative indicators of road safety relevant behaviour, namely well-established Surrogate Safety Measures (SSMs), such as Post-Encroachment-Time (PET), Time-to-Closest-Approach (TCA) and Time-to-Collision-(TTC) alongside a number of indicators like maximum speed of two interaction partners, the angle they approach each other in and the minimum distance they had at one point in their interaction. To facilitate potential MOB uses, this study leverages over 51 h of naturalistic video data at a busy Vienna intersection to advance road safety research by (i) employing random parameters binary modelling of the likelihood of critical conflict occurrence and (ii) Gaussian generalized additive spatial modelling to identify key factors influencing the absolute values of conflict angles on critical conflicts only. Within the examined intersection, specific speed and acceleration effects were determined, together with the respective heterogeneity-in-means, as well as significant categorical effects of different road user types. All road user types were ultimately less likely to be involved in safety–critical conflicts compared to cars in both leading (firstly detected) and following (secondly detected) roles, with the exception of cyclists in the leading role. Within the micro-spatial analysis, the kinematic parameters of the second road user only (speed, max acceleration and max deceleration), the duration of the interaction as well as intersection-specific local effects related to the position of the leading road user were all found to influence the transformed absolute value of the angles of critical conflicts.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108155"},"PeriodicalIF":5.7,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548571","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic evaluation of emergency lane occupation based on an improved driving risk field model","authors":"Chuzhao Li ,&nbsp;Yangming Zhou ,&nbsp;Zhiyu Tao ,&nbsp;Guofa Li ,&nbsp;Zhou Wu","doi":"10.1016/j.aap.2025.108143","DOIUrl":"10.1016/j.aap.2025.108143","url":null,"abstract":"<div><div>In China, the frequent occupation of emergency lanes on highways significantly affects traffic efficiency and safety. This study collected data from six highway segments and extracted lane-changing (LC) behavior. A logistic regression model was employed to identify key factors influencing emergency lane changes (ELC). An improved driving risk field model was developed to assess risks during the LC process, integrating the heading angle of the obstacle vehicles, speeds of both the main and obstacle vehicles, as well as the filtering effect of lane lines. Compared to three existing risk field models, our model provides a more accurate assessment of collision risks during lane changes. This study revealed that the primary reasons for ELC are driven by the pursuit of speed and driving space, influenced by the speed and relative distance of surrounding vehicles, and the intention to overtake. Moreover, ELC is characterized by reduced lateral speed and acceleration, as well as a lower minimum time-to-collision (TTC), while exhibiting higher acceptable risks. Risk variation trends during lane changes from emergency to normal lanes include conservative, general, and aggressive types, while changes from normal to emergency lanes feature only conservative and general types. Additionally, ELC significantly negatively impacts following vehicles in the target lane after LC, leading to greater lateral displacement, more significant longitudinal deceleration, and heightened risk levels.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108143"},"PeriodicalIF":5.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A secure and privacy-preserving authentication framework for Connected and Autonomous Vehicles based on DRG-PBFT and zero-knowledge proof","authors":"Anjum Mohd Aslam,&nbsp;Aditya Bhardwaj,&nbsp;Rajat Chaudhary","doi":"10.1016/j.aap.2025.108145","DOIUrl":"10.1016/j.aap.2025.108145","url":null,"abstract":"<div><div>Connected and Autonomous Vehicles (CAVs) are pivotal to advancing Intelligent Transportation Systems (ITS) but introduce significant security and privacy challenges, particularly in dynamic environments requiring real-time data exchange. The existing security measures and consensus mechanisms, such as Practical Byzantine Fault Tolerance (PBFT), are susceptible to various attacks, including identity forgery, unauthorized access, and compromised safety testing, and suffer from scalability and latency issues. This study addresses these challenges by proposing a Dynamic Reputation Grouping-based PBFT (DRG-PBFT) approach, integrated with Simulation Extractable Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (SE-ZK-SNARKS). The proposed framework leverages reputation-based dynamic grouping to enhance consensus efficiency and reduce communication overhead. SE-ZK-SNARKS provide anonymity and privacy-preserving identity authentication, enabling CAVs to prove their legitimacy without revealing sensitive information. The proposed framework has been validated through extensive simulations using the NS-3 network simulator integrated with blockchain. The simulation results demonstrate that our proposed approach outperforms existing methods, achieving reduced consensus latency, communication overhead, authentication time and improved throughput. Overall, the findings and methodologies presented in this study address critical challenges in securing CAV communications while maintaining scalability and efficiency and can serve as a valuable reference for researchers and practitioners aiming to improve the safety and reliability of CAVs in real-time environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108145"},"PeriodicalIF":5.7,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144535838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Human-Centric explanations for users in automated Vehicles: A systematic review","authors":"Zishuo Zhu ,&nbsp;Xiaomeng Li ,&nbsp;Patricia Delhomme ,&nbsp;Ronald Schroeter ,&nbsp;Sebastien Glaser ,&nbsp;Andry Rakotonirainy","doi":"10.1016/j.aap.2025.108152","DOIUrl":"10.1016/j.aap.2025.108152","url":null,"abstract":"<div><h3>Background</h3><div>The decision-making processes of automated vehicles (AVs) can confuse users and reduce trust, highlighting the need for clear and human-centric explanations. Such explanations can help users understand AV actions, facilitate smooth control transitions and enhance transparency, acceptance, and trust. Critically, such explanations could improve situational awareness and support timely, appropriate human responses, thereby reducing the risk of misuse, unexpected automated decisions, and delayed reactions in safety–critical scenarios. However, current literature offers limited insight into how different types of explanations impact drivers in diverse scenarios and the methods for evaluating their quality. This paper systematically reviews what, when and how to provide human-centric explanations in AV contexts.</div></div><div><h3>Methods</h3><div>The systematic review followed PRISMA guidelines, and covered five databases—Scopus, Web of Science, IEEE Xplore, TRID, and Semantic Scholar—from 2000 to April 2024. Out of 266 identified articles, 59 met the inclusion criteria.</div></div><div><h3>Results</h3><div>Providing a detailed content explanation following AV’s driving actions in real time does not always increase user trust and acceptance. Explanations that clarify the reasoning behind actions are more effective than those merely describing actions. Providing explanations before action is recommended, though the optimal timing remains uncertain. Multimodal explanations (visual and audio) are most effective when each mode conveys unique information; otherwise, visual-only explanations are preferred. The narrative perspective (first-person vs. third-person) also impacts user trust differently across scenarios.</div></div><div><h3>Conclusions</h3><div>The review underscores the importance of tailoring human-centric explanations to specific driving contexts. Future research should address explanation length, timing, and modality coordination and focus on real-world studies to enhance generalisability. These insights are vital for advancing the research of human-centric explanations in AV systems and fostering safer, more trustworthy human-vehicle interactions, ultimately reducing the risk of inappropriate reactions, delayed responses, or user error in traffic settings.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108152"},"PeriodicalIF":5.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning salient representation of crashes and near-crashes using supervised contrastive variational autoencoder","authors":"Boyu Jiang ,&nbsp;Feng Guo","doi":"10.1016/j.aap.2025.108148","DOIUrl":"10.1016/j.aap.2025.108148","url":null,"abstract":"<div><div>Models capable of learning representations that are salient in safety–critical events (SCEs; including crashes and near-crashes) are crucial for road safety. This study proposes a novel deep learning model, the supervised contrastive variational autoencoder (scVAE), that incorporates supervised contrastive learning methods into the variational autoencoder (VAE) framework. By leveraging two distinct encoders, the scVAE encourages the salient latent variables to be discriminative, capturing the unique representations of SCEs while being regulated by the response variable to focus on the most relevant representations for accurate clustering. Through application on the Second Strategic Highway Research Program (SHRP 2) Naturalistic Driving Study kinematic datasets, we demonstrated the effectiveness of the scVAE in learning salient representations that enable improved clustering compared to alternative models. Quantitative analysis revealed clear clustering patterns in the learned salient representation space, facilitating downstream tasks such as generating samples, denoising, and prediction. The proposed approach of combining contrastive and supervised learning can be scaled to other model frameworks and data modalities, offering a promising direction for learning-enhanced representations that cater to tasks of interest. The study findings highlight the contributions of scVAE to traffic safety, offering enhanced capabilities for driving scenario generation and SCE detection.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108148"},"PeriodicalIF":5.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safety and efficiency-oriented adaptive strategy controls for connected and automated vehicles in unstable communication environment","authors":"Yangzhen Zhao ,&nbsp;Xuedong Hua ,&nbsp;Weijie Yu ,&nbsp;Wenxie Lin ,&nbsp;Wei Wang ,&nbsp;Qihao Zhou","doi":"10.1016/j.aap.2025.108121","DOIUrl":"10.1016/j.aap.2025.108121","url":null,"abstract":"<div><div>Connected and automated vehicle (CAV) has demonstrated significant potential in enhancing traffic safety, efficiency, and environmental sustainability in the premise of high-quality communication environment. However, attributed to unexpected disturbances in real life, vehicular communication often suffers from unstable information transmission that lowers the performance of CAV platooning. Despite this drawback, existing research lacks sufficient attention on CAV platooning in the presence of unstable communication environment (UCE) and rarely addresses the response strategies. To fill these research gaps, this study integrates dynamic communication topologies into multi-class controls of automated and/or connected vehicles in response to various data abnormalities, based on simulation data generated from the modified intelligent driver model (IDM) and the communication disturbance framework designed to emulate UCE. The impact of UCE on CAV platooning performance is evaluated in terms of traffic safety and efficiency. The study introduces five adaptive dynamic strategy controls (ADSCs) and three hybrid switching ADSCs to enhance platoon performance under varying UCE conditions, along with recommendations on applicable ADSCs for maintaining the platoon performance under different UCE scenarios. Also, our sensitivity analysis of attenuation model parameters identifies optimal configurations for the development of ADSCs. Results show that continuous UCE (CUCE) poses greater safety challenges compared to intermittent UCE (IUCE), with combined IUCE being the most detrimental. Scenarios involving aggressive acceleration behavior significantly increase the collision risk and speed oscillation within the platoon. The proposed hybrid switching ADSCs significantly improve CAV platoon performance, offering resilient and adaptive vehicle control strategies to smooth traffic flow under disturbances.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108121"},"PeriodicalIF":5.7,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144517601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Directional vibrotactile takeover requests on a wrist-worn device: effects of age, pattern type, and urgency in automated driving","authors":"Wei-Hsiang Lo,&nbsp;Gaojian Huang","doi":"10.1016/j.aap.2025.108093","DOIUrl":"10.1016/j.aap.2025.108093","url":null,"abstract":"<div><div>Drivers are still required to perform the takeover task in highly automated vehicles. This task, which is cognitively and physically demanding, may present challenges for older adults due to general age-related declines in perception and cognition. Tactile modalities that may not be occupied by many non-driving-related tasks could serve as a potential solution for delivering takeover requests. Among these, directional vibrotactile stimuli presented via a wrist-worn device represent a promising approach. However, the effects of the two common types of directional vibrotactile patterns, dynamic patterns that vibrate sequentially at different locations and static patterns that vibrate at fixed locations, are still unknown. Therefore, this study aimed to investigate the effect of age (younger and older adults), vibrotactile pattern types (Baseline, Full-Dynamic, Semi-Dynamic, and Static), and interpulse interval (shorter (300 ms) and longer (800 ms)) on takeover performance. Forty participants (20 younger and 20 older adults) were engaged in the SAE Level 3 driving simulator study. Overall, Static and Baseline patterns were associated with faster reaction and takeover times and were perceived as more useful and satisfactory compared to the Full-Dynamic and Semi-Dynamic patterns. Shorter interpulse intervals (300 ms) for vibrotactile takeover requests resulted in better takeover performance, as indicated by shorter reaction and takeover times compared to longer interpulse intervals (800 ms). Finally, younger adults reacted faster to vibrotactile takeover requests than older adults did. The findings from the current study may inform the design of human–machine interfaces on wearable devices for next-generation automated vehicles.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108093"},"PeriodicalIF":5.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering of freeway cut-in scenarios for automated vehicle development considering data dimensionality and imbalance","authors":"Penghui Li ,&nbsp;Qianru Dong ,&nbsp;Xiangjun Zhao ,&nbsp;Chao Lu ,&nbsp;Mengxia Hu ,&nbsp;Xuedong Yan ,&nbsp;Chunjiao Dong","doi":"10.1016/j.aap.2025.108151","DOIUrl":"10.1016/j.aap.2025.108151","url":null,"abstract":"<div><div>Representative driving scenarios derived by clustering of naturalistic driving data are guidelines for the function definition and algorithm development of automated vehicle. However, current clustering methods struggle with data dimensionality and imbalance, leading to significant biases. To tackle these issues, this study proposed a novel two-layer self-adaptive multiprototype-based competitive learning algorithm, and implemented it in clustering of freeway cut-in scenarios. Firstly, the extracted cut-in segments from naturalistic driving data included environmental, static, and dynamic vehicle elements, composed of discrete, continuous, and time series variables, posing a challenge in multi-dimensional parameter clustering. To tackle this, we utilized the K-medoids clustering method, based on dynamic time warping distance, to cluster variables such as cut-in vehicle velocity, converting them into discrete variables and applying one-hot encoding for easier clustering distance calculations. Secondly, to address the imbalance issue where minority sample categories were absorbed into majority types in naturalistic driving data clustering, we employed a multi-prototype clustering method in the second layer. Each cluster was represented by one or more sub-clusters to ensure adequate representation of minority clusters. Moreover, the inclusion of adaptive competitive learning allowed the algorithm to autonomously determine the optimal number of clusters, eliminating the need for manual parameter tuning. Consequently, the proposed algorithm produced eleven representative freeway cut-in scenarios from 2415 segments, with a better clustering goodness than the other traditional clustering methods. Moreover, four representative cut-in scenarios were frequently appeared in the dataset and commonly recognized by previous studies, whilst seven were rare in the dataset but common in real-world driving circumstances, such as at night, adverse weather conditions, and commercial vehicle cut-in scenarios. These findings suggest that the proposed clustering method effectively addresses the challenges of dimensionality and imbalance, indicating its potential for wide application in constructing representative scenarios for automated vehicles development.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108151"},"PeriodicalIF":5.7,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An interpretable stacking ensemble learning model for visual-manual distraction level classification for in-vehicle interactions","authors":"Yahui Wang ,&nbsp;Zhoushuo Liang ,&nbsp;Pengfei Tian ,&nbsp;Yue He","doi":"10.1016/j.aap.2025.108150","DOIUrl":"10.1016/j.aap.2025.108150","url":null,"abstract":"<div><div>Recognizing the level of driver distraction during the execution of secondary tasks within the intelligent cockpit is crucial for ensuring a seamless interaction between human drivers and intelligent vehicle systems. To address this issue, this paper proposes a framework for recognizing driver distraction levels that integrates clustering, classification, and interpretability. First, Feature Selection with Optimal Graph(SF²SOG) is employed to identify discriminative features from the data facilitating dimensionality reduction. Following this, Agglomerative Clustering are employed to classify the gathered unlabeled data on driving distraction behaviors into three distinct categories. Additionally, a heuristic-based stacking ensemble model is introduced to identify the levels of driver distraction, using the factors that influence these levels as input parameters for classification. To improve the effectiveness of the stacking model, three diverse classifiers adaptive boosting (AdaBoost)、random forest (RF) and extreme gradient boosting (XGBoost) are selected to serve as the base models, while a relatively simple yet accurate model logistic regression (LR) is used as the <em>meta</em>-classifier. Finally, Shapley Additive exPlanations (SHAP) is employed for interpretability analysis. Notably, heuristic-based stacking ensemble model achieved a commendable accuracy rate of 96.25%, highlighting its significant advantage. Further analysis shows that higher maximum pupil diameter (maxPD) and mean pupil diameter (meanPD) indicate increased distraction, while greater glance frequency and lane deviation reflect reduced situational awareness and control. These findings are crucial for reducing accidents and enhancing driving safety in the era of intelligent vehicles.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"220 ","pages":"Article 108150"},"PeriodicalIF":5.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144489568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}