Accident; analysis and prevention最新文献_第3页

A doubly robust estimation framework to quantify potential bias in linked crash-EMS-trauma data with multi-cohort overlap 一个双重稳健的估计框架，量化与多队列重叠相关的碰撞- ems -创伤数据的潜在偏差。

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-10 DOI: 10.1016/j.aap.2025.108380

Sajjad Karimi, Robert Kluger

{"title":"A doubly robust estimation framework to quantify potential bias in linked crash-EMS-trauma data with multi-cohort overlap","authors":"Sajjad Karimi, Robert Kluger","doi":"10.1016/j.aap.2025.108380","DOIUrl":"10.1016/j.aap.2025.108380","url":null,"abstract":"<div><div>Reliable estimation of injury severity is essential for informing trauma care, evaluating crash interventions, and guiding EMS resource allocation; however, analyses based on linked administrative datasets are often compromised by incomplete linkage and selection bias. This study employs a doubly robust estimation framework to address potential bias in injury severity estimation when integrating multiple datasets. Using Augmented Inverse Probability Weighting (AIPW), we adjust for selection bias introduced by incomplete linkage while improving robustness to misspecification in either the selection or outcome model. Using data from a multi-source linkage of crash, EMS, and trauma records, we estimate the Injury Severity Score (ISS) under three approaches: naïve complete-case analysis, inverse probability weighting (IPW), and AIPW. The naïve approach yielded a mean ISS of 13.52, while both IPW (10.86) and AIPW (10.93) provided adjusted estimates accounting for selection. Subgroup analyses revealed substantial differences in effect size and direction between models. For instance, the impact of male gender on ISS was estimated at 3.98 in AIPW versus 2.22 in naïve analysis. Similarly, secondary collisions and frontage-road crashes showed ISS increases exceeding 10 points under AIPW, compared to considerably lower naïve estimates. Several protective factors, including airbag deployment and crash setting, also demonstrated stronger effects when adjusted for bias. Our results demonstrate that traditional analyses of linked data may underestimate or misstate key risk and protective associations. The proposed AIPW framework offers a practical, statistically rigorous solution for producing population-level inferences in injury severity research using linked administrative data.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108380"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145951060","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}

引用次数: 0

Evaluation of the safety improvement effects and adaptability of speed limit measures on downhill curved sections of mountainous freeways 山地高速公路下坡弯道限速措施安全改善效果及适应性评价

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-21 DOI: 10.1016/j.aap.2026.108416

Yesihati Azati , Xuesong Wang , Xiaohan Yang , Syed Zaier Zaidi , Mohammed Quddus

{"title":"Evaluation of the safety improvement effects and adaptability of speed limit measures on downhill curved sections of mountainous freeways","authors":"Yesihati Azati , Xuesong Wang , Xiaohan Yang , Syed Zaier Zaidi , Mohammed Quddus","doi":"10.1016/j.aap.2026.108416","DOIUrl":"10.1016/j.aap.2026.108416","url":null,"abstract":"<div><div>Downhill curved segments of mountainous freeways are high-risk locations due to the combined effects of longitudinal potential energy and lateral centrifugal forces, and speed limit optimization is a key measure for mitigating crash risk on such segments. However, conventional speed limit settings often rely on uniform standards and fail to adequately account for the combined influences of roadway geometry, slope, traffic conditions, and weather, resulting in heterogeneous safety outcomes across different road environments. This study employs a Causal Forest model with explicitly incorporated quarterly time variables, complemented by a Difference-in-Differences (DID) model, to evaluate the safety effectiveness of speed limit optimization on downhill curved segments of the Guidu Freeway. Empirical results indicate that speed limit measures significantly reduce crash risk, with the Causal Forest model estimating an average treatment effect (ATE) of −0.203 (95% confidence interval: −0.213 to −0.193), demonstrating high precision and robustness and outperforming the DID model in estimation stability. Heterogeneity analysis further reveals that the safety benefits are most pronounced on segments with relatively mild horizontal curvature, low curvature variability, and moderate downhill slopes, as well as under moderate traffic volumes (approximately 7,500–9,000 vehicles/day), while higher truck proportions weaken the effectiveness of the measures. In contrast, fog frequency exerts a relatively limited moderating effect, as treatment effects remain negative across different fog conditions. Overall, the findings confirm that speed limit optimization can substantially improve safety on downhill curved segments of mountainous freeways and highlight the importance of accounting for roadway geometry and traffic composition when designing targeted speed management strategies.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108416"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023726","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}

引用次数: 0

A ‘Cluster-then-Estimate’ Natural Language Processing (NLP) Approach for Classifying Maritime Incident Severity Based on Textual Descriptions 基于文本描述的海事事件严重程度分类的“聚类-估计”自然语言处理（NLP）方法

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-22 DOI: 10.1016/j.aap.2026.108413

Tianyi Chen , Maohan Liang , Wei Siong Lee , Yutong Cai , Qiang Meng

{"title":"A ‘Cluster-then-Estimate’ Natural Language Processing (NLP) Approach for Classifying Maritime Incident Severity Based on Textual Descriptions","authors":"Tianyi Chen , Maohan Liang , Wei Siong Lee , Yutong Cai , Qiang Meng","doi":"10.1016/j.aap.2026.108413","DOIUrl":"10.1016/j.aap.2026.108413","url":null,"abstract":"<div><div>Textual incident description is a vital source for understanding the severity of maritime incident. In the maritime industry, relevant authorities and companies typically rely on manual methods to estimate incident severity based on textual descriptions. However, manual estimation is less efficient for assessing vessels’ operational risk or managing historical incident archives, where a large volume of incidents is involved. Therefore, this study proposes a ‘cluster-then-estimate’ approach which uses Natural Language Processing (NLP) techniques to automatically estimate the severity level of incidents based upon their textual descriptions. In the proposed approach, Latent Dirichlet Allocation (LDA) is used to group the preprocessed textual descriptions into multiple clusters, with each cluster representing an incident type. Then, Bidirectional Encoder Representation from Transformers (BERT) model is fine-tuned for each cluster to estimate the incident severity based on the descriptions. This study introduces the detailed training schedule for the proposed approach. In the case study, a total of 22,458 incidents, categorized into three incident levels as per the extent of life loss and property damage, are used to train and validate the proposed approach. The proposed approach is compared to several state-of-the-art baseline models. The comparison demonstrates the superior performance of the proposed approach in accurately estimating the severity level of incidents. It manifests that the ’cluster-then-estimate’ strategy effectively leverages the strengths of BERT to further enhance its estimation capability. To the best of our knowledge, the proposed approach is one of the first to adopt NLP techniques for incident severity estimation based on textual descriptions, which offers practical value for improving incident assessment and decision-making in the maritime industry.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108413"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023727","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}

引用次数: 0

Classifying experienced male drivers’ mental workload on freeway ramps based on heart rate and speed measurements: A real-vehicle experiment 基于心率和速度测量的经验丰富的男性驾驶员在高速公路坡道上的心理负荷分类：一个实车实验。

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-20 DOI: 10.1016/j.aap.2026.108415

Jie Wang , Lu Chen , Quankang Zhu , Shijian He , Amjad Pervez

{"title":"Classifying experienced male drivers’ mental workload on freeway ramps based on heart rate and speed measurements: A real-vehicle experiment","authors":"Jie Wang , Lu Chen , Quankang Zhu , Shijian He , Amjad Pervez","doi":"10.1016/j.aap.2026.108415","DOIUrl":"10.1016/j.aap.2026.108415","url":null,"abstract":"<div><div>Freeway ramps are recognized as high-risk segments of the road network due to their geometric complexity and dynamic traffic demands. This study investigates drivers’ mental workload in ramp areas by integrating psycho-physiological responses, specifically heart rate growth (HRG), with vehicle kinematic data, including speed and acceleration. Data were collected through real-world driving experiments from 32 experienced male drivers (aged 30–50 years) under both daytime and nighttime conditions. The findings revealed that HRG values were significantly higher at night, indicating increased cognitive stress in low-light conditions. In addition, the study identified a strong linear relationship between HRG and speed across all scenarios, indicating that increased speed is closely associated with higher mental workload. The relationship between HRG and acceleration followed a three-phase pattern, with sharp HRG changes at both low and high acceleration levels, and more stable responses within the mid-range. Based on these relationships, a classification framework was developed to categorize experienced male drivers’ mental workload into three workload categories (Class 1, Class 2, and Class 3) using joint thresholds of HRG, speed, and acceleration. These findings provide a data-driven basis for identifying cognitively demanding ramp segments and inform the design of adaptive speed guidance systems, real-time driver monitoring technologies, and ramp infrastructure improvements.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108415"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146017013","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}

引用次数: 0

Safety-oriented facility design and operation management for transportation hub station 面向安全的交通枢纽站设施设计与运行管理。

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-18 DOI: 10.1016/j.aap.2025.108388

Yixin Shen , Hongfei Jia , Xin Ye , S.M. Lo , Biao He

{"title":"Safety-oriented facility design and operation management for transportation hub station","authors":"Yixin Shen , Hongfei Jia , Xin Ye , S.M. Lo , Biao He","doi":"10.1016/j.aap.2025.108388","DOIUrl":"10.1016/j.aap.2025.108388","url":null,"abstract":"<div><div>Given the high efficiency and punctuality, transportation hub station are widely used by citizens, travelers daily. The large volume of passengers tends to cause overcrowding in transportation hub stations. Therefore, passenger movement efficiency has been a great concern for station designers, engineers, and facility managers. As the main facility connecting different floors in multilevel metro station, passengers’ movement on the vertical pedestrian transit facilities including stairs and escalators are critical for passengers’ safety and efficiency. To minimize passenger crowding and improve passenger movement efficiency, this study analysed the factors affecting passenger flow on vertical pedestrian transit facility and derived useful insights. By investigating the efficiency of passenger movement on the platform, influencing factors including the speed of escalator, passengers’ willingness to choose the stairs to move up floor levels, the layout and length of the mills barrier were explored. Furthermore, a safety-oriented evacuation layout was also detailed in the study. The study of the mills barrier revealed that a mills barrier placed between a staircase and an escalator promoted passenger efficiency. Moreover, a mills barrier length of 1 or 1.5 m is recommended. For the guidance strategy on the metro platform, the effect of passengers’ willingness to choose the stairs to move up on passenger efficiency was also investigated. Results indicated that passenger dwelling time decreased with an increasing proportion of passengers choosing the stairs. The suggested proportion of passengers choosing the stairs is 30%–40%, which effectively improve passenger efficiency. For the fire evacuation in transportation hub station, the removable facilities near the bottleneck point should be planned decently to be removed with the fastest speed, that will effectively speed up the evacuation process. The results are expected to be useful for designers, engineers, and facility managers.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108388"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146002796","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}

引用次数: 0

DrowsyDG-Phys: Generalizable driver drowsiness estimation in conditional automated vehicles using physiological signals 基于生理信号的有条件自动驾驶车辆驾驶员困倦估计

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-16 DOI: 10.1016/j.aap.2026.108407

Jiyao Wang , Wenbo Li , Zhenyu Wang , Suzan Ayas , Birsen Donmez , Dengbo He , Kaishun Wu

{"title":"DrowsyDG-Phys: Generalizable driver drowsiness estimation in conditional automated vehicles using physiological signals","authors":"Jiyao Wang , Wenbo Li , Zhenyu Wang , Suzan Ayas , Birsen Donmez , Dengbo He , Kaishun Wu","doi":"10.1016/j.aap.2026.108407","DOIUrl":"10.1016/j.aap.2026.108407","url":null,"abstract":"<div><div>Driver drowsiness is one of the leading causes of crashes, injuries, and fatalities on the road. Traditional drowsiness detection models relied on manually extracted physiological features processed through machine learning algorithms. However, these methods lacked flexibility and robustness across diverse real-world conditions. Although recent advances in deep learning have improved detection accuracy through automated feature extraction based on larger learnable parameter space, the generalization of existing models is still limited due to domain shifts. In this study, we proposed <strong>DrowsyDG-Phys</strong>, a novel domain generalization (DG) framework for driver drowsiness detection using three physiological signals (i.e., electrocardiogram, electrodermal activity, and respiration signals) that can be measured by in-vehicle or wearable sensors. Our approach introduced a backbone network for explicit time and frequency domain feature learning. In addition, our approach integrated three novel loss functions: a prior knowledge-based contrastive regularization for robustness, a feature centralization loss to promote generalization in heterogeneities, and a novel loss function to align drowsiness assessment criteria. Finally, we established a multi-source DG benchmark and evaluated our model on three existing datasets and a self-collected dataset involving 60 participants in a simulated SAE Level-3 driving scenario. Our proposed DrowsyDG-Phys achieves 78.5% accuracy on the DG protocol, as well as 88.4% accuracy on the cross-subject protocol. Experimental results demonstrated that DrowsyDG-Phys outperformed baseline methods, and improved generalization and robustness of physiological signal-based drowsiness monitoring.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108407"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974338","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}

引用次数: 0

Cooperative or competitive? Resolving social dilemmas in autonomous vehicles through evolutionary game theory 合作还是竞争？利用进化博弈论解决自动驾驶汽车中的社会困境

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-16 DOI: 10.1016/j.aap.2026.108402

Rui Li, Yiru Liu, Jian Sun, Ye Tian

{"title":"Cooperative or competitive? Resolving social dilemmas in autonomous vehicles through evolutionary game theory","authors":"Rui Li, Yiru Liu, Jian Sun, Ye Tian","doi":"10.1016/j.aap.2026.108402","DOIUrl":"10.1016/j.aap.2026.108402","url":null,"abstract":"<div><div>With the large-scale deployment of autonomous vehicles (AVs), AV-human-driven vehicle (HV) interactions are increasingly common. AVs face a social dilemma: competitive behavior raises ethical and public acceptance concerns, whereas cooperative behavior can invite exploitation and degrade efficiency. We use Evolutionary Game Theory (EGT) to model long-run adaptation between AVs and HVs and quantify agent sociality via a data-calibrated Social Value Orientation (SVO) metric. After calibrating HV social preferences from unprotected left-turn trajectories, we incorporate HV heterogeneity into a two-population EGT with cooperative and competitive types. SVO-informed rewards are used to construct payoff matrices for replicator analyses to identify evolutionarily stable strategies (ESS). Experiments show that AV policies with moderate egoism mitigate the social dilemma and tend to achieve population-level dominance in both roles (left-turning and straight-going), whereas overly cooperative policies are evolutionarily unstable. Moreover, AVs benefit from opponent-aware, dynamically adjustable sociality to accommodate diverse HV preferences. To test the theory, we run agent-based imitation simulations. Sensitivity analyses indicate that AV advantages are hard to observe at low market penetration but become pronounced as penetration approaches about 50%, after which convergence accelerates. Overall, the framework clarifies when and why AV sociality preferences succeed over time, offering actionable guidance for designing adaptive, socially compatible AV decision policies in mixed traffic.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108402"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974328","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}

引用次数: 0

Visual motion contrast thresholds in the periphery predict older drivers’ behavior at intersections 外围视觉运动对比阈值预测老年驾驶员在十字路口的行为。

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-20 DOI: 10.1016/j.aap.2026.108411

Vincent Francoeur , Christine Saber , Steven Henderson , Charles Collin , Stephanie Yamin , Sylvain Gagnon

{"title":"Visual motion contrast thresholds in the periphery predict older drivers’ behavior at intersections","authors":"Vincent Francoeur , Christine Saber , Steven Henderson , Charles Collin , Stephanie Yamin , Sylvain Gagnon","doi":"10.1016/j.aap.2026.108411","DOIUrl":"10.1016/j.aap.2026.108411","url":null,"abstract":"<div><div>Peripheral motion contrast sensitivity decline is likely due to a progressive dysfunction of the magnocellular pathway in the aging brain. Previous research from our group had demonstrated that the Peripheral Motion Contrast Threshold 2-minute test version (PMCT-2) predicts older drivers’ hazardous behaviors in simulated driving environments. This study extends this work by examining correlations between PMCT-2 scores and on road driving outcomes at intersections coded from video recordings of fifty older drivers (65–89) navigating predefined urban routes in their own vehicles. We found significant correlations between PMCT-2 and scanning errors at non-signalized and stop-signalized intersections. We also found significant PMCT-2 correlation with driving compliance errors, notably incomplete stops, which was further supported by single-predictor regression using heteroskedasticity-robust estimation. Multiple linear regression analyses further showed that PMCT-2 remained the only significant predictor of stop-sign compliance errors after adjusting for age, gender and scanning error rates at stop signs. In contrast, its relationship with scanning errors was attenuated in linear models, reflecting the very low frequency of scanning errors observed on the road. These findings build on prior evidence that the PMCT-2 predicts older drivers’ performance outcomes and, for the first time, demonstrate its potential to predict actual on-road driving performance at intersections.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108411"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146017068","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}

引用次数: 0

Adaptive risk inversion with iterative exploration for high-risk AV–VRU interactions 基于迭代探索的AV-VRU高风险交互自适应风险反演

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-23 DOI: 10.1016/j.aap.2026.108417

Yujia Zhao , Ying Ni , Jialin Fan , Jie Wang , Jian Sun , Shaoxiong Tian , Qiang Wang

{"title":"Adaptive risk inversion with iterative exploration for high-risk AV–VRU interactions","authors":"Yujia Zhao , Ying Ni , Jialin Fan , Jie Wang , Jian Sun , Shaoxiong Tian , Qiang Wang","doi":"10.1016/j.aap.2026.108417","DOIUrl":"10.1016/j.aap.2026.108417","url":null,"abstract":"<div><div>The interaction between vulnerable road users (VRUs) and automated vehicles (AVs) represents a critical aspect of AV safety evaluation, especially under realistic high-risk scenarios. However, existing scenario-based testing methodologies often inadequately capture VRUs’ behavioral complexity and struggle to systematically explore rare yet critical long-tail risks. This limitation diminishes the representativeness and rigor of safety validation, potentially underestimating the risks inherent in complex AV–VRU interactions. To address these shortcomings, this study proposes ARISE (Adaptive Risk Inversion for Scenario Exploration) specifically designed for generating long-tail, high-risk AV–VRU interaction scenarios. The framework integrates failure-driven feedback with interpretable behavioral modeling, systematically identifying, parameterizing, and refining high-risk regions within the scenario space. A multidimensional behavioral model capturing VRU decision-making variability is developed, enabling hierarchical extraction of critical single- and multi-parameter risk intervals with clear interpretability. The proposed ARISE is implemented on Open Natural Driving Intelligence Automotive Simulation Test Environment (OnSite), facilitating iterative scenario generation and systematic evaluation of AV safety performance. Experimental case studies validate that ARISE significantly improves both the diversity and severity of generated high-risk interactions compared to conventional methods. These findings underscore ARISE’s capability to effectively bridge the gap between scenario generation and AV safety validation, providing a robust methodological foundation for assessing AVs in challenging and realistic VRU interactions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108417"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146023725","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}

引用次数: 0

ROAR: Robust accident recognition and anticipation for autonomous driving ROAR：为自动驾驶提供强大的事故识别和预测

IF 6.2 1区工程技术

Accident; analysis and prevention Pub Date : 2026-04-01 Epub Date: 2026-01-16 DOI: 10.1016/j.aap.2026.108414

Xingcheng Liu , Yanchen Guan , Haicheng Liao , Zhengbing He , Zhenning Li

{"title":"ROAR: Robust accident recognition and anticipation for autonomous driving","authors":"Xingcheng Liu , Yanchen Guan , Haicheng Liao , Zhengbing He , Zhenning Li","doi":"10.1016/j.aap.2026.108414","DOIUrl":"10.1016/j.aap.2026.108414","url":null,"abstract":"<div><div>Accurate accident anticipation is essential for enhancing the safety of autonomous vehicles (AVs). However, existing methods often assume ideal conditions, overlooking challenges such as sensor failures, environmental disturbances, and data imperfections, which can significantly degrade prediction accuracy. Additionally, previous models have not adequately addressed the considerable variability in driver behavior and accident rates across different vehicle types. To overcome these limitations, this study introduces <strong>ROAR</strong>, a novel approach for accident detection and prediction. ROAR combines Discrete Wavelet Transform (DWT), a self-adaptive object-aware module, and dynamic focal loss to tackle these challenges. The DWT effectively extracts features from noisy and incomplete data, while the object-aware module improves accident prediction by focusing on high-risk vehicles and modeling the spatial–temporal relationships among traffic agents. Moreover, dynamic focal loss mitigates the impact of class imbalance between positive and negative samples. Evaluated on three widely used datasets — Dashcam Accident Dataset (DAD), Car Crash Dataset (CCD), and AnAn Accident Detection (A3D) — our model consistently outperforms existing baselines in key metrics such as Average Precision (AP) and mean Time-to-Accident (mTTA). These results demonstrate the model’s robustness in real-world conditions, particularly in handling sensor degradation, environmental noise, and imbalanced data distributions. This work offers a promising solution for reliable and accurate accident anticipation in complex traffic environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"228 ","pages":"Article 108414"},"PeriodicalIF":6.2,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145974337","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}

引用次数: 0