Accident; analysis and prevention最新文献

{"title":"How similar or different are automated vehicle and human-driven vehicle crash patterns? Findings from crash sequence analysis","authors":"Cesar Andriola ,&nbsp;Madhav V. Chitturi ,&nbsp;David A. Noyce ,&nbsp;Yu Song","doi":"10.1016/j.aap.2025.108239","DOIUrl":"10.1016/j.aap.2025.108239","url":null,"abstract":"<div><div>Given the current state of vehicle automation, understanding the similarities and differences between Automated Vehicle (AV) and Human-driven Vehicle (HDV) crashes is crucial to identifying specific challenges and areas for improvement of AV technology. The challenges of directly comparing AV and HDV crashes include differing traffic environments, crash reporting discrepancies, and underreporting of HDV crashes. To address these challenges 555 AV crashes and 39,270 HDV crashes are used to perform a Crash Sequence Analysis. Results show that while AV and HDV crashes can be classified into similar groups based on vehicle movements, the types of crashes can differ significantly. While intersections pose greater challenges for AVs compared to HDVs, the severity of AV crashes is lower, which might be attributed to the reduced number of crashes involving AVs and vulnerable road users. Considering similar crash contexts, AV and HDV crashes can differ significantly, especially in scenarios involving pedestrians, left and right turns, the stopping movement of AVs, and red light violations. Furthermore, the analysis shows three groups with contexts unique to the AV crashes (rear end crashes following a lane change or stopped vehicles, and side swipe crashes on narrow streets), which can indicate both technological challenges and the differences in crash exposure caused by the manufacturers’ training environment. The above emphasizes the importance of addressing expectancy violations likely to emerge in a mixed fleet environment of AVs and HDVs, particularly accounting for geographical and cultural specificities. These findings provide key insights for shaping future AV development, guiding public sector decisions, and building public trust in automation.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108239"},"PeriodicalIF":6.2,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045583","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":"Deriving workload from driving behavior and psycho-physiology in work zones","authors":"Chi Zhao ,&nbsp;Siyang Zhang ,&nbsp;Zherui Zhang ,&nbsp;Yecheng Lyu","doi":"10.1016/j.aap.2025.108240","DOIUrl":"10.1016/j.aap.2025.108240","url":null,"abstract":"<div><div>Due to atypical driving scenarios, work zones may increase driving uncertainty, elevate behavioral deviations and intensify psychological workload, potentially pose risks and even lead to crashes. Existing studies predominantly focus on regular scenarios, traditional models and theories, which are not able to explain work zone driving behaviors and psycho-physiology due to different driving environment and complexity. This study aims to better understand the dynamic interaction among work zone driving behavior, driver psychology and physiology, so as to develop an interpretable workload representation framework. A driving simulator study with one baseline and three work zone scenarios was conducted, along with the collection of driving behavior, physiological data, and post-simulator survey data. Sixteen features were inputted to Optuna framework for Bayesian hyperparameter optimization, then a stacking ensemble learning model was built to identify workload levels, with a 93.14% accuracy, excessing other ten machine learning models including Light Gradient Boosting Machine (LightGBM) and Neural Networks. Workload correlations with driving behavior and psycho-physiology were analyzed and visualized through SHapley Additive exPlanation (SHAP) method. Significantly higher workload is characterized by greater lateral position shifts, more frequent brake pedal utilization, higher heart rate, lower heart rate variability, and more often changes in pupil diameter and gaze position. The results of this study reveal the dynamic relationships among driving behavior, driver psychology and physiology under different work zone setups, which could provide clearer insight for the design and optimization of driver assistance and autonomous driving systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108240"},"PeriodicalIF":6.2,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045661","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 crash-injury-aware driving safety field for real-time risk assessment and its application in autonomous vehicle motion planning","authors":"Wenfeng Guo ,&nbsp;Jing Luo ,&nbsp;Xiaolin Song ,&nbsp;Jun Li","doi":"10.1016/j.aap.2025.108224","DOIUrl":"10.1016/j.aap.2025.108224","url":null,"abstract":"<div><div>Driving risk comprises not only the likelihood of a collision but also the severity of its potential consequences. In this paper, we introduce a crash-injury-aware driving safety field (CIA-DSF) for real-time risk assessment, which explicitly incorporates key features that influence crash injury severity into the potential field formulation. First, an interpretable machine learning framework combining eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) is developed to identify the key features and qualitatively analyze their complex interactions in determining crash injury severity. Then, these extracted features are integrated into the driving safety field, accompanied by the introduction of impact area tuning factor to capture how vehicle geometry influences the spatial distribution of injury severity. Next, the CIA-DSF is utilized within a model predictive control (MPC)-based motion planner, enabling autonomous vehicles to proactively anticipate and effectively mitigate driving risks. Finally, three representative case studies are conducted to validate the effectiveness and superiority of the proposed risk assessment framework and the corresponding motion planner. Simulation results demonstrate that both the shape and intensity of the potential field are continuously updated in respond to collision probability and anticipated injury severity, and the motion planner consistently guides autonomous vehicles away from scenarios that could result in severe injuries if a collision were to occur.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108224"},"PeriodicalIF":6.2,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145045660","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":"Interactive Risk (IR): An omnidirectional safety metric of CAVs based on multimodal trajectory prediction and driving risk field","authors":"Junkai Jiang ,&nbsp;Zhiyuan Liu ,&nbsp;Hao Cheng ,&nbsp;Zeyu Han ,&nbsp;Zehong Ke ,&nbsp;Yuning Wang ,&nbsp;Qing Xu ,&nbsp;Jianqiang Wang","doi":"10.1016/j.aap.2025.108228","DOIUrl":"10.1016/j.aap.2025.108228","url":null,"abstract":"<div><div>Traffic accidents pose a significant threat to human life and property, and with the increasing presence of connected and autonomous vehicles (CAVs), effective risk assessment has become more critical. Current safety metrics, often limited to longitudinal or lateral assessments, fail to address omnidirectional risks or account for the uncertainties associated with vehicle intentions. This paper introduces a new omnidirectional safety metric, Interactive Risk (IR), which combines the concept of the driving risk field with multimodal trajectory prediction. IR captures the uncertainty of vehicle intentions, quantifies the probability and severity of potential accidents, and provides a comprehensive measure of traffic risk. Through case studies of typical collision scenarios and experiments with the simulation and real world dataset, we demonstrate that IR accurately reflects the risk levels faced by CAVs, detects collision risks earlier, and aligns more closely with human intuition compared to baseline safety metrics. Furthermore, we propose four key applications of IR, including traffic risk monitoring, ego-vehicle risk warning, driving decision-making performance evaluation, and motion and trajectory planning. The results highlight the potential of IR to enhance safety assessment in dynamic traffic environments and provide valuable insights for future research and application in autonomous vehicle systems.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108228"},"PeriodicalIF":6.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020126","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":"Internal cognitive responses to typical external inducing factors in the indoor evacuation: The regulatory model of the brain","authors":"Guanning Wang ,&nbsp;Tao Chen ,&nbsp;Song Wang ,&nbsp;Han Yang ,&nbsp;Jianyu Wang","doi":"10.1016/j.aap.2025.108206","DOIUrl":"10.1016/j.aap.2025.108206","url":null,"abstract":"<div><div>Individual cognitive patterns in pathfinding play a pivotal role for understanding the intricate dynamics of crowd evacuation. However, the relationship between individual cognitive responses and whose evacuation performance remains underexplored in scholarly research. To address this issue, a new evacuation experimental design of functional near-infrared spectroscopy combined with an eye tracking device was employed by manipulating various inducing factors. A total of six inducing conditions were performed to investigate the corresponding cognitive responses. Results show that: On the behavioral level, the influence of surrounding pedestrians on individual’s decision is significant, as individuals tend to follow those around them or work together to move obstacles ahead. Moreover, compared to building broadcasts, the guidance provided by guiding personnel is more effective. On the cognitive level, different inducing factors in the building stimulate activation in distinct prefrontal cortex regions. When pedestrians evacuate alone or are directed by on-site personnel, the ventrolateral prefrontal cortex (VLPFC) shows significant activation. However, when there are accompanying individuals, guiding personnel, building broadcasts, or obstacles within the building, the interaction between individual, social, and environmental factors increases, leading to significant activation of the dorsolateral prefrontal cortex (DLPFC). From the perspective of the relationship between cognition and behavior, different inducing factors influence the brain dynamics, and then change corresponding evacuation behavior. BSE model could explain the relationship between the DLPFC activity and the willingness of following, relatively. By quantitatively measuring the brain cognitive activity and comprehensively revealing its influence on pedestrians’ decision making, this study is expected to enhance the understanding of brain dynamics in pedestrian evacuation, and offer significant insights for emergency management in pedestrian evacuation.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108206"},"PeriodicalIF":6.2,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145020125","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":"Objective data-driven insights into pedestrian decisions, comprehensibility, and perceived safety of autonomous vehicles with varied eHMIs: Evidence from a real-world experiment","authors":"Tianpei Tang ,&nbsp;Bang Luo ,&nbsp;Wei Wang ,&nbsp;Xiaofan Xue ,&nbsp;Shengnan Zhao ,&nbsp;Yuntao Guo","doi":"10.1016/j.aap.2025.108227","DOIUrl":"10.1016/j.aap.2025.108227","url":null,"abstract":"<div><div>In future traffic environments dominated by highly autonomous vehicles (AVs), pedestrians may face challenges in accurately interpreting AV behavior, thereby potentially increasing the risk of pedestrian-AV interactions. External human–machine interfaces (eHMIs) have been proposed to facilitate communication between AVs and pedestrians; however, comprehensive evaluations using objective data from real-world interactions are limited. This study developed a systematic evaluation framework grounded in the ISO 9241–11 standard, integrating four key indicators: decision accuracy, comprehensibility, decision efficiency, and perceived safety. Objective data were collected through behavioral observation and eye tracking, with decision accuracy, total fixation time, decision time, and the coefficient of variation of pupil diameter as quantitative metrics. The study examined the effects of eHMI types (light-band, symbol, text), deceleration strategies (gentle, early, aggressive braking, no braking), and yielding behaviors (yielding, non-yielding) on pedestrian decision-making and perceptions. A total of 24 participants were recruited for a real-world crossing interaction experiment. The results showed that eHMIs significantly improved decision accuracy under yielding conditions, while decision accuracy remained high under non-yielding conditions regardless of eHMI type. eHMIs enhanced comprehensibility, with symbol-based and text-based eHMIs performing better than light-band eHMIs. eHMIs also improved pedestrian decision efficiency and perceived safety, with significant differences observed across different eHMI types and yielding behaviors. Furthermore, while deceleration strategies had no significant effect on eHMI comprehensibility or decision efficiency, they played a crucial role in shaping perceived safety. These findings inform the design of eHMIs and deceleration strategies to optimize pedestrian-AV interactions, contributing to safer AV integration in traffic environments.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108227"},"PeriodicalIF":6.2,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011001","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":"Investigating pedestrian crash injury patterns: A comparative study of children and non-children","authors":"Zifei Wang ,&nbsp;Huizhong Guo ,&nbsp;Chengxin Zhang ,&nbsp;Zhen Hu ,&nbsp;Feng Zhou ,&nbsp;Zhaonan Sun ,&nbsp;Rini Sherony ,&nbsp;Shan Bao","doi":"10.1016/j.aap.2025.108223","DOIUrl":"10.1016/j.aap.2025.108223","url":null,"abstract":"<div><div>Pedestrian injuries remain a public health concern, with child pedestrians being particularly vulnerable due to their unique physical and cognitive characteristics. This study presents a comprehensive analysis comparing injury severity patterns between child (<span><math><mo>≤</mo></math></span>14 years) and non-child (<span><math><mrow><mo>&gt;</mo><mn>14</mn></mrow></math></span> years) pedestrians using Lasso logistic regression and advanced machine learning techniques, specifically Catboost with SHAP (SHapley Additive exPlanations) values to interpret the models. By analyzing six years of national crash data from the Crash Report Sampling System (CRSS) from 2016 to 2021, we identify significant factors influencing injury outcomes for both age groups. Our findings reveal that several variables were consistently associated with injury severity across both age groups and modeling approaches, including speed limit, lighting condition, pedestrian age, pedestrian contributing factor, vehicle pre-event movement, vehicle body type, traffic control, and intersection. However, key differences emerged. For child pedestrians, roadway surface and pedestrian position were identified only through a statistical modeling approach, while factors such as driver age, drug involvement, accompanying status, and year of crash were only found in the CatBoost model. In contrast, non-child models identified a broader set of driver-related factors, including age, drinking, and drug usage, which were less influential for children. The study demonstrates the value of integrating machine learning with traditional statistical methods to capture complex relationships and improve the understanding of injury severity for vulnerable road users. These findings offer valuable insights into pedestrian injury patterns across different age groups, informing targeted interventions aimed at enhancing pedestrian safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108223"},"PeriodicalIF":6.2,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004762","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 knowledge-integrated learning framework for accurate quantification and semantic interpretation of driving aggressiveness","authors":"Zhaokun Chen ,&nbsp;Wenshuo Wang ,&nbsp;Chaopeng Zhang ,&nbsp;Yingqi Tan ,&nbsp;Lu Yang ,&nbsp;Junqiang Xi","doi":"10.1016/j.aap.2025.108225","DOIUrl":"10.1016/j.aap.2025.108225","url":null,"abstract":"<div><div>Aggressive driving is a major contributor to traffic fatalities, necessitating reliable assessment methods to guide driver interventions. Existing methods, however, lack granularity in assessing both the severity and specific maneuver categories of aggressive driving behaviors. This paper proposes a novel framework for multidimensional aggressiveness assessment using lateral-longitudinal acceleration and vehicle speed. The framework combines domain-specific prior knowledge with a non-parametric statistical method to quantify aggressiveness levels and automatically extract aggressive driving samples. We then classify them into distinct maneuver categories through fuzzy clustering and semantic analysis, assigning each sample a membership degree for every category. Finally, we integrate the samples’ levels with their membership distribution across the maneuvers to generate comprehensive profiles of individuals’ driving aggressiveness. Experimental validation with real-world driving data (<span><math><mrow><mi>N</mi><mo>=</mo><mn>90</mn></mrow></math></span> drivers) and real-time in-vehicle testing confirms our framework’s effectiveness and practicality. Additionally, a spatiotemporal analysis of driving maneuvers reveals insights into the evolution of aggressive driving and its relationship with environmental factors.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108225"},"PeriodicalIF":6.2,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004763","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":"Clear zone or W-beam guardrail for rural highways? A Full Bayes before-after evaluation by employing the Poisson Gamma-Lindley model","authors":"Shinthia Azmeri Khan,&nbsp;Hassan Bin Tahir,&nbsp;Shamsunnahar Yasmin,&nbsp;Shimul Md Mazharul Haque","doi":"10.1016/j.aap.2025.108218","DOIUrl":"10.1016/j.aap.2025.108218","url":null,"abstract":"<div><div>Run-off-road crashes often result in fatal and severe injuries on rural highways. Clear zones and W-beam guardrails are often installed to reduce run-off-road crashes and the extent of severe injuries. While the effectiveness of clear zone improvement and W-beams has been studied, their comparative effects in a similar empirical setting are not well known. At the same time, crashes on rural highways are often characterized by a high frequency of zero counts; however, models capable of addressing this issue—such as the Poisson-Gamma Lindley models—have not yet been tested in before-after evaluations of engineering treatments. Towards this end, this study aims to examine the safety effectiveness of clear zones and W-beam guardrails on rural highways by applying a Full Bayes approach with Poisson-Gamma Lindley model that considers heterogeneities resulting from the preponderance of zeros. A total of 82.70 km of treated roadway segments with Clear zone improvements (0–5 m to 5–10 m) implemented in 2014 and 2015 were extracted from Bruce Highway in Queensland, Australia. W-beam guardrails were installed along a total of 45.5 km of the same rural highway between 2013 and 2017. The computed Crash Modification Factors (CMFs) show that the clear zone widening significantly reduced total injury (CMF = 0.57), fatal, serious, and moderate injury (CMF = 0.58), fatal and serious injury (CMF = 0.53), and run-off-road crashes (CMF = 0.63). W-beam guardrails are also found to significantly reduce total injury (CMF = 0.75), fatal, serious, and moderate injury (CMF = 0.77), fatal and serious injury (CMF = 0.81), and run-off-road crashes (CMF = 0.56). Overall, both countermeasures significantly reduce run-off-road crashes and other injury severities, including total injury, fatal, serious, and moderate injury, and fatal and serious injury crashes. W-beam guardrails have been found to provide greater reductions in run-off-road crash occurrences, whereas clear zone improvements are more effective in reducing injury severity by offering more forgiving roadside conditions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108218"},"PeriodicalIF":6.2,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144933165","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":"Assessment of the safety benefits of HUD warning under high-risk pedestrian crossing event in the connected environment","authors":"Yu Zhang ,&nbsp;Xiaohua Zhao ,&nbsp;Yang Bian ,&nbsp;Jianling Huang ,&nbsp;Duan Yu ,&nbsp;Haolin Chen","doi":"10.1016/j.aap.2025.108157","DOIUrl":"10.1016/j.aap.2025.108157","url":null,"abstract":"<div><div>The head-up display (HUD) warning system in a connected environment is expected to improve driving behavior and enhance pedestrian crossing safety. While existing research has preliminarily examined the effectiveness of HUD warning system in avoiding pedestrian collisions, scant attention has been given to the microcosmic influence on driving behavior and a precise quantification of its overall benefits, especially in high-risk pedestrian crossing scenarios. To investigate these influences, this study employed driving simulations to construct six connected scenarios: three warning systems (Baseline/head-down display(HDD)/HUD) × two weather conditions (clear weather/foggy weather). Data on the driving behavior of 34 drivers across these scenarios were collected. The whole spatial change process of driving behavior in pedestrian crossing events is described from the microscopic level, and the influence law of warning system and weather conditions on reaction, acceleration and deceleration behavior is analyzed. A comprehensive index system reflecting the safety level of the risk-avoidance stage, the recovery level of the recovery stage and the stability level of the overall stage was constructed to explore impact characteristics and utilities of the three warning systems under different weather conditions. The study found that the speed space variations under HDD and HUD conditions were more gentle compared to Baseline conditions, especially in the HUD group, but there were differences in individual adherence to HDD and HUD systems. The results of two-way repeated measures ANOVA and fuzzy comprehensive evaluation indicated that compared to the Baseline and HDD, the HUD warning system improves the safety level and stability level under clear and foggy weather conditions, but does not have a significant advantage in the recovery level. Specifically, the HUD system enables drivers to react earlier, complete risk avoidance earlier, execute smoother acceleration and deceleration maneuvers, and maintain more stable lateral control. Overall, the HUD warning system helps drivers achieve optimal driving performance in a connected environment, even in more hazardous foggy conditions. The research results can provide support for relevant departments to evaluate and improve HUD system in a targeted manner.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"222 ","pages":"Article 108157"},"PeriodicalIF":6.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144922790","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}