Accident; analysis and prevention最新文献

{"title":"Segment length optimization for crash frequency modelling: Evaluating power spectral segment length in safety performance assessment","authors":"Parveen Kumar ,&nbsp;Geetam Tiwari ,&nbsp;Sourabh Bikas Paul","doi":"10.1016/j.aap.2025.108122","DOIUrl":"10.1016/j.aap.2025.108122","url":null,"abstract":"<div><div>Selecting an appropriate segment length is essential for road safety analysis, as it directly influences crash analysis accuracy, hazardous location identification, and safety performance evaluation. The traditional segmentation approaches rely on individual expertise or engineering judgment and often lack standardized metrics for evaluating segmentation performance. Therefore, this study expands the utilization of Spatial Frequency Domain Analysis (SFDA) based Power Spectral Segment Length (PSSL) in crash frequency modeling for predicting fatal crash occurrence. The power spectral analysis reveals that crash frequencies predominantly concentrate in low-frequency bands, which helps in determining the Power Spectral Percentage (PSP), a critical measure for evaluating segmentation performance. The Random Parameters Negative Binomial (RPNB) models are developed for six rural two-lane highways in order to evaluate the effectiveness of PSSL, accounting for unobserved heterogeneity in crash data. The study results indicate that PSSL-based segmentation consistently outperforms traditional segmentation methods, as demonstrated by Cumulative Residual (CURE) plots and Goodness-of-Fit statistics. Additionally, the study results show that roadside service areas, population density, minor access points, and heterogeneous traffic characteristics are the most significant predictors of fatal crashes across all highway types. Hence, this study provides an optimized, data-driven, and theoretically justified framework for segment length selection, which improves accuracy, reliability, and scalability in crash modeling and road assessment.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108122"},"PeriodicalIF":5.7,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138521","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":"Exploring the relationship between cyclists’ perceived unsafety, crash risk, and exposure in Dutch cities","authors":"Teun Uijtdewilligen ,&nbsp;Mehmet Baran Ulak ,&nbsp;Gert Jan Wijlhuizen ,&nbsp;Karst T. Geurs","doi":"10.1016/j.aap.2025.108113","DOIUrl":"10.1016/j.aap.2025.108113","url":null,"abstract":"<div><div>Road safety of cyclists can be investigated in terms of objective measures based on crashes and conflicts and in subjective measures based on perceptions of cyclists. The number of studies investigating these two safety measures simultaneously is limited, in particular studies that also include exposure metrics. Therefore, the present study aims to find out to what extent perceived unsafety and crash risk of cyclists correlate and spatially align and how this relates to exposure to cyclists and motorised vehicles in the four largest Dutch cities. For this purpose, data and models estimated in earlier work on objective road safety and perceived safety of cyclists are combined. Perceived unsafety is expressed as the probability that a cyclist indicates a road section as unsafe while crash risk is expressed as the probability of a bicycle crash occurring. Results show a significant positive correlation between perceived unsafety and crash risk. It is also shown that perceived unsafety increases stronger than crash risk, which is particularly related to an increase in exposure to cyclists, followed by exposure to motorised vehicles. Conversely, crash risk remains relatively low with an increase in exposure to cyclists, which might indicate a safety-in-numbers effect. However, from a certain point in the exposure to cyclists, crash risk increases more strongly. Presumably, this hints at a situation beyond the safety-in-numbers effect where increasing cycling volumes affect cycling safety more negatively. It can be concluded that the probability of perceiving a road section as unsafe significantly follows the same direction as the probability of a bicycle crash occurring, but with a increasing exposure to cyclists perceived unsafety increases stronger than crash risk. With higher exposure to motorised vehicles, on the other hand, the increase in perceived unsafety and crash risk is more gradual.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108113"},"PeriodicalIF":5.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131157","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":"Influencing factors of risky behavior in truck safety: A random parameter model incorporating trip-wise heterogeneity","authors":"Xiao Hu ,&nbsp;Yunxuan Li ,&nbsp;Ke Zhang ,&nbsp;Meng Li","doi":"10.1016/j.aap.2025.108089","DOIUrl":"10.1016/j.aap.2025.108089","url":null,"abstract":"<div><div>Truck-related crashes cause significant economic losses and casualties, making perception and control of truck driving risk a critical task for the logistics industry. However, heterogeneity in each truck trip is ignored in current studies, hindering precise prediction of truck driving risk. In this research, we defined trip-wise driving behavior as the driving characteristics extracted from real-time trajectory during a single trip, and investigated its impact on truck driving risk considering heterogeneity. Multi-source data were collected and aggregated, including on-board device data recording long-term trajectory and conflict events from 4,672 trucks in China, accompanied by high-resolution traffic environment data collected at the same time. We extracted trajectories on the same route for trucks within the selected fleet, and illustrated the existence of heterogeneity in trip-wise driving behavior using the Kruskal–Wallis test. A random parameter logit model was employed to study the influencing factors on truck driving risk, considering trip-wise heterogeneity. Results indicated that the heterogeneity of each truck trip was mainly reflected in standard deviation of trip-wise speed and environmental conditions (e.g., traffic speed, time of day). The effect of higher standard deviation of trip-wise speed varies significantly across trips, decreasing risk in 73.7% trips and increasing risk in 26.3% trips; this variability was shown through the normal distribution of the estimated parameter. Furthermore, heterogeneity shows the complex factors influencing truck driving risk and reveals overlooked patterns in long-term and trip-wise driving behavior, highlighting the importance of combining long-term behavior pattern with trip-wise behaviors for better risk prediction.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108089"},"PeriodicalIF":5.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106479","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":"Crash outcomes of yellow school buses: Random parameter and correlated random parameter logit models with heterogeneity in means","authors":"Monire Jafari ,&nbsp;Subasish Das ,&nbsp;Swastika Barua ,&nbsp;Mahmuda Sultana Mimi ,&nbsp;Michael Starewich","doi":"10.1016/j.aap.2025.108109","DOIUrl":"10.1016/j.aap.2025.108109","url":null,"abstract":"<div><div>Despite rigorous safety standards, school buses in the United States still experience around 26,000 crashes annually, resulting in approximately 10 fatalities, with a fatality rate persistently static despite advances in vehicle safety. Such crashes often have significant implications, particularly for the young passengers involved. This study utilized a novel approach by analyzing Texas Crash Records Information System (CRIS) data from 2017 to 2022 through a Random Parameter Logit Model with Heterogeneity in Means (RPLHM) and Correlated RPLHM (CRPLHM). This method allows for a detailed examination of unobserved heterogeneity and specific variances within the data, enhancing the understanding of the complex dynamics influencing crash severity. The analysis revealed that crashes on state highways typically presented a lower likelihood of fatal and severe crash outcomes. Additionally, demographic attributes such as age significantly impacted crash outcomes, with middle-aged drivers (25–54) often experiencing less severe injuries. Additionally, driver inattention was associated with an increased occurrence of no-injury crash outcomes. While daylight is associated with less moderate and possible injury crashes, clear weather was associated with higher no-injury crashes. The transferability tests revealed temporal instability in yellow school bus crash severity patterns across 2017–2022. Key variables such as intersections, daylight, and driver characteristics demonstrated varying effects over time. While morning and afternoon crashes increasingly reduced the likelihood of fatal and severe injuries in later years, factors like divided roadways and clear weather saw greater variability in their impact on no-injury and moderate injury outcomes. These findings highlight the importance of year-specific modeling and support data-driven policymaking to improve school bus safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108109"},"PeriodicalIF":5.7,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106585","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":"Modeling dilemma zone at urban signalized intersections using crowdsourced trajectory data","authors":"Pramesh Pudasaini ,&nbsp;Henrick Haule ,&nbsp;Yao-Jan Wu","doi":"10.1016/j.aap.2025.108070","DOIUrl":"10.1016/j.aap.2025.108070","url":null,"abstract":"<div><div>The stop/go dilemma drivers face at the yellow onset is highly correlated with the potential risks of rear-end collisions and red-light running crashes. This dilemma has been physically characterized using the Type I and Type II definitions. Unlike the Type II definition with several limitations, the Type I counterpart incorporates the dynamics of driver-vehicle attributes to quantify the dilemma zone accurately but requires high-quality vehicle trajectory data. Such trajectory data in existing studies are extracted from field-setup video cameras or radar, undergoing manual trajectory reduction and labor-intensive data processing challenges. Moreover, accurate modeling of the Type I dilemma zone dynamics and accuracy evaluation with the Type II methods remain major research gaps in the existing literature. This study addresses these gaps and challenges by accurately quantifying the Type I dilemma zone using a large sample of crowdsourced vehicle trajectory data. Quantile regression is implemented to capture the dynamics of individual driver-vehicle attributes directly into the minimum stopping and the maximum clearing distances. Results across 15 intersection approaches consistently showed that the Type I dilemma zone is created if vehicles approach at a very high speed. Accuracy evaluation yielded low root mean squared errors of 14.8 ft and 25.1 ft in estimating the start and end of zone boundary, demonstrating the proposed method’s superiority over other dilemma zone quantification methods. Besides boundary comparison, driver behavior at the approach area is analyzed to understand potential rear-end and right-angle collision risks. This study advances our understanding of dilemma zone boundary dynamics and provides a sound empirical basis to support the development of efficient dilemma zone protection and signal timing strategies to improve intersection safety.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108070"},"PeriodicalIF":5.7,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106635","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":"Effectiveness of smart horizontal markings on drivers’ behavior along horizontal curves: A driving simulation study","authors":"Francesco Angioi ,&nbsp;Juan de Oña ,&nbsp;Carolina Díaz-Piedra ,&nbsp;Rocío de Oña ,&nbsp;Leandro L. Di Stasi","doi":"10.1016/j.aap.2025.108086","DOIUrl":"10.1016/j.aap.2025.108086","url":null,"abstract":"<div><div>Photoluminescent road markings (PRMs) are a potentially useful visual guidance technology for improving road safety in low-visibility conditions. However, the effectiveness of PRMs requires further research. Moreover, road infrastructure regulations lack guidelines for PRMs design. Here, we aimed at determining the effects of different PRMs colors and widths on transversal and longitudinal driving behavioral indices. We conducted a simulation-based 3x2x2 within-subjects experiment (<em>PRM</em>: unlit vs. smart green vs. smart red; <em>marking width</em>: conventional vs. wide; <em>curve direction</em>: left vs. right). We designed six two-lane rural highway scenarios with nighttime light conditions and no traffic. Each scenario included twenty-four horizontal curves with radii ranging from 120 to 440 m (recommended speed range 60–90 km/h). Thirty participants (age range 20–54 years) drove a semi-dynamic driving simulator for about one hour. Our results showed that the presence of PRMs affected the drivers’ transversal behavior. The smart markings induced drivers to keep greater lateral distances from the road edge line than unlit ones along right curves. Smart green markings showed higher variability for vehicle positioning, indicating lower vehicle control. Wider-than-normal markings induced users to drive closer to the edge line at the Tangent-to-Spiral section. Overall, our study showed that smart markings - both green and red - induce the driver to “shy away” from the edge line, thus representing a potential tool for preventing roadway departure events. Further studies are expected to confirm these results by focusing on different PRM layouts, traffic, and weather conditions.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108086"},"PeriodicalIF":5.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144084332","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 novel bi-level clustering optimization approach to balance treatment of crash data","authors":"Tanveer Ahmed,&nbsp;Vikash V. Gayah","doi":"10.1016/j.aap.2025.108107","DOIUrl":"10.1016/j.aap.2025.108107","url":null,"abstract":"<div><div>Understanding the impact of safety countermeasures on crash outcomes is crucial but challenging. When using cross-sectional data to quantify a countermeasure’s effectiveness, underlying differences in road characteristics can lead to imbalances between treated sites and control sites that do not have the countermeasure, which can introduce bias into the evaluation. Propensity score-based matching methods have been widely used in the traffic safety literature to identify treated and control sites with more balanced covariates; however, the use of propensity scores does not guarantee bias between treated and control entities is minimized and its success is highly dependent on propensity score model formulation. To address this issue, this study introduces a novel Bi-Level Clustering Optimization (BLCO) method to match treated and control sites in a way that minimizes imbalance across the two groups. The proposed method utilizes competitive learning to specifically minimize the sum of squares of standardized bias of covariates across the treated and control groups, better simulating the conditions of a randomized trial using non-random observational data. The proposed BLCO method was compared to propensity score matching methods using binary logit regression, random forest algorithms, as well as the genetic matching method. The results demonstrate that the proposed BLCO method significantly outperforms these benchmarks at balancing covariates across treated and control groups, reducing mean absolute standardized bias by 96.16% compared to the unmatched data and achieving an 88.76% improvement over propensity score matching. Additionally, treatment effects of the treated estimated using optimally clustered data showed better model fit compared to the other methods. The proposed method is robust across varying dataset sizes and efficiently handles high-dimensional covariates without transformation, making it applicable to different domains for treatment effect estimation and informed decision-making.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108107"},"PeriodicalIF":5.7,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071856","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":"Nighttime safety of pedestrians: The role of pedestrian automatic emergency braking systems","authors":"Nastaran Moradloo ,&nbsp;Iman Mahdinia ,&nbsp;Asad J. Khattak","doi":"10.1016/j.aap.2025.108110","DOIUrl":"10.1016/j.aap.2025.108110","url":null,"abstract":"<div><div>The rise in vulnerable road user fatalities, e.g., pedestrians, is alarming. Nighttime poses unique challenges among the risk factors in pedestrian-involved crashes, accounting for approximately 74% of fatalities in the United States. Safe vehicles, a key element of the Safe System Approach, are a promising solution to improve pedestrian safety. Vehicle automation, particularly Pedestrian Automatic Emergency Braking (P-AEB) systems, can mitigate pedestrian-involved crashes. However, the P-AEB systems’ effectiveness, especially in darkness, has remained uncertain. Through analyzing the recent Insurance Institute for Highway Safety nighttime dataset from 2021 to 2023, integrated with vehicle data to create a unique database (1973 field tests), this study aims to understand the P-AEB systems’ effectiveness at night and explore correlates of their performance, especially the role of headlight technologies, employed sensors, vehicle propulsion type (Electric Vehicles (EVs) vs. non-EVs), and vehicle weight and size within the scope of controlled experimental data. Using a random-effects Heckman sample selection model with panel data, the study estimates the crash probability and impact speed in case of a crash, addressing the inherent panel structure of the data and unobserved heterogeneity among different vehicles and scenarios. Statistics indicate that P-AEBs stopped vehicles in 63.6% of the cases and, on average, reduced speed by 33.12% for the crashes. Results reveal that P-AEBs perform better in vehicles with light-emitting diode headlights than those with halogen headlights and may be relatively less effective in larger, heavier cars and EVs. Additionally, integrating camera and radar sensors improves P-AEBs’ reliability instead of relying solely on cameras. Future efforts can enhance nighttime pedestrian safety by focusing on advanced headlight technologies and sensor integration, improving P-AEBs’ prediction algorithms for various scenarios, and addressing systems’ limitations in larger, heavier vehicles and EVs.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108110"},"PeriodicalIF":5.7,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070996","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":"Advanced traffic conflict analysis for safety evaluation at roundabouts under mixed traffic using extreme value theory","authors":"Abhijnan Maji,&nbsp;Indrajit Ghosh","doi":"10.1016/j.aap.2025.108108","DOIUrl":"10.1016/j.aap.2025.108108","url":null,"abstract":"<div><div>Roundabout safety evaluation in non-lane-based, heterogeneous traffic conditions in low-middle-income countries brings challenges due to unavailable/unreliable crash data, thereby switching to the utilization of safety surrogates. This study employed high-resolution drone videos and advanced image-processing techniques to extract vehicular trajectories. Traffic conflicts were identified using Surrogate Safety Measures (SSMs), namely, Time-to-Collision (TTC) and maximum post-collision (hypothetical) velocity difference (MaxDeltaV). Extreme Value Theory (EVT) was used to determine threshold values for TTC (1.25 s) and MaxDeltaV (4.5 m/s). By employing the determined thresholds in the Surrogate Safety Assessment Model (SSAM) tool developed by the Federal Highway Administration (FHWA), conflicts were classified into lane-change (42 %), rear-end (33 %), and crossing (25 %) types. An AdaBoost regressor model was developed using a negative binomial objective function for conflict frequency prediction. SHAP analysis revealed that increased circular road widths reduced conflict frequency, while higher conflicting and approaching traffic volumes increased the likelihood of conflicts. Based on TTC and MaxDeltaV values, hierarchical and two-step clustering techniques classified the identified conflicts into four severity levels: low (33.8 %), moderate (34.1 %), high (19.3 %), and very high severity (12.8 %). An Ordered Probit model predicted conflict severity based on geometric, traffic, built-environmental factors, and conflict types, with a prediction accuracy of 88.4 %. Larger central island diameters and circulatory road widths reduced severity, while higher average speeds increased conflict severity. This research presents a novel framework that applies EVT to establish SSM thresholds for unsignalized roundabouts under mixed traffic, integrating advanced statistical and machine learning techniques to assess conflict frequency and severity proactively.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108108"},"PeriodicalIF":5.7,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068863","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":"Recognizing autonomous driving disengagement scenarios using the transferable knowledge from human driver’s EEG cognitive data","authors":"Geqi Qi ,&nbsp;Shuo Zhao ,&nbsp;Jixiang Yu ,&nbsp;Peihao Li ,&nbsp;Wei Guan","doi":"10.1016/j.aap.2025.108102","DOIUrl":"10.1016/j.aap.2025.108102","url":null,"abstract":"<div><div>Without human participation in driving operations, the adoption of autonomous driving (AD) technology greatly enhances driving safety by reducing human errors. Even though AD can handle common scenarios properly, some exceptions still call for the human takeover with AD failing to engage due to the incomprehensible or intensely conflict situations that rarely occur. To help AD understand and recognize the disengagement scenarios effectively, this paper incorporates the human electroencephalogram (EEG) cognitive data into modeling and proposes a transfer learning framework to let AD absorb the integrative knowledge from the manual driving (MD). Several disengagement scenarios are designed using a driving simulator and EEG data are collected from both “drivers” in MD and “supervisors” in AD. A conditional maximum mean discrepancy (CMMD) function is introduced to identify the common brain activity characteristics, allowing the recognition model to be transferred from the cognitively demanding domain of MD to the less demanding domain of AD. The results indicate that the proposed model can achieve an 80 % recognition rate for typical disengagement scenarios, such as static obstacles, intersection conflict and vehicle cut-in, using only 30 % of AD training labels. The transferable common feature space from EEG data improves the recognition accuracy by 21.2 % compared with the model only using AD domain data. By accurately recognizing the type of disengagement scenarios, the AD system can activate appropriate safety mechanisms or provide more explicit takeover prompts, which could effectively reduce the risk of accidents due to delayed or incorrect takeovers.</div></div>","PeriodicalId":6926,"journal":{"name":"Accident; analysis and prevention","volume":"219 ","pages":"Article 108102"},"PeriodicalIF":5.7,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070995","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}