Accid Anal Prev [JOURNAL]
Sun
200 papers
Towards self-explaining spiral tunnels: road-prototype-based facility design and visual-perception fluid assessment.
Xia Y, Zhang C, Zhang M … +3 more , Wang B, Gao Y, Xu S
Accid Anal Prev · 2026 Aug · PMID 42070494 · Publisher ↗
With increasing demand for spiral tunnels, drivers face spatial cognitive challenges due to strong geometry transitions. To address this issue, a self-explaining-road-based framework is developed, focusing on the system-...
With increasing demand for spiral tunnels, drivers face spatial cognitive challenges due to strong geometry transitions. To address this issue, a self-explaining-road-based framework is developed, focusing on the system-level optimization of four categories of tunnel environmental facilities, including visual guidance facilities, traffic signs, overall environment, and roadway. These elements are systematically combined under a road prototype design approach to induce expected driving behavior. In addition, a visual-perception fluid (VPF) model is proposed to evaluate drivers' visual-perception performance in spiral tunnels. Four semantic categories were extracted via environmental semantic segmentation to compute equivalence mass of environmental information, information flow velocity, cognitive resistance, and composite VPF index. A driving-simulator experiment with 12 scenarios yielded 48 valid samples, and a hierarchical paired permutation test evaluated design effects. The prototype design increased composite VPF index by 1.9 (4.6%) relative to ordinary design, with significant improvements in structural feature parameters (p < 0.05), supporting the assessment capability of the model. Prototype designs consistently enhanced key semantic information (visual guidance facilities and roadway) and reduced cognitive resistance by 2.2% and showed favorable medium-term and long-term stability. Simultaneously, engineering optimization should prioritize entrance areas and the saliency design of visual guidance facilities and traffic signs.
Using psychophysiological metrics for understanding drivers' mental workload and visual attention when overtaking automated truck platoons.
Lin Z, Chen F, Öztürk İ … +1 more , Merat N
Accid Anal Prev · 2026 Aug · PMID 42070493 · Publisher ↗
The automated truck platoon is one of the most promising connected autonomous vehicle technologies and is expected to become mainstream in the future. It is foreseeable that automated truck platoons and human drivers wil...
The automated truck platoon is one of the most promising connected autonomous vehicle technologies and is expected to become mainstream in the future. It is foreseeable that automated truck platoons and human drivers will share the roads and interact regularly. As a new traffic element, the truck platoon may influence other drivers' behaviors and mental states, potentially compromising the safety of mixed traffic. While existing studies have extensively explored drivers' behavioral responses to truck platoons, little is known about their psychophysiological states during such interactions, particularly how platoon organization influences drivers' mental workload and attention. To address this gap, a comprehensive set of platoon organization factors was considered, and a high-fidelity driving simulator experiment was conducted. The study employed a 2 (platoon speed: 80 km/h vs. 100 km/h) × 2 (platoon size: three vs. five trucks) × 2 (inner gap: 5 m vs. 25 m) × 2 (traffic environment: presence vs. absence of a lead vehicle) within-subjects factorial design. Drivers' heart rate, pupil diameter, gaze dispersion, and subjective mental workload ratings were recorded and analyzed, with data collected from 35 participants. Results showed that compared to the baseline, drivers' horizontal gaze dispersion was more concentrated during interactions with the truck platoon. Furthermore, an inner gap of 5 m can significantly increase drivers' mental workload compared to an inner gap of 25 m, as indicated by mean heart rate and mean pupil diameter. Regarding platoon speed, drivers' horizontal gaze was more dispersed at a platoon speed of 100 km/h compared to 80 km/h, likely due to greater attention to maintain lateral distance from the median divider and the platoon. Moreover, drivers' mental workload showed a significant decreasing trend with repeated interactions with the truck platoon. These findings provide insights into the operational strategies of truck platoons from a human factors perspective.
Safer pathfinding strategies for multilevel interchange guide signs based on driving behavior coordination analysis.
Chen Y, Zhao X, Luan S … +3 more , Gao L, Dong W, Liu Q
Accid Anal Prev · 2026 Aug · PMID 42068757 · Publisher ↗
Highway multilevel interchanges present unique challenges, characterized by complex road conditions, ambiguous routes and short intervals. Multiple ramps necessitate swift and continuous direction changes, significantly...
Highway multilevel interchanges present unique challenges, characterized by complex road conditions, ambiguous routes and short intervals. Multiple ramps necessitate swift and continuous direction changes, significantly jeopardizing pathfinding safety. Guide signs play a crucial role in improving information recognition and driving safety. This study replaces accident conflicts with risk behaviors and constructs a risk prevention research framework: Firstly, through subjective demand analysis, the cognitive preferences and functional expectations of drivers were analyzed to clarify design orientation. Secondly, the influence of pathfinding process under traffic facility intervention and potential risk relationship was captured in a driving simulation experiment. Furthermore, focus on coupled collaborative analysis, the correlation between longitudinal stability and lateral maneuvering behavior was quantified, and collaborative effectiveness evaluation was conducted. Ultimately, the optimized sign served as a guide for improving behaviors. Results show that: 1) At different ramp decision points, setting "destination separation for different directions" and "lane guidance" information represent drivers'core demands; 2) Under the influence of 4 sign schemes, there are indeed differences in the pathfinding behavior of longitudinal and lateral dimensions, which should be improved in a coordinated manner. 3) Approaching the ramp exit, setting different directions information can help drivers make decisions more quickly, reach the target speed earlier, and reduce conflict time with mainstream traffic. Approaching the ramp for the first diversion, setting both information helps drivers tend to accelerate smoothly, and conduct lane change adjustments before making decisions. Approaching the ramp for the second diversion, simplify setting lane guide information can improve lateral maneuvering and enable faster path selection. 4) Providing information prompts in the form of navigation can offer continuous direction guidance, timely lane changing, with higher longitudinal safety margin and lateral stability. The proposed suggestions for risk prevention can achieve a coordinated improvement in traffic safety across multiple dimensions.
Evaluating active driver intervention strategies in sequential conflict scenarios: a counterfactual reasoning-based framework.
Xie S, Fu T, Wang J … +4 more , Guo Z, Wang J, Shangguan Q, Fu L
Accid Anal Prev · 2026 Aug · PMID 42066655 · Publisher ↗
Multi-round conflicts among different road users are a key contributor to the high incidence of intersection crashes. In sequential conflict scenarios, early round interactions can constrain the maneuvering space availab...
Multi-round conflicts among different road users are a key contributor to the high incidence of intersection crashes. In sequential conflict scenarios, early round interactions can constrain the maneuvering space available in later rounds and influence road users' attention allocation, thereby producing highly volatile risk throughout the episode. Active interventions that target the process-level evolution of sequential conflicts are essential. However, most existing intervention and evaluation approaches are developed under a single-conflict assumption and do not provide a strategy set tailored to different risk levels in sequential conflicts, nor a consistent evaluation model for assessing intervention effectiveness. This study proposes a counterfactual reasoning-based strategy evaluation model that quantifies the net benefits of active interventions and explains how these benefits manifest through interpretable safety mechanisms. First, we develop a multi-layer indicator system that characterizes within-round risk and cross-round risk evolution. We then construct a mechanism-level outcome space with four interpretable dimensions using exploratory factor analysis. Next, trajectory-level counterfactual reasoning is employed to estimate the benefits of each strategy within this common outcome space. The proposed model is validated through a case study conducted on a six-degree-of-freedom driving simulator, from which risk-level intervention recommendations are derived. The experiment involves 30 drivers and 8 scenarios, and evaluates multiple intervention configurations, including offline, online and their combinations. All proposed strategy configurations yield positive net benefits across scenarios. In particular, the online voice prompts are most effective in reducing peak conflict intensity. The proposed model supports interpretable risk-level strategy selection for sequential conflicts and provides a transferable evaluation template for driver assistance systems.
Assessing future road safety with the advent of L-category quadricycles: Simulation insights from current crash patterns.
Gulino MS, Vichi G, Kullgren A … +1 more , Vangi D
Accid Anal Prev · 2026 Aug · PMID 42066654 · Publisher ↗
Heavy quadricycles are gaining traction as sustainable urban mobility solutions due to their compact design, energy efficiency, and reduced environmental impact. However, their lightweight structure and limited safety fe...
Heavy quadricycles are gaining traction as sustainable urban mobility solutions due to their compact design, energy efficiency, and reduced environmental impact. However, their lightweight structure and limited safety features pose significant challenges in collisions, particularly with heavier traditional passenger cars. This study investigates the safety implications of introducing heavy quadricycles (L6e and L7e categories) into the circulating fleet, focusing on collision dynamics and occupant Injury Risk (IR). Advanced simulation tools are employed to reconstruct real-world impacts from an in-depth accident database and analyse the consequences of substituting traditional cars with L-category quadricycles. Velocity change (ΔV) and IR are determined across various collision scenarios as a function of market penetration. Results indicate that in high-speed scenarios (90 km/h) L-category quadricycles experience substantially higher ΔV compared to traditional cars in similar collisions, leading to increased occupant loads and IR across the investigated collision scenarios. Conversely, in 50 km/h urban zones, the average fleet IR decreases, with ΔV averaging 12.6 km/h at 50% penetration. The safest environment is observed in 30 km/h cities, where IR decreases by over 50%. The findings suggest that current consumer programme tests may not fully capture certain critical collision scenarios for L-category quadricycles, notably side impacts. Consequently, further attention should be directed towards safety assessment protocols and design refinements that enhance crashworthiness without compromising the fundamental vehicle concept. The study concludes that while L-category quadricycles offer benefits for sustainable urban transportation, their integration requires careful management to address safety concerns, particularly in high-speed environments.
Constructing Bayesian networks from knowledge graphs for risk assessment and causal inference of urban rail transit equipment.
Zhu L, Liu S, Huang YC … +2 more , Wang FS, Liu ZG
Accid Anal Prev · 2026 Aug · PMID 42066653 · Publisher ↗
The safety risks associated with urban rail transit equipment are characterized by multi-source heterogeneity and dynamic evolution. Traditional expert-driven static management models often fail to meet the proactive pre...
The safety risks associated with urban rail transit equipment are characterized by multi-source heterogeneity and dynamic evolution. Traditional expert-driven static management models often fail to meet the proactive prevention demands in complex scenarios, leading to critical issues such as ambiguous risk identification and insufficiently targeted prevention measures. This study proposes a novel risk assessment and inference method that integrates knowledge graphs with Bayesian networks. First, a safety risk knowledge graph is constructed based on historical accident case reports. Then, a mapping method is proposed to convert the knowledge graph into a Bayesian network. Subsequently, data-driven statistical approaches are employed to estimate the network parameters. Finally, a case study involving equipment failures in urban rail transit is conducted to validate the proposed method. Experimental results demonstrate that the proposed method effectively identifies key risk factors and accurately traces accident causes through backward inference. The method also significantly outperforms traditional approaches in terms of practical accuracy. The findings provide intelligent decision support for the risk management of urban rail transit equipment.
Quantifying the safety effects of left-turn signal control mode: A heterogeneous causal inference framework.
Xu S, Chen Y, Xie Y … +1 more , Wang C
Accid Anal Prev · 2026 Aug · PMID 42061231 · Publisher ↗
Given that correlation-based evidence is insufficient for designing safety performance countermeasures, identifying causal relationships and treatment effect heterogeneity across traffic conditions remains critical for t...
Given that correlation-based evidence is insufficient for designing safety performance countermeasures, identifying causal relationships and treatment effect heterogeneity across traffic conditions remains critical for targeted intervention development. This paper proposes a heterogeneous causal inference framework to investigate the heterogeneous treatment effects (HTE) of left-turn control mode on left-turn conflicts. Left-turn conflicts are identified using post-encroachment time and conflict speed thresholds. A heterogeneous causal graph is constructed to identify causal relationships, followed by Pearson correlation analysis to address redundancy among candidate confounders. Forest Doubly Robust Learning models quantify the HTE of Permissive-only and Protected-Permissive Left-Turn (PPLT) control modes using data from Bellevue, Washington (September 13-19, 2019). Results show PPLT control yielded 0.272 additional conflicts/h relative to Protected-only (compared with 0.510 for Permissive-only), with narrower confidence intervals suggesting more stable effects. Moreover, the main effects and interaction effects of specific safety-related factors are thoroughly analyzed based on Generalized Additive Model (GAM). Main effects analysis reveals both left-turn volume and opposing through volume significantly influence conflict frequency, with nonlinear interaction effects demonstrating that Permissive-only control exhibits rapid conflict escalation at opposing through volumes exceeding 400 (veh/h/lane), regardless of left-turn demand. PPLT effectively manages left-turn volumes below 200 (veh/h) even under high opposing through volumes (>800 veh/h/lane). In practice, comparisons with existing traffic engineering handbooks reveal discrepancies between current warrant criteria and empirically derived safety thresholds. These findings provide preliminary, jurisdiction-specific evidence that agencies may consider when reviewing and refining local left-turn phasing warrants.
A prediction-based framework for developing integrated pedestrian safety systems under high-risk scenarios.
Sun H, Liu S, Li Q … +6 more , Shen J, Gao X, Ji X, Zhang Y, Kou D, Nie B
Accid Anal Prev · 2026 Aug · PMID 42061230 · Publisher ↗
Pedestrians are among the most vulnerable road users and remain a primary focus of intelligent vehicle safety. In high-risk scenarios where avoidance is physically infeasible, vehicles' front-end structural designs (e.g....
Pedestrians are among the most vulnerable road users and remain a primary focus of intelligent vehicle safety. In high-risk scenarios where avoidance is physically infeasible, vehicles' front-end structural designs (e.g., active hoods) play a crucial role in injury mitigation. However, existing systems typically rely on contact-based sensors, which leave extremely short time for device deployment. To address this challenge, this study proposes a prediction-based integrated pedestrian safety framework. We constructed a vehicle-perspective, human-in-the-loop virtual reality dataset specifically enriched with high-risk boundary cases (near-misses and collisions). Using this data, we developed a short-horizon forecasting model (combining causal CNN and stacked LSTM), which achieved robust sub-meter accuracy in capturing abrupt pedestrian behavior. Compared with conventional contact-based systems, the proposed prediction-based triggering method provides a nominal 200 ms forecast horizon. This gives the active hood actuator more time margin to ensure it fully deploys before the pedestrian's head makes contact. Integrated simulation results demonstrated that this anticipation enabled the active hood to fully deploy before head contact, thereby realizing HIC reductions of up to 62.1% at 45 km/h (speed-dependent range: 5.9%-62.1%) and, importantly, enabling reversible electric actuation that is not feasible under reactive contact-triggered strategies. An evaluation on 110 real-world near-miss cases further confirmed trigger conservatism, with 0 observed false positives under the offline geometric check. Validation on real-world collision cases demonstrated the framework's robustness and transferability. Overall, this methodological framework supports a paradigm shift from post-impact detection to pre-impact prediction, enabling the next generation of proactive pedestrian protection systems.
Young novice drivers' hazard and speed management skills training: A systematic review.
Ratchaneepun B, Molesworth BRC, Molloy O
Accid Anal Prev · 2026 Aug · PMID 42048797 · Publisher ↗
Hazard Management (HM) and Speed Management (SM) skills are critical for safe driving. These skills are often lacking or underdeveloped in young novice drivers, resulting in their disproportionate involvement in road cra...
Hazard Management (HM) and Speed Management (SM) skills are critical for safe driving. These skills are often lacking or underdeveloped in young novice drivers, resulting in their disproportionate involvement in road crashes. Various training interventions have been developed to address this issue; however, their effectiveness varies due to differences in training methods, duration, and environments. This review aims to evaluate the current training approaches and their efficacy in enhancing young novice drivers' HM and SM skills. Following the PRISMA framework, this systematic review focused on peer-reviewed studies related to HM and SM skills training. Thirty-three articles met the predetermined inclusion criteria, supplemented by four additional studies identified through the snowballing approach. From the thirty-seven articles, twenty-four focused on HM, and thirteen on SM skills. Notably, seven studies on HM skills demonstrated effects on SM behaviour, though these effects were limited to the location of the hazard. No SM skills study demonstrated effects on HM skills. Multifaceted training interventions, such as error-based training, the combination of active and passive training, improved HM skills. Multiple training doses helped retain this training effect. In terms of SM, training with the provision of feedback proved to be effective in reducing young drivers' speeding behaviour, and a single dose was sufficient to see this effect extend for a prolonged period. Although various training methods have shown promise in improving HM and SM skills, transfer between the two skills remains limited. These findings provide guidance for future research to improve these two essential skills for young driver safety.
Understanding overtaking risk evolution patterns and their influencing factors based on trajectory data.
Bai J, Lee JJ, Zheng L
Accid Anal Prev · 2026 Aug · PMID 42044582 · Publisher ↗
Understanding the dynamic risk of overtaking behaviors is essential for improving highway safety and guiding adaptive driving strategies. This study develops a stage-based overtaking risk framework, capturing longitudina...
Understanding the dynamic risk of overtaking behaviors is essential for improving highway safety and guiding adaptive driving strategies. This study develops a stage-based overtaking risk framework, capturing longitudinal and lateral risks across Lane-change, Overtaking, and Back-to-lane stages. A comprehensive risk indicator is constructed by weighting risk metrics at specific stages, and overtaking trajectories are aligned via Dynamic Time Warping for time-series clustering. Three typical risk evolution patterns are identified: hesitant, aggressive, and robust, accounting for 42.45%, 10.07%, and 47.48%, respectively. These risk evolution patterns reveal distinct temporal peaks of risk: hesitant drivers exhibit dual peaks at both lane changes; aggressive drivers face the highest risk during Overtaking stage; while robust drivers complete the overtaking task with the lowest overall risk. To explain the formation of these patterns, random parameters multinomial logit models with heterogeneity in means are estimated using macroscopic traffic-flow indicators. Results show that truck presence significantly increases the likelihood of hesitant trajectories, while higher standard deviation of upstream speed exhibits a significant positive association with aggressive behaviors. Furthermore, heterogeneity analysis reveals that under higher upstream speeds, drivers become more sensitive to downstream disturbances, amplifying failed overtaking. Compared with conventional multinomial logit model, the counter model with random parameters with heterogeneity in means shows a substantially better fit, highlighting the necessity of accounting for unobserved heterogeneity in traffic flow. This study contributes a data-driven paradigm that integrates interpretable risk metrics, time-series clustering, and discrete choice modeling, offering practical insights for adaptive risk management in automated driving.
Exploring the heterogeneous relationship between abnormal driving events and freeways crash risk: A two stage analysis framework integrating causal inference and random parameters logit.
Zhou M, Yan Y, Wang T … +3 more , Kieu M, Yuan H, Zhang C
Accid Anal Prev · 2026 Aug · PMID 42035613 · Publisher ↗
Establishing the context-dependent relationship between abnormal driving events (ADEs) and crash risk is fundamental for developing next-generation proactive safety systems. However, current methodologies often oversimpl...
Establishing the context-dependent relationship between abnormal driving events (ADEs) and crash risk is fundamental for developing next-generation proactive safety systems. However, current methodologies often oversimplify by neglecting that ADE risk significance is influenced by operating contexts, and by ignoring contextual heterogeneity in event evolution within fixed pre-crash windows-both crucial for understanding crash causation and timing interventions. This study proposes a two-stage analytical framework. First, a causal forest with debiased machine learning (CF-DML) approach is employed to quantify the effect of ADE exposure on crash risk and assess its effect heterogeneity across contexts. Second, a random parameters logit model with heterogeneity in means (RPLHM) is used to characterize pre-crash temporal patterns as either ADE-dense or ADE-sparse. The analysis utilizes crash and ADE data from 2023 to 2024 on two freeways in Shandong Province, China, integrated with matched weather, traffic, and roadway geometry. Results show that hard acceleration and braking are positively associated with crash risk under pronounced context-specific heterogeneity, while sharp turning consistently correlates with reduced risk. Several contextual variables-including temperature, traffic volume, truck proportion, and speed dispersion-significantly moderate the ADE-crash relationship. The pre-crash ADE distribution is closely linked to temperature, wind speed, daytime, traffic volume, speed dispersion, and crash type. Accordingly, three prototypical risk contexts are identified: ADE-informative (where ADE exposure strongly indicates crash risk), pre-crash ADE-dense, and pre-crash ADE-sparse. Targeted traffic management countermeasures proposed for each context advance the mechanistic understanding of crash risk and provide a foundation for developing targeted, context-sensitive, and effective safety interventions.
Spatiotemporal diagnosis towards mismatch between traffic crash risks and safety governance in rapidly urbanizing cities.
Huang H, Yuan Z, Yang Y … +6 more , Li G, Salamatov VY, Han B, Kozhankov AY, Luo Z, Yu S
Accid Anal Prev · 2026 Aug · PMID 42035612 · Publisher ↗
The velocity of urban functional expansion often outpaces the evolution of safety governance, creating a critical spatiotemporal mismatch that undermines traffic safety. However, existing studies typically rely on static...
The velocity of urban functional expansion often outpaces the evolution of safety governance, creating a critical spatiotemporal mismatch that undermines traffic safety. However, existing studies typically rely on static snapshots, failing to capture the dynamic migration of these traffic accident risks or decode the evolving underlying mechanisms behind the governance deficit. To investigate the spatiotemporal evolution of this mismatch and analyze its underlying behavioral and structural causes, this research proposed a novel diagnostic framework integrating Geographically Weighted Regression with Random Forest interpretation. Using a decade of crash data (2013-2022) from a rapidly urbanizing Chinese city, we reconstructed the spatiotemporal trajectory of high-risk mismatch zones, where observed crash frequencies systematically exceed the levels predicted by static built environment factors. The empirical results indicate that this safety-governance mismatch manifests through three critical dimensions: spatiotemporal migration, mechanism heterogeneity, and spatial variation in crash patterns. First, spatially, the center of gravity of safety risks has shifted systematically from the mature city center to the expanding suburban fringe, empirically verifying the lagging governance hypothesis. Second, temporally, the dominant risk factors have fundamentally shifted. Behavioral factors and conflict patterns consistently dominate the risk hierarchy over the decade, while infrastructure factors remain marginal. This confirms that the governance deficit is a management deficit rather than a physical infrastructure gap. Finally, through mechanism deciphering, a differential diagnosis isolates the unique crash patterns of these high-risk zones. Unlike the congestion-induced passive errors in the core, these fringe zones are characterized by Improper Operation and severe Vehicle-Non-Motor conflicts, reflecting a critical gap in managing mixed traffic flows. The successful application of this diagnostic framework demonstrates its efficacy in identifying the blind spots of traditional static management. These findings challenge the traditional "build-first, manage-later" paradigm. Instead, a dynamic, precision-based governance framework is advocated to synchronize safety management with the spatiotemporal evolution of urban risks.
Differences in street crossing behavior between virtual and real environments for different groups of people under restricted use sites.
Zou T, Chen W, Chen Y … +2 more , Lin QF, Hu L
Accid Anal Prev · 2026 Aug · PMID 42034940 · Publisher ↗
The crossing behavior of pedestrians in virtual reality (VR) environments has been extensively used in traffic safety research; however, the kinematic differences compared to real-world situations remain insufficiently e...
The crossing behavior of pedestrians in virtual reality (VR) environments has been extensively used in traffic safety research; however, the kinematic differences compared to real-world situations remain insufficiently explored. This study constructed a virtual scene that accurately matches real road conditions and recruited 179 participants of various ages to conduct crossing experiments at three speeds: normal walking, fast walking, and normal running, within three distinct environments: real, spacious virtual, and narrow virtual. Using motion capture and questionnaires, parameters such as gait, trajectory, and limb angles, as well as subjective experiences, were quantified. The findings indicate that differences between real and virtual environments primarily manifest in speed, stride length, and trajectory deviation, while limb angles representing core movements showed no significant differences. In the spacious virtual environment, pedestrian trajectories maintained a straight line during the initial 50% (approximately 2.81 ± 0.15 m), with deviations increasing with age and speed; conversely, the narrow virtual environment resulted in significant distortions in movement patterns. Post-experiment, participants estimated the onset of dizziness at 16.9 ± 2.79 min. Analysis of individual differences revealed high consistency in movements within both environments, yet substantial variability existed among participants. Based on these findings, this research recommends conducting VR crossing experiments in spacious settings, at normal speeds, and limiting walking distance to approximately 2.8 m to ensure participant safety and comfort while obtaining data that closely reflect real-world pedestrian kinematics. This guideline provides empirical support for future high-fidelity traffic safety studies using VR technology.
Autonomous driving accelerated evaluation method for independent/dependent variables based on importance sampling.
Chen Y, Li A, Jiang H … +3 more , Ma S, Shen Q, Tao S
Accid Anal Prev · 2026 Jul · PMID 42025080 · Publisher ↗
The safety of Autonomous Vehicles (AVs) is crucial to the development of the autonomous driving field. The accelerated evaluation methods based on scenario simulation have become a hot research direction due to low test...
The safety of Autonomous Vehicles (AVs) is crucial to the development of the autonomous driving field. The accelerated evaluation methods based on scenario simulation have become a hot research direction due to low test cost and high test efficiency. Among them, the Importance Sampling (IS) method has attracted much attention. However, the IS method based on variable independence is limited in its application actual scenarios. To address this issue, this paper proposes an accelerated evaluation method compatible with both independent and dependent variables. For independent variables, a non-parametric Kernel Density Estimation (KDE) method is employed for distribution fitting, combined with IS and Bayesian Optimization (BO) to present a non-parametric sampling strategy. For dependent variables, by utilizing the Copula model and Maximum Likelihood Estimation (MLE) to capture the dependencies between variables, the joint distribution of dependent variables can be obtained, facilitating accelerated evaluation in conjunction with IS and BO. Furthermore, this paper selects the relative half-width as the convergence indicator and sets a reasonable threshold according to the probability distribution of variables. Through simulation testing in cut-in scenarios, this accelerated evaluation method not only effectively accommodates both independent and dependent variables but also achieves an increase in testing efficiency of over 200 times compared to Monte Carlo methods. Meanwhile, through the analysis of different variable combinations, it is found that this method can select the variable combination with the highest test efficiency according to the importance distribution of each variable combination, providing new ideas and technical support for the theory of accelerated evaluation.
Safety effects of take-over requests on vehicle conflicts in realistic mixed traffic simulation.
Sekadakis M, Oikonomou MG, Yannis G
Accid Anal Prev · 2026 Jul · PMID 42025079 · Publisher ↗
This study investigates how take-over-related dynamics in Automated Driving (AD) influence safety interactions in mixed traffic using a spatial Generalized Additive Model (GAM) applied to a calibrated microsimulation of...
This study investigates how take-over-related dynamics in Automated Driving (AD) influence safety interactions in mixed traffic using a spatial Generalized Additive Model (GAM) applied to a calibrated microsimulation of a real highway corridor in central Greece. The simulation reproduced automated driving transitions (Level 2/3) and take-over events (Take-Over Request (TOR) → driver response → manual driving → re-engagement) under baseline, lane-closure, and ODD-exit conditions. Vehicle trajectories were analyzed with the Surrogate Safety Assessment Model (SSAM) to extract conflict-level Time-to-Collision (TTC) indicators, enabling a quantitative assessment of take-over safety across an entire network rather than at the driver or maneuver level. The fitted spatial GAM effectively captured the variability in safety outcomes and revealed significant effects of Market Penetration Rate (MPR), take-over Time Budget (TB), scenario context, speed limit, vehicle type (first or second in the conflict), and conflict geometry. Higher automation shares and speed limits were associated with longer TTC, reflecting smoother and more stable interactions, whereas take-over events consistently reduced TTC regardless of TB, confirming elevated short-term risk during control transitions. The spatial smooth revealed localized low-TTC zones near the TOR area and merge lanes. Methodologically, this study combines microsimulation, SSAM-based surrogate safety analysis, and multivariate spatial GAM modeling to quantify TOR effects on AD-Human Driven Vehicle (HDV) interactions at the network level. The framework bridges simulation and statistical analysis by linking modeled behavior with interpretable safety outcomes. Substantively, the findings show that automation benefits scale with market share but are constrained by transition management and roadway geometry, emphasizing the importance of spatially aware, take-over-sensitive safety strategies in mixed traffic.
From ambiguity to precision: Digitized traffic rules for autonomous driving.
Shi R, Wang X, Zhu M … +3 more , Zhang J, Fu C, Zang J
Accid Anal Prev · 2026 Jul · PMID 42013591 · Publisher ↗
In the mixed traffic streams with interacting human-driven and automated vehicles (AVs), AVs must accurately infer and comply with the traffic rules followed by human drivers, which is a prerequisite for safety. However,...
In the mixed traffic streams with interacting human-driven and automated vehicles (AVs), AVs must accurately infer and comply with the traffic rules followed by human drivers, which is a prerequisite for safety. However, current traffic rules often use ambiguous expressions such as "not impede," which are easily interpreted by humans but difficult for AV implementation. To address this challenge, this study proposes a rule optimization framework that combines knowledge-based reasoning with data-driven optimization. The methodology is built on safety‑critical event (SCE) data collected from six intersections across three Chinese cities. The methodology involves three stages: (1) Semantic classification is conducted to categorize traffic rules; (2) For each category, Metric Temporal Logic is used to formalize natural language rules. The underspecified parameters are supplemented using domain knowledge extracted from 26 regulatory documents across ten countries/organizations. Initial parameter ranges are then estimated by incorporating behavioral patterns observed in SCEs; (3) A genetic algorithm is employed to calibrate the optimal parameters within simulation environments reconstructed from real-world SCEs. Experimental evaluations show that: (1) Relative to human drivers, the rule-integrated planner enhances safety performance by over 90% while maintaining both operational efficiency and ride comfort; (2) When embedded into existing planners on the CommonRoad and INTERACTION benchmarks, the optimized rules consistently reduce collision risks, confirming strong generalizability; (3) The final performance is primarily driven by parameters that control turn ranges, temporal safety thresholds, and longitudinal distances. Overall, this study introduces an interpretable and transferable rule optimization framework that improves safety and strengthens the alignment of AV behavior across different driving environments.
Exploring the heterogeneity in impacts of 30 km/h speed interventions: A subgroup meta-analysis based on a systematic international review.
Ambros J, Šípek M, Křivánek V … +4 more , Jurewicz C, Turner B, Elvik R, Valentová V
Accid Anal Prev · 2026 Jul · PMID 42001676 · Publisher ↗
The 30 km/h speed zones or limits have been implemented worldwide. However, the knowledge of their effectiveness is limited and often based only on European studies. While impacts on speed and safety were generally known...
The 30 km/h speed zones or limits have been implemented worldwide. However, the knowledge of their effectiveness is limited and often based only on European studies. While impacts on speed and safety were generally known, additional impacts (e.g., on traffic volume, noise, emissions) have been studied less frequently, and with inconclusive results. In addition, the impacts were strongly varied, often based on less rigorous forms of literature review, with moderators behind the variations largely unexplored. To address these gaps, the present paper used systematic and unrestricted study selection with random-effects meta-analysis, including the subgroup analysis of moderating variables. This approach provided more robust effect sizes for crash reduction (17%), injury reduction (18%), and speed reduction (-4.68 km/h), also showing that effects on traffic volume were not statistically significant. Subgroup analysis highlighted statistically significant moderators of success for 30 km/h speed limit applications. For example, larger reductions of crashes were associated with 30 km/h traffic calming zones (instead of sign-only schemes), large area interventions, and higher baseline speeds. Some results were consistent with existing meta-analyses, but some were incomparable, likely because previous reviews may have overestimated the real impacts. This could be the case for traffic noise, where this meta-analysis estimate was approximately -1 dB, which is generally not noticeable. The paper provides important updates on the road safety and speed impacts of 30 km/h speed interventions, along with important moderators (as well as contraindicators) of success, which will be useful in future planning and evaluation of such speed limit applications.
Spatial-temporal risk field-based coupled dynamic-static driving risk assessment and trajectory planning in weaving segments.
Ma G, Sun B, Liang H … +2 more , Yang W, Zhou H
Accid Anal Prev · 2026 Jul · PMID 42001675 · Publisher ↗
As connected and automated vehicles (CAVs) gradually penetrate the existing transportation system, the inherent turbulence within weaving segments is expected to be mitigated through CAV technologies. However, despite ma...
As connected and automated vehicles (CAVs) gradually penetrate the existing transportation system, the inherent turbulence within weaving segments is expected to be mitigated through CAV technologies. However, despite making progress in capturing static and dynamic risk factors, traditional CAV technologies still lack foreseeability for dynamic risks. This leads to suboptimal results in trajectory planning, thereby hindering the maximization of expected benefits. To fill these gaps, we first propose a spatial-temporal coupled risk assessment paradigm by constructing a three-dimensional spatial-temporal risk field (STRF). Specifically, we introduce spatial-temporal distances to quantify the impact of future trajectories of dynamic obstacles. We also incorporate a geometrically configured specialized field for weaving segments to constrain vehicle movement directionally. To enhance the STRF's accuracy, we further developed a parameter calibration method using real-world aerial video data, leveraging YOLO-based machine vision and dynamic risk balance theory. A comparative analysis with traditional risk field shows that the STRF possesses superior risk foreseeability. Building on these results, we final design a STRF-based CAV trajectory planning method in weaving segments. We integrate spatial-temporal risk occupancy maps, dynamic iterative sampling, and quadratic programming to enhance safety, comfort, and efficiency. By incorporating both dynamic and static risk factors during the sampling phase, our method ensures robust safety performance. Additionally, the proposed method simultaneously optimizes path and speed using a parallel computing approach, reducing computation time. Real-world cases show that, compared to the baseline schemes, and real human driving trajectories, our method significantly improves safety, reduces lane-change completion time, and minimizes speed fluctuations.
Demographic and causal patterns in child cyclist head Injuries: Informing helmet test methods.
Dawber W, Baker CE, Sharp D … +2 more , Agrawal S, Ghajari M
Accid Anal Prev · 2026 Jul · PMID 42001674 · Publisher ↗
Children's cycle helmets are certified using the same impact conditions as adult helmets, which can overlook important factors contributing to child head injuries. Our objective is to identify common patterns in traumati...
Children's cycle helmets are certified using the same impact conditions as adult helmets, which can overlook important factors contributing to child head injuries. Our objective is to identify common patterns in traumatic brain injury pathologies, age, sex, riding environment, cause of injury, helmet use, and helmet injury reduction in child cyclists to inform child-specific test methods. We reviewed 48,074 head injury cases in cyclists under 17 years across 24 studies. An aggregate data meta-analysis was conducted to identify recurring patterns overall and in studies with a high proportion of severe injuries (n = 3,542 cases). Cases most often involved male riders (71.8%, CI: 71.6-72.1%), aged 10-13 years (40.2%, CI: 39.1-41.3%), occurring on paved roads (75.0%, CI: 74.2-75.9%) without prior collision (84.4%, CI: 84.1-84.8%). Injuries were predominantly intracranial (73.7%, CI: 71.6-75.8%). Studies with mostly severe injuries included significantly more males, on-road incidents, motor vehicle collisions, intracranial haemorrhages, and skull fractures. Helmets reduced odds of head injuries (OR = 0.44, CI = 0.41-0.47), but the efficacy was lower for severe injuries (OR = 0.61, CI = 0.58-0.65), which contrasts most findings for adult helmets. The identified factors associated with severe injuries in child cyclists, such as vehicle collisions and intracranial injuries with rotational mechanisms, are not represented in current child helmet test procedures. This work provides a foundation for further work aimed at quantifying representative head impact biomechanics in typical and severe child cycling incidents, with the ultimate goal of developing helmet test procedures tailored specifically to children.
A comprehensive analysis of contributing factors in bus crashes: integrating passenger injury severity and vehicle damage severity using the Frank copula model.
Dong Y, Huang H, Wang J … +2 more , Jin J, Liu J
Accid Anal Prev · 2026 Jul · PMID 41996738 · Publisher ↗
Bus crashes remain a significant public safety concern due to their potential to cause both passenger injuries and substantial vehicle damage. These two severity outcomes are interrelated, as the physical forces in high-...
Bus crashes remain a significant public safety concern due to their potential to cause both passenger injuries and substantial vehicle damage. These two severity outcomes are interrelated, as the physical forces in high-impact crashes contribute simultaneously to occupant harm and structural damage. However, most existing studies model these outcomes independently, overlooking their statistical dependence and shared risk factors. To address this gap, this study employs a copula-based modeling framework to jointly analyze passenger injury and vehicle damage severity using bus crash data from Maryland (2015-2022). The joint estimation results reveal distinct sets of significant predictors for passenger injury severity and vehicle damage severity. For injury outcomes, the most influential variables include driver fault, airbag deployment, and safety equipment usage, underscoring the critical role of both human error and protective systems in shaping injury levels. In contrast, vehicle damage severity is more strongly associated with vehicle type, movement status at the time of impact, and mechanical condition. The inclusion of the vehicle type variable shows that school buses, despite being structurally safer, are frequently involved in crashes due to high exposure, contributing to notable damage risks. Additionally, adverse vehicle conditions and airbag deployment exhibit a strong association with disabling or destroyed damage, highlighting the role of structural integrity and energy dissipation mechanisms in post-impact outcomes. These patterns, revealed through the Copula-MNL model, reflect not only the different underlying risk structures of the two severity measures but also their shared dependence on critical safety-related factors.
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