This study examined differences in the frequency, severity, and distribution of officially recorded traffic sanctions before and after attendance at attitude-change-based road safety awareness and re-education courses integrated into the Penalty Point System in Catalonia. A retrospective longitudinal pre-post design was applied to administrative records from the complete 2017 cohort of 14,022 course participants. The main sanction-profile analyses focused on 9,886 drivers with at least one pre-course sanction, accounting for 36,546 pre-course and 23,022 post-course sanctions. Period-by-category differences were examined using Pearson chi-square tests of homogeneity, Cramér's V, and aggregate log-linear Poisson models. During follow-up, 37.1% of drivers with previous sanctions had no further officially recorded sanction, and the total sanction count in this group was 37.0% lower. Alcohol- and drug-related sanctions showed the largest relative decline (-77.9%), followed by safety-device and driver-related sanctions. Mobile phone-related sanctions declined by 62.0%. Speeding showed a smaller reduction (-17.2%) and represented a larger share of the post-course profile. Significant distributional differences were found for the main offence categories, offence severity, driver-related offence subtypes, and serious speeding subtypes (Cramér's V = 0.128-.210). Sanctions recorded in average-speed enforcement sections and urban access areas increased despite declines in other speeding categories. These findings show that a single recidivism indicator would conceal meaningful differences in offence type, severity, and speeding context. Because the study was observational, lacked a comparison group, and relied on detected offences, the patterns cannot be attributed causally to course attendance or interpreted as direct evidence of behavioural or attitudinal change.
Driving risk assessment is crucial for advancing transportation safety, particularly in the context of increasing vehicle automation and electrification. This study proposes a modified two-dimensional time-to-collision (2D-MTTC) measure that incorporates relative acceleration in two-dimensional space to better characterize driving risk across different types of passenger vehicles. The proposed 2D-MTTC is validated through a two-stage validation process using different datasets to show its effectiveness and performance compared with the benchmark two-dimensional time-to-collision (2D-TTC): i) macro-level validation by computing the correlation between conflict risk and historical crash records of the road network; and ii) micro-level validation by detecting critical risk events in real conflicts and crash trajectories. After validation, the driving risk captured by 2D-MTTC between different types of passenger vehicles is examined using the Third Generation Simulation dataset (TGSIM). Specifically, the identified driving risks are compared between automated vehicles (AVs) and human-driven vehicles (HDVs), as well as between electric-based automated vehicles (EAVs) and internal combustion engine-based automated vehicles (ICEAVs). The results demonstrate the effectiveness of 2D-MTTC and its improved performance relative to 2D-TTC in identifying driving risks and capturing their duration and intensity. The findings further reveal that the driving risk characteristics among AVs and HDVs, EAVs and ICEAVs are significantly different and urge the consideration of vehicle types in proactive safety analysis.
This study develops a cross-facility hierarchical Bayesian marginal-copula framework for jointly modeling rear-end crash counts and three connected-vehicle (CV)-derived longitudinal conflict measures: time to collision (TTC) conflicts, deceleration rate to avoid a crash (DRAC) conflicts, and time to collision with disturbance (TTCD)-based conflict risk. The framework is designed for cross-facility generalization between freeways and arterials: hierarchical marginal models with facility-type random effects enable partial pooling and principled information sharing across facility types, while an inference-for-margins (IFM)-estimated multivariate Gaussian copula characterizes crash-conflict associations among the four safety indicators while preserving outcome-specific marginal distributions. The empirical analysis combines police-reported rear-end crashes with CV trajectory data aggregated to freeway and arterial segments in Ann Arbor, Michigan. Results show strong exposure effects: an e-fold increase in annual average daily traffic (AADT) is associated with an approximately threefold increase in expected crash counts, whereas an e-fold increase in CV exposure increases TTC and DRAC conflict counts by about 2.5 to 2.6 times and TTCD-based conflict risk by roughly a factor of 4 to 5. The conflict measures are more strongly concordant with one another (Kendall's τ≈0.47 for TTC-DRAC) than with crashes (all crash-conflict τ<0.20), suggesting that TTC and DRAC capture closely related longitudinal interaction mechanisms whereas crash occurrence is only weakly associated with any single conflict metric. Cross-facility prediction experiments show that naive freeway-to-arterial extrapolation can substantially degrade predictive performance; for example, TTC-conflict root mean square error (RMSE) increases by about 89%. By contrast, the hierarchical pooled specification mitigates this degradation and achieves arterial predictive accuracy comparable to an arterial-only benchmark, with sensitivity results indicating stable performance under moderately reduced arterial training samples.
Existing approaches for concrete scenario generation and assessment primarily focus on trajectory-level interactions while neglecting physical perception constraints, such as sensor field-of-view limitations and occlusions from surrounding objects, leading to unrealistic risk representations. To fill this gap, this study proposes a framework that integrates vehicle dynamics, environmental constraints, and perception limitations into a unified generation process. Specifically, vehicle trajectories are parameterized using a kinematic bicycle model and perturbed through controlled variations in longitudinal acceleration and steering angle. A voxel-based surrogate perception metric is introduced to efficiently evaluate cooperative perception (CoP) by modeling line-of-sight occlusions, which achieves a balance between computational efficiency and accuracy. By modeling perception limitations, the framework generates not only risky but also CoP-adverse scenarios. A multi-constraint optimization framework driven by the genetic algorithm is proposed, which ensures that generated scenarios satisfy physical feasibility, road compliance, collision avoidance, and degraded perception conditions, while increasing interaction risk between agents. Experimental results based on real-world trajectory datasets demonstrate that the proposed approach can effectively generate diverse and high-risk scenarios with reduced CoP performance and satisfactory computational efficiency. This framework may not only help generate CoP-adverse scenarios for the assessment and evaluation of vehicle-infrastructure CoP systems, but also offer a tool for the construction of scenario library to support end-to-end learning-based methods.
In the context of connected vehicles, head-up displays (HUD) have been widely used to provide warning information and enhance driving safety. However, existing systems still lack effective ways to present information about inherent road risk, which is particularly crucial for truck drivers in complex road scenarios. To address insufficient support for truck drivers’ evasive maneuvers in scenarios involving overlapping road-vehicle risk factors, this study focused on a high-risk situation involving sudden braking by a preceding vehicle on a curve. Based on the International Road Assessment Programme (iRAP) methodology, a Road-risk Head-Up Display (RHUD) warning system integrating quantified road-risk information was developed. A driving simulation experiment was conducted using a scenario based on the Qingyin Expressway prototype, in which a traditional HUD and an enhanced RHUD were implemented for comparison. Thirty-seven professional truck drivers completed simulated driving tasks involving sudden lead-vehicle braking on a curved segment under both display conditions. The longitudinal safety margin index was selected as the primary metric for risk-avoidance behavior. Survival analysis showed that, in the scenario of sudden braking before a curve, the RHUD significantly improved drivers’ risk-avoidance behavior (χ2 = 7, p = 0.008), yielding improvements of 27.68%, 32.67%, and 37.94% at the 25th, 50th, and 75th percentiles, respectively. Further analysis revealed that under the RHUD condition, the effects of demographic factors such as age were substantially attenuated, whereas driving performance was primarily governed by situational variables, particularly driving experience and initial speed. This suggests that structured road-risk information helps reduce individual differences in drivers’ responses to high-risk events. These findings demonstrate the effectiveness of incorporating road-risk information into truck HUD warning design and confirm the feasibility of linking iRAP-based quantitative road-risk information with in-vehicle warning systems. The proposed approach provides empirical support for optimizing truck warning strategies based on road-risk assessment results and offers practical implications for improving safety in complex road environments.
Multimodal human-machine interaction (HMI) is increasingly implemented in smart cockpit systems, yet empirical evidence on how specific modality configurations jointly shape driver workload and performance remains limited. From a neuroergonomic perspective, this study examined how variations in combinations of visual layout, auditory feedback, and haptic interaction were associated with driver workload regulation and vehicle-control performance in a controlled driving simulation. Twenty-nine licensed drivers performed secondary interaction tasks under systematically varied multimodal configurations, while neural activity, eye movements, physiological responses, driving performance, and subjective workload were concurrently recorded. The results indicate that different multimodal configurations do not produce uniform patterns of workload and performance outcomes; instead, different configurations elicit distinct patterns across neural, physiological, and behavioral indicators, suggesting differences in how cognitive demands may be distributed across perceptual, motor, and executive processes. Configurations characterized by more stable visual attention and direct interaction were associated with different patterns of processing demands and vehicle-control responses. Cross-indicator analysis further showed that relationships among measurement domains were selective rather than uniformly convergent. Notably, subjective workload did not always align with objective indicators, suggesting that different measures may capture partially distinct aspects of driver state. Overall, the findings highlight that the effectiveness of multimodal HMI depends on how modalities are configured rather than simply on the presence of multiple modalities, and underscore the value of interpreting driver workload through complementary rather than interchangeable indicators.
Analyzing the factors influencing pedestrian crashes at crosswalks from both drivers’ and pedestrians’ visual perspectives is essential for improving pedestrian safety. However, current studies have not sufficiently considered the holistic visual characteristics of road environments and have provided limited practical guidance for crosswalk safety improvement. To address these gaps, this study integrates XGBoost with SHAP values to conduct an interpretable analysis of pedestrian crashes at crosswalks, considering holistic characteristics of the visual road environment. Appearance, depth, and color features were extracted from street-view images for 373 crosswalks, and hierarchical clustering was applied to classify them into three distinct visual clusters representing holistic environmental characteristics. These visual clusters were then combined with road design, crosswalk, traffic control, exposure, and contextual variables to develop an XGBoost model for predicting whether pedestrian crashes occurred at each crosswalk, achieving an accuracy of 0.866 and an AUC of 0.896. SHAP values were then used to identify the relative importance of each independent variable, the specific effects of individual variables, and the joint effects between variables. Road width and visual clusters emerged as important factors, with wider roads associated with a higher predicted crash likelihood and visually balanced environments associated with a lower predicted crash likelihood. Notable joint effects were also observed between road width and visual clusters. The proposed methodology helps provide practical insights for crosswalk design and pedestrian safety improvement.
Motorcycle casualties in Thailand are strongly concentrated on a small proportion of the road network, yet road infrastructure assessment at the national scale remains constrained by incomplete road-inventory data and the cost of field audits. This study develops an integrated framework for identifying motorcycle crash hotspots and examining their visually observable road-environment correlates. Motorcycle crash records from the Thailand Road Accident Management System (2019-2024) were analysed using Network Kernel Density Estimation (NKDE) on 100 m road segments. The NKDE output is interpreted as severity-weighted crash concentration, not exposure-normalised crash risk. For hotspot and matched control segments, representative Mapillary Street-View Imagery (SVI) was processed using a Vision-Language Model (VLM) pipeline to extract 27 iRAP-aligned attributes. Only manually validated, reliable VLM-derived attributes were retained and combined with route-level traffic-volume proxies in an XGBoost classifier, with SHAP used to interpret feature contributions. The selected NKDE configuration showed strong concentration: the top 1% of network length captured 52.2% of severity-weighted motorcycle crashes. Model interpretation indicates that hotspot classification is associated with commercial access activity, traffic-exposure proxies, constrained median and roadside environments, and rigid roadside objects. These associations are interpreted cautiously as hotspot indicators rather than causal effects. The framework provides a scalable approach for linking realised motorcycle casualty concentration with SVI-derived infrastructure hazard indicators in data-constrained settings.
Hazard prediction and attentional orienting play a critical role in road safety interventions aimed at reducing accident risk. Two complementary studies employed the Hazard Prediction-Orienting (HP-O) task to examine the independent contributions of driving experience and attentional orienting to hazard prediction performance. The studies integrated measures of behavioural accuracy, eye movements, and physiological responses. Attentional orienting was manipulated through invalid, valid, and simple trials. Study I compared 20 novice and 20 experienced drivers on behavioural measures, and a sample of 17 per group on psychophysiological measures following the exclusion of six participants due to signal artefacts. Study II examined behavioural performance and eye movement measures within the HP-O paradigm in a sample of 17 inexperienced and 18 experienced drivers. Across both studies, driving experience and trial type independently predicted HP-O task accuracy: experienced drivers consistently outperformed inexperienced drivers, and accuracy was highest in simple trials, followed by valid and invalid trials. Psychophysiological significant findings from Study I showed that phasic cardiac responses were largest during invalid trials, reflecting an orienting response to unexpected or incongruent information. Eye-tracking data from Study II revealed that inexperienced drivers exhibited longer fixation durations and higher detection rates of potential or irrelevant hazard areas, suggesting less efficient attentional allocation. In contrast, experienced drivers produced larger saccadic amplitudes and more fixations per trial, reflecting broader and more flexible visual scanning strategies. The findings demonstrate that misleading attentional cues impair hazard prediction, and driving experience is associated with differences in hazard prediction and visual search performance.
Right-hook crashes in right-hand traffic where cyclists going straight are struck by right-turning vehicles pose a major safety concern. This study aims to establish to which extent such conflicts may stem from non-driving-related tasks (NDRT), low saliency of cyclists (covertness), or drivers' insufficient knowledge of applicable rules. Forty-four drivers participated in a fixed-base driving simulator study using an extended reality (XR) setup with integrated eye tracking. Participants were stratified by urban cycling experience (cyclist-drivers vs. drivers) and self-reported driving style (cautious vs. assertive). Each drove an urban route including 12 right-turn-on-yield scenarios, with and without NDRT. Observed visual sampling was combined with a questionnaire-based rule knowledge assessment to examine whether scanning failures were due to workload or lack of rule knowledge. NDRT engagement primarily reduced default glances while glances to relevant areas (Left, Right, Over Shoulder) were preserved. Over-the-shoulder checks were rare overall. In 85 % of right-turns, no such glance occurred before turning, regardless of NDRT status. Rule knowledge mirrored these patterns, with drivers being more likely to correctly indicate the requirement to yield to salient crossing traffic streams of cars (96 % correct) or cyclists and pedestrians (81 % correct) than non-salient crossing bicycle or pedestrian traffic (46 % correct). Drivers with cycling experience scored slightly better overall but still missed nearly half of the non-salient yielding requirements. The findings indicate that gaps in rule knowledge contribute to failures to check for cyclists. Countermeasures should prioritise systemic interventions, complemented by education, rather than solely relying on behaviour-focused measures.
This study develops and validates a smartphone-based framework for automatically detecting emergency maneuvers, strong jolts, and crashes involving electric scooters and electric bicycles. Detection criteria were established through controlled track experiments and subsequently evaluated using data collected during a naturalistic riding study involving 119 participants and more than 26,000 km and 1,600 h of riding, combining accelerometer, gyroscope, GPS, and video recordings. Threshold-based detection criteria were defined using variables selected for their physical relevance and ability to discriminate between target and non-target situations. Hard braking, sharp turns, strong jolts, and crash-related events were identified using combinations of acceleration, jerk, rotational dynamics, and post-event vehicle motion. Video review showed that 74% of hard-braking detections corresponded to harsh-braking maneuvers, 64% of sharp-turn detections reflected genuine avoidance maneuvers, and 91% of strong-jolt detections were associated with infrastructure features. Video verification of collision candidates confirmed several reported and previously unreported impacts, including collisions with other road users and single-vehicle falls. Application of the framework to the naturalistic dataset revealed marked differences between vehicle types. E-scooter users experienced higher rates of hard braking and strong jolts than e-bicycle users, reflecting behavioral differences and vehicle characteristics. Illustrative mapping examples showed that detected events and rider-reported hazardous situations could occur in close proximity, suggesting opportunities for future spatial analyses of micromobility safety. Although additional validation on larger crash datasets is required, the results demonstrate that threshold-based approaches can provide meaningful indicators of rider safety, support large-scale monitoring of micromobility risks, and contribute to infrastructure and transport-safety assessment.
The objective of this study was to evaluate the effect of both cognitive and visual distraction on drivers' gaze behaviour and takeover performance during an SAE Level 2 automated drive. A driving simulator study was conducted where drivers needed to take over control during a safety critical scenario i) while engaged in an auditory version of the 2-back task (cognitive distraction), ii) during ambient occlusion of the driving scene (visual distraction) or iii) a combination of both. In line with previous studies, results showed that, under the 2-back task, drivers showed a lower horizontal dispersion of gaze for scanning the environment. In the ambient occlusion condition, drivers compensated for the temporary absence of the driving scene by dispersing their gaze vertically and towards offroad areas of the environment. In terms of their takeover performance, the results found no significant differences between cognitive and visual distraction manipulations alone. However, drivers' performance was significantly worse, when both manipulations were combined. The findings suggest that both visual and cognitive distraction may tax drivers' cognitive resources and consequently impact their takeover performance. This finding is relevant for future development of driver monitoring systems, which should consider the impact of cognitive load on drivers' performance, even if their eyes are facing towards the road.
Misuse of advanced driver assistance systems (ADAS) has led to fatalities, and training could be a promising countermeasure. Research on ADAS training for young drivers is limited. The current study investigated the effectiveness of a video-based ADAS training for young drivers (aged 18-21) and adult drivers (aged 25-67). The training spent similar time introducing ADAS limitations (situations where ADAS may not work) and drivers' responsibilities (paying constant attention and taking over when needed). Sixty-three participants were recruited (balanced gender and age groups), with 31 receiving training and 32 receiving no training. Participants drove in a simulator using ADAS that controlled vehicle speed and steering. To explore whether training would be beneficial even when a driver monitoring system (DMS) was implemented, all participants had a DMS that alerted them when they had been looking away from the road for more than a few seconds. Glancing behavior, manual interaction with a secondary task, and takeover performance were analyzed. The training was found to have benefits for both age groups (e.g., less time looking at the secondary task, longer minimum gap time), but additional benefits were observed for young drivers (shorter glance duration and fewer long glances to the secondary task). These findings can inform training design and implementation to promote safer use of ADAS. For example, policymakers could consider incorporating ADAS training into early licensing stages (i.e., driver education and knowledge testing) to reach young drivers. The short training videos could also be distributed to consumers through dealerships or media campaigns.
This paper presents an analysis of lane changing manoeuvres under real traffic conditions, focusing on how drivers adapt their behaviour across different driving regimes. Using naturalistic driving data collected on a high-capacity road in Madrid (Spain), the research explores the role of traffic context in decision-making focusing on gap acceptance, vehicle dynamics, safety metrics and motivational factors. To analyse these dynamics, drivers were exposed to six controlled traffic configurations designed to reproduce representative real-world highway scenarios with different surrounding vehicle distributions, inter-vehicle distances, and relative speeds. Driver behaviour was analysed across three distinct manoeuvre phases, corresponding to car-following, anticipation, and lane change execution. The results show that traffic density and the surrounding environment significantly shape driver strategies. Higher traffic complexity leads to longer anticipation phases, greater dispersion in timing, and reduced safety margins. While Time-to-Collision (TTC) offers general insights into risk, gap-based metrics proved more stable and informative for predicting the actual execution of the manoeuvre. This study contributes to the understanding of context-dependent lane-change behaviour using controlled naturalistic driving scenarios. The findings support the development of predictive models and the design of advanced driver assistance systems (ADAS) and automated vehicles, enabling safer and more adaptive lane-change algorithms that better account for driver intent and traffic dynamics.
The Operational Design Domain (ODD) defines the conditions under which automated driving and driver-assistance systems are expected to operate. This study evaluates the ODD of a camera-based Lane Support System (LSS) using direct Mobileye 6.0 lane-detection quality outputs. A large-scale hybrid factorial-observational field design covered 6 different Light × Weather combinations across 9,351 road sections on two-lane rural roads with wide variability in lane-marking retroreflectivity (RL) and road horizontal alignment and cross section characteristics. Statistical and machine-learning classification models were calibrated and compared to analyze the relationships between lane-marking quality and environmental, road, and traffic features. An AutoML-LightGBM pipeline with SMOTE-based class-imbalance treatment achieved the best accuracy of 0.81. SHAP analysis identified low RL, rain, night conditions, narrow lanes, and high curvature as contributors to critical detection conditions. Because standard ML is optimized for prediction rather than causal inference, Double Machine Learning was added to estimate adjusted effects from observational data. Higher RL, higher speed, and wider lanes were associated with better expected Mobileye quality scores, whereas rain, night conditions, and higher curvature were associated with lower detection quality. SHAP dependence and conditional SHAP analyses supported the identification of maintenance-mitigable infrastructure constraints and harder environmental/geometric ODD limits. One practical result is that RL transitions from low-quality detection mainly occur within 120-150 mcd/(m2·lx) across the majority of environmental and physical conditions, although this transition is less evident under sharp curvature or rain.
Railway perimeter safety events are rarely determined at the moment an external disturbance enters the operating boundary; their consequences emerge through subsequent state transitions that are recorded only incompletely in accident narratives. This study develops a text-derived Bayesian state-transition framework that reconstructs these narratives as auditable probabilistic state flows. Grounded coding defines the causal states, sentence-level coding identifies directed transitions, and first- and second-order Bayesian models are combined with absorbing-state analysis to characterize local continuation, path dependence and eventual outcome convergence. Among 335 screened cases, 281 contributed transition evidence to a 90-state model. The results reveal that path dependence is selective rather than universal. Preceding mechanisms can strongly redirect the successor distribution of the same intermediate state, yet adding preceding-state information reduces out-of-sample performance when the corresponding triadic evidence is absent and improves it only when that path is sufficiently represented. More historical context is therefore not inherently more informative under sparse narrative evidence. A complementary pattern emerges between evolutionary distance and outcome certainty: upstream and mid-chain states retain longer remaining paths but unstable terminal destinations, whereas near-outcome states have short remaining paths and highly stable outcome convergence. The framework therefore shifts perimeter-safety analysis from identifying hazardous factors to determining when history changes the future of a state and when that future has become sufficiently constrained for targeted intervention.
This study investigates how task complexity and coping capacity interact to influence crash risk within the framework of the Safety Tolerance Zone (STZ). The STZ is defined as a dynamic condition in which the driver remains within acceptable boundaries and is operationalised primarily through headway, which was considered as an indicator of crash risk. This work aims to identify the interaction of road, vehicle and driver-related factors to the estimation of task complexity, coping capacity and risk. To this end, data from a naturalistic driving experiment involving 135 drivers and 31,954 trips collected over a four-month period were analysed across experimental phases incorporating real-time and post-trip interventions. Generalised Linear Models were used to examine the effect of explanatory variables on key driving behaviour indicators, while Structural Equation Models were applied to estimate the relationships among the latent constructs of task complexity, coping capacity and risk, expressed through STZ phases. The results showed that environmental factors, including time of day, weather, distance and duration, were positively associated with task complexity and increased crash risk. Driver-related and vehicle-related state factors influenced coping capacity, which was generally negatively associated with risk. The findings also indicated that the relationship between task complexity and coping capacity is dynamic, suggesting behavioural adaptation under more demanding driving conditions. Moreover, the intervention phases showed that real-time warnings and post-trip feedback contributed to safer driving behaviour, including greater headway and fewer harsh events. Overall, the study highlights the potential of data-driven interventions to improve road safety and support more effective driver assistance systems.
Driving cessation in older adults with dementia is associated with reduced independence, social isolation, and accelerated cognitive decline, yet determining fitness to drive remains challenging. This qualitative study explored older adults' attitudes toward driving safety decisions and their preferred responses if informed that they may no longer be safe to drive. Twenty-two older adults, including individuals with mild cognitive impairment (MCI) at risk for dementia and cognitively healthy controls, participated in walking-talking interviews following cognitive and hazard perception screening assessments. Interviews explored participants' views on what should happen next if test results suggested they were unsafe to continue driving. Audio-recorded interviews were transcribed verbatim and analysed using thematic analysis. Six themes and sixteen codes were identified. Participants with MCI demonstrated greater acceptance of driving cessation, and sought confirmatory assessments, whereas healthy control participants prioritised transparency about test results, openness to remediation through lessons or vehicle adaptations, and preservation of independence. These findings highlight the importance of personalised interventions and the potential for non-invasive in-car technologies to support safe driving in individuals with early-stage dementia. This study highlights the value of qualitative methods in understanding the nuanced perspectives of older drivers and informs future strategies for managing driving cessation in cognitively impaired individuals at risk for dementia.
Integrated active-passive safety plays an important role in improving vehicle crash safety, especially in hazardous scenarios where collision avoidance may become dynamically infeasible. Conventional active-safety strategies mainly focus on collision avoidance and may not fully account for occupant-injury outcomes once an impact becomes unavoidable. To address this issue, this study proposes a stability-constrained injury-aware Model Predictive Path Integral (SCI-MPPI) framework for integrated active-passive safety decision-making. A driving safety domain is constructed based on nonlinear vehicle dynamics, tire-force saturation, and braking-steering stability boundaries, and is embedded into trajectory planning as a dynamic feasibility constraint. High-fidelity crash simulations and THOR anthropomorphic test device (ATD)-based occupant-injury responses are used offline to train a physically partitioned radial basis function (RBF) surrogate model for rapid online Weighted Injury Criterion (WIC)-based injury prediction in frontal asymmetric collisions. SCI-MPPI then optimizes sampled trajectories while considering road boundaries, obstacle avoidance, vehicle-footprint constraints, stability limits, and injury-related costs. Simulation results show that SCI-MPPI generates collision-free trajectories and smooth dynamic responses in avoidable scenarios. Comparative evaluations against baseline methods indicate that SCI-MPPI shows a favorable balance between nonlinear trajectory-optimization performance and computational efficiency. In unavoidable-collision cases, SCI-MPPI achieves a mean per-case first-impact WIC reduction of 18.56% while keeping injury-oriented maneuvers within the driving safety domain. These results indicate that SCI-MPPI supports the coordination of active obstacle avoidance and first-impact injury mitigation within stability-constrained maneuvering limits, providing guidance for improving collision safety in future intelligent-vehicle systems.
Train derailments are the most common type of mainline railroad accident in the United States and often result in substantial property damage and operational disruptions. Among track-related derailment causes, buckled track and broken rails or welds present the highest risk. Buckled-track derailments result from compressive thermal stresses, whereas broken-rail derailments are most often associated with metallurgical defects or fatigue cycles, and are not necessarily attributed to tensile stress. This study focuses on buckled-track derailments due to their direct relation to rail thermal behavior and their challenging detection, as they rarely interrupt signal circuits. Buckled-track derailment risk was analyzed using the Federal Railroad Administration's (FRA) accident database and track classification system that is based on maximum allowable train speed and minimum geometric conditions. Buckled-track derailment risk was studied from 2000 to 2024 across FRA Track Classes. Additionally, ambient and rail temperatures at and near the time of buckled-track derailments were analyzed. Results indicated that buckled-track derailment risk has decreased in recent years. However, the proportion of buckled-track derailments relative to all track-caused derailments has increased. Analysis of temperature trends revealed that 62 % of buckled-track derailments on Class I mainlines and sidings between 2011 and 2024 occurred at rail temperatures higher than 90 % of all days that year, demonstrating a strong association between extreme heat and derailments. Furthermore, comparison of rail temperature with design rail neutral temperature (RNT) revealed that derailments likely did not occur at the railroad's desired RNT. This finding suggests that RNT may decrease over time or may not be properly set when rail was installed or adjusted, underscoring the need for improved continuous welded rail (CWR) stress management practices or monitoring methods.
