Search Results (4,212)

Accidents and vehicle damage caused by irregular road surfaces, such as potholes and cracks, remain a significant challenge in autonomous driving, particularly in terms of safety and trajectory reliability. Existing approaches often treat perception and control as separate processes, limiting their ability to respond effectively to road-surface anomalies in real time. In the proposed work, a unified framework for road-surface anomaly-aware control that integrates 3D point cloud perception with a Linear Parameter-Varying Model Predictive Controller (LPV-MPC) is presented. The proposed approach utilizes onboard sensors to capture detailed geometric information of the road surface and detect anomalies relevant to vehicle motion. The detected anomalies are represented in a control-oriented form and incorporated into the LPV-MPC framework, enabling adaptive trajectory planning and speed regulation. This integration allows the controller to proactively adjust vehicle behavior in response to surface irregularities, improving both safety and tracking performance. Experimental results demonstrate that the proposed method enhances robustness against road disturbances and improves trajectory tracking compared to conventional control approaches without anomaly awareness. These results highlight the effectiveness of tightly coupling perception and control for reliable autonomous driving in real-world conditions. Full article

This article presents an analysis of the consequences of a road accident caused by a failure to notice an oncoming vehicle, caused by the left headlamp malfunction. Research was conducted using computer simulation enabling the reproduction of vehicle dynamics under night-time conditions, heavy snowfall and reduced pavement adhesion (α_p = 0.20; α_s = 0.15). An overtaking manoeuvre was performed in three speed scenarios of the overtaking vehicle: 60, 70 and 80 km/h. In the first phase of the collision (the overtaking vehicle–the oncoming vehicle), a consistent increase in deformation depth was observed with increasing speed, from 342 mm and 469 mm (60 km/h) to 400 mm and 518 mm (80 km/h). The corresponding equivalent energy speed (EES) reached maximum values of 57.4 km/h and 70.5 km/h, respectively. Contact was strongly inelastic in nature (the coefficient of restitution 0.06–0.07), and the transferred impulse initiated intensive rotational motion. The second phase of the collision involved secondary contact between the overtaken vehicle and the overtaking vehicle. Collision severity was directly dependent on residual energy after the first impact. In the 80 km/h scenario, the deformation depth in this phase reached 146 mm, with EES of approximately 10–11 km/h. The analysis demonstrated that the energy not dissipated during the first stage determined the course of the subsequent contact and resulted in a complete loss of directional stability of all vehicles, ultimately leading to a departure from the roadway. Full article

Pedestrian safety at urban intersections requires risk-aware mechanisms that extend beyond binary collision detection toward comparative prioritization among multiple agents. This study introduces the Intelligent Pedestrian Model (IPM), a reference-normalized scalar framework that represents pedestrian risk as a function of trajectory, contextual, infrastructural, and behavioral factors, decomposed into Exposure and Severity components. Building on IPM, the Safety-Prioritized Trajectory Model (SPTM) operationalizes the Exposure component using an observation-only, leakage-free kinematic proxy embedded into a cost-aware negative log-likelihood objective. Evaluation on the ETH/UCY benchmark under a strictly inductive protocol shows that moderate prioritization (β ≈ 1.0) improves best-of-K multimodal performance (ALL FDE@K: 0.979 → 0.970 m) while maintaining mean displacement accuracy within seed-level variability. The results indicate that Exposure-based weighting does not act as a global accuracy enhancer but redistributes predictive capacity toward safety-relevant motion regimes. Validation currently covers two ETH/UCY folds under a controlled inductive protocol, while broader cross-fold evaluation remains for future work. Full article

This study proposes a method for evaluating accessibility to neighborhood parks within pedestrian sheds in environments where pedestrian network data are limited and aims to analyze the effects of residential development period and housing type on park accessibility. The study area is Daegu, South Korea. In residentially dense areas, residential activity blocks were delineated using roads with four or more lanes in consideration of pedestrian safety. This approach was intended to establish residential activity areas that account for pedestrian discontinuities. Residential activity areas are classified into five categories of park accessibility, based on whether a neighborhood park lies within walking distance, the number of parks available, and their proportional relationship to the total block area. In addition, periods of residential development are defined according to the year of building approval, and their associations with park accessibility are analyzed in relation to housing type. The analysis identified 464 residential activity blocks within the study area, of which 253 contained parks within pedestrian sheds. The actual distribution of parks within the blocks differed from the results of the conventional buffer-based accessibility analysis conducted for parks within pedestrian sheds. For example, although some blocks included parks within the statutory maximum walking distance of 1 km under the conventional buffer criterion, residents were in practice required to cross roads with four or more lanes to access the parks, indicating that the parks were not effectively located within the residential activity area. In terms of the relationship with the period of residential development, areas densely occupied by residential buildings established before 1980 exhibited relatively low park accessibility, whereas those established since 1990 demonstrated relatively favorable park accessibility. These findings suggest that spatial disparities in park accessibility are structurally shaped by the timing of urban development and patterns of residential formation, rather than by population density alone. This study presents an approach to evaluating accessibility that is applicable even in the absence of pedestrian network data and provides policy implications by identifying priority areas for neighborhood park provision to improve park equity in older residential areas. Full article

The paper analyses the economic impact of the reduction in road traffic accidents in Slovakia between 2000 and 2024 and quantifies both direct and indirect costs of road crashes. Over this period, annual crashes declined from more than 50,000 to approximately 11,500 and fatalities from over 600 to 262, demonstrating the effectiveness of national road safety strategies. The methodology is based on the national road accident database, complemented by macroeconomic and demographic indicators, and follows European recommendations for the valuation of external costs of transport. The study applies the value of a statistical life, the value of a statistical life year, the relative severity index and the critical accident rate, with particular emphasis on comparing the value of a statistical life and the relative severity index. The total VSL-based economic costs of road traffic crashes in 2024 are estimated at approximately €1.25 billion, underscoring the scale of the socioeconomic burden. Building on the forecasted values for 2025, the paper further tests and compares these methodologies on a specific road section, illustrating their practical implications for project appraisal and safety management. The results confirm that VSL-based estimates systematically exceed RSI-based estimates by 21–45% per year, reflecting the broader societal costs captured by the VSL concept. The study shows that investments in safety measures are economically worthwhile and reduce the burden on public finances, while also highlighting the need to harmonize methodologies and improve data quality. Full article

Road intersection crashes remain a major contributor to injuries due to complex conflict patterns and multimodal interactions. Among the factors influencing intersection safety, inadequate intersection sight distance (ISD) attributed to roadside sight obstructions can limit drivers’ ability to respond to conflicting movements, potentially affecting crash injury outcomes. Despite its importance, visual obstruction has rarely been examined as a distinct context in traffic crash injury severity modeling. This study investigates crash injury severity at visually obstructed intersections using an ordinal probit modeling framework applied to 951 intersection crashes documented with sight obstruction as a contributing factor in Wyoming over the period 2014 through 2023. Crash data were analyzed to identify the effects of driver behavior, vehicle characteristics, roadway geometry, environmental conditions, and traffic control on ordered injury severity outcomes ranging from property damage only (PDO) to fatal and serious injury. Nonparametric bootstrap resampling with 1000 iterations was employed to assess parameter stability and construct empirical confidence intervals. Average marginal effects were estimated to quantify the change in probability of each injury severity level associated with key predictors. The results indicate that alcohol involvement produces the largest severity shift, reducing the probability of PDO outcomes by 51.2 percentage points while increasing the probability of fatal and serious injury by 34.2 percentage points. Hillcrest grade locations increase fatal and serious injury risk by 14.4 percentage points, while adverse road surface conditions, including snowy, icy, and wet pavements, consistently reduce fatal and serious injury probability by 12.5 to 15.1 percentage points, reflecting behavioral adaptation to visually salient hazard cues. Bootstrap validation confirms strong parameter stability across all estimates, with 94% of parameters showing bootstrap standard errors within 25% of their asymptotic counterparts. By formally establishing visually obstructed intersections as a dedicated severity modeling context and integrating systematic bootstrap validation, this study contributes both substantive and methodological insights to support evidence-based prioritization of intersection safety improvements. Full article

Autonomous driving technology is widely recognized as a key solution for enhancing future road safety by preventing traffic accidents caused by human error. However, the widespread adoption of autonomous vehicles (AVs) has not yet been achieved, and traffic accidents involving autonomous vehicles in mixed traffic conditions continue to be reported. This study analyzed conflict events using real-world autonomous driving data and identified AV conflict hotspots. A two-dimensional Time to Collision was employed as a surrogate safety indicator to comprehensively capture various types of conflicts in urban interrupted traffic flow. Analysis of approximately 1000 h of driving data revealed 958,011 conflict events, which were distributed along major AV trajectories. The Network Kernel Density Estimation was applied to identify AV conflict hotspots based on conflict events. The optimal hotspot identification model was determined by evaluating various parameter combinations using the Predictive Accuracy Index validated against real-world accident data. Several hotspots were identified on arterial roads with signalized intersections, nearby bus stops, and frequent access points to roadside facilities such as restaurants, stores, gas stations, and residential complexes. Differences in hotspot patterns by conflict type reveal distinct risk characteristics across road sections, emphasizing the necessity of customized safety countermeasures for each conflict type. The findings of this study are expected to accelerate the deployment and wider adoption of autonomous driving technology, promoting the sustainable operation of AVs. Full article

Real-time crash risk assessment is a key component of proactive road safety management, enabling the identification of hazardous conditions within short temporal intervals before crashes occur. Traditional crash-based models are unsuitable for such applications due to the rarity, reporting delay, and stochastic nature of crash data. Traffic conflicts, capturing near-miss interactions between road users, provide a practical alternative for real-time safety analysis. Over the past decade, numerous modelling approaches have been developed to translate conflict information into crash risk estimates; however, the literature remains fragmented and lacks a unified analytical synthesis. This review presents a state-of-the-art, model-centric analysis of conflict-based approaches, classifying them into five paradigms: statistical/regression-based, Bayesian, extreme value theory (EVT), machine learning (ML), and hybrid models. Beyond classification, the study conducts a structured cross-paradigm comparison across key dimensions, including conflict representation, data characteristics, temporal modelling, uncertainty treatment, validation strategies, computational complexity, and operational readiness. The paradigms are further interpreted through the complementary lenses of conflict frequency and severity. The review identifies key research gaps, including fragmented conflict definitions, challenges in modelling rare and extreme events, incomplete treatment of uncertainty and spatiotemporal dynamics, and limitations in validation, transferability, and deployment. Emerging research directions include standardized and adaptive conflict indicators, EVT–machine learning integration, integrated uncertainty-aware frameworks, advanced spatiotemporal modelling, transferable models, and scalable real-time implementation. By combining structured evidence mapping and cross-paradigm synthesis, this study supports model selection, development, and deployment for dynamic crash risk assessment. Full article

As a complex structural form capable of simultaneously bearing upper-deck highway traffic, lower-deck highway traffic, and rail transit, the curved chord stiffened double-deck continuous steel truss bridge is distinct from traditional single-deck bridges. The spatial superposition of multiple traffic types within this structure may result in multiple components approaching their critical states concurrently. Despite prior research efforts on this structural type, the failure evolution process from local yielding to global collapse under mixed traffic loads remains ambiguous. This study addresses these questions through systematic numerical investigation of a nine-span bridge with a 300 m main span. A two-stage analytical approach is employed: a Midas/Civil analysis first identifies critically stressed regions, then ABAQUS multi-scale modeling enables refined analysis of critical components while maintaining computational efficiency. Twenty-nine combined traffic loading cases encompassing dual- and triple-category configurations are systematically analyzed. The results show that the ultimate load-carrying capacity coefficients range from approximately 7 to 18, with a minimum of 7.137, and the dual-level highway combinations exert greater influence than road–rail combinations. More importantly, three failure path convergence characteristics were discovered. First, the initial failure position under each working condition tends to be consistent, initiating at the lower chord near the top of the mid-span pier, which confirms that inherent structural defects exist at this location. Second, the gusset plate at the top of pier W6 appears as the second failure location in 48% of cases and ranks within the first four locations across all cases. Third, path similarity progressively increases with traffic diversity. Additionally, Q370qE steel exhibits 5–22% stress exceedance with variable critical locations depending on traffic conditions. Based on these convergence characteristics, a safety monitoring scheme is proposed: monitoring points need to be arranged symmetrically on both sides of the bridge on the top chords, bottom chords, web members, and wedge plates near the tops of the piers. Full article

High-speed train aerodynamics have mainly been improved by passive design methods, such as streamlined noses, local fairings, and surface smoothing. These methods have achieved clear benefits, but several important aerodynamic problems remain difficult to solve by geometry optimization alone. Open-air drag is still affected by tail flow separation, base-pressure recovery, and disturbances around bogies and the underbody; crosswind safety is influenced by unsteady leeward-side separation and wake asymmetry; slipstream behavior depends on wake vortices, boundary-layer development, and complex near-ground underbody flow; and tunnel-related pressure transients arise from compression-wave generation, propagation, and reflection. These coupled effects mean that one fixed train shape cannot perform optimally in all operating conditions. For this reason, this review proposes that active flow control (AFC) should not be regarded only as a drag-reduction or stability-improvement technique for high-speed trains. Instead, it should be understood as a mission-adaptive aerodynamic control framework, in which different control actions are used for different operating scenarios. This paper first clarifies that passive optimization is increasingly subject to diminishing returns under multi-objective and engineering constraints. It then reviews AFC studies on drag reduction, base-pressure recovery, wake and slipstream control, underbody flow conditioning, crosswind mitigation, and tunnel pressure-wave suppression. Related AFC studies on bluff bodies, road vehicles, and other separated flows are included only when their physical relevance to trains is clear. The review further distinguishes gross aerodynamic improvement from net energy gain and identifies actuator power, durability, maintainability, acoustic impact, validation level, and full-scale transferability as decisive feasibility factors. Current research is still dominated by open-loop numerical studies with simplified actuation. Future work should therefore move toward multi-objective, closed-loop, energy-aware, sensor–actuator-integrated, and explainable machine-learning-assisted AFC. The main message is that the next step in train aerodynamics is not simply a better fixed shape, but a control-enabled train that can selectively redistribute aerodynamic authority across its mission profile. Full article

Road-railway combined steel truss bridges are increasingly adopted in urban infrastructure due to their structural efficiency and versatility. This study proposes a three-level multi-scale finite element framework to investigate the safety reserve and progressive failure mechanism of a four-span (80 + 120 + 120 + 80 m) continuous steel truss bridge carrying both highway and railway traffic. At the macro level, a beam element model was established in Midas/Civil to determine the most unfavorable loading configurations, yielding a minimum buckling load factor of 31.0 under dead load and a maximum vertical displacement of 175 mm at mid-span under combined traffic loading. At the meso level, a mixed beam–shell element model incorporating geometric and material nonlinearities was developed in ABAQUS, revealing an ultimate load factor of 6.61 with distinct progressive failure characteristics: initial yielding occurs near the intermediate pier supports, where deformation is constrained, while final instability develops at Joint A17 due to its lower relative stiffness. At the micro level, a refined solid-shell submodel of the critical joint, driven by displacement boundary conditions extracted from the global model, was constructed to capture the local failure mechanism. The results demonstrate that the governing failure mode is shear buckling of the gusset plate, induced by a vertical displacement differential of approximately 30 mm between the web members on opposite sides of the joint arising from differential stiffness. The stress analysis further reveals pronounced stress concentrations in the splice plates adjacent to the more flexible web member, confirming the asymmetric load distribution mechanism. Based on these findings, strengthening measures including increased gusset plate thickness at pier-top joints, optimized chord sections, and the use of higher-strength steel in critical regions are recommended. Full article

Forward road information is essential to improve the safety of next-generation advanced driver assistance systems/automated driving systems. In this study, we developed a noncontact friction estimation system that integrates multivariate information from multiple sensors, including three-dimensional light detection and ranging, millimeter-wave radar, and infrared thermometers. We used the continuous peak µ measured by a proprietary friction measurement trailer as the ground truth which has demonstrated extremely high measurement accuracy (R² = 0.9987) on test road sections and similar surfaces. Through multivariate regression analysis using real road data, including snowy surfaces, the system achieved a high explanatory power with an adjusted coefficient of determination of 0.75. In addition, a time-series analysis of squared errors revealed that sensor fusion based on three physical factors, road roughness, moisture content, and thermal response resulted in the most accurate and robust estimation model. Full article

This paper presents a deployment-oriented longitudinal platoon-control architecture for connected and autonomous vehicles operating under repeated leader hard-braking, cut-ins, and spatially varying road friction. The proposed stack combines four elements: (i) a lightweight scalar Kalman filter (KF) that smooths a friction-related signal and feeds friction-dependent constraint tightening; (ii) a model predictive control (MPC) backbone whose weights and horizon are selected offline using multi-objective GA/NSGA-II tuning; (iii) a bounded proximal policy optimization (PPO) residual policy, trained with the aid of a learned surrogate model, that refines the MPC command during transient events; and (iv) a command-level safety projection that enforces instantaneous actuation and clearance constraints at the fast control tick. The contribution is therefore not a new MPC formulation or a new reinforcement-learning algorithm in isolation, but an integrated and experimentally characterized control stack that keeps the safety-critical structure explicit while using learning to improve transient behavior. The method is evaluated in a CARLA digital twin of a six-vehicle platoon over a 5 km mixed urban–highway route and is further assessed in hardware-in-the-loop (HIL) on an automotive ECU using a multi-rate ROS 2/AUTOSAR implementation (50 Hz estimation/safety loop, 10 Hz MPC/RL refresh). Across 10 held-out disturbance seeds, the full stack improves spacing regulation, maintains non-amplifying disturbance propagation according to the reported string-stability indices, and reduces a route-normalized positive tractive-energy-at-the-wheels proxy by about 12% relative to Manual MPC and by up to 18% relative to a PID-CACC reference. Because the PID-CACC baseline does not enforce hard constraints and can collide under the tested disturbance suite, the main performance comparison is among collision-free controllers. The friction signal used in CARLA is derived from simulator road-surface annotations before filtering, so the present study should be interpreted as a friction-aware control and integration study rather than a validated onboard friction-estimation result. Likewise, the reported energy metric is an effort proxy and is not a calibrated fuel or battery consumption model. Full article

Traffic calming devices (TCMs) are widely implemented to reduce urban vehicle speeds; however, their influence on drivers’ direct control inputs remains underexplored. This study examines how drivers redistribute braking, throttle and steering inputs in the presence of speed cushions, extending driver–infrastructure interaction assessment beyond speed-only metrics. A driving simulator reproduced an urban corridor in Messina (Italy). Twenty-five drivers completed three scenarios: baseline without traffic calming (No TCM), daytime with speed cushions and nighttime with speed cushions. Cushion colour (red/blue) and width (1.5, 1.8, 2.1 m) were varied. Vehicle telemetry was analyzed using repeated-measures ANOVA with corrected post hoc tests and partial η² as effect size. The analysis was complemented by paired within-subject comparisons, bootstrap confidence intervals and additional transient indicators computed on travelled-distance windows to support transparent effect interpretation without replacing the RM-ANOVA framework. Compared with No TCM, speed cushions increased mean braking (+224% Day, +372% Night) and reduced the mean normalized throttle input by approximately 55%, with stronger braking at night. Width primarily influenced throttle release and steering corrections, whereas colour modulated braking under reduced visibility. Despite limitations related to sample size and simulation, the findings provide actionable evidence for contexts where cushion width and colour are not standardized. Full article

Deep learning models are widely used in autonomous vehicle systems for perception, localization, and decision-making. However, their lack of transparency poses significant challenges in safety-critical environments. This systematic review presents a unified mathematical framework for explainable deep learning which integrates multimodal inputs, graph-theoretic road geometry, uncertainty modeling, and intrinsically interpretable representations. Road-structured priors that include lane topology and spatial constraints are incorporated into learning and optimization processes for ensuring model predictions and explanations to remain physically and semantically grounded. The review synthesizes methods across saliency-based, concept-based, causal, and intrinsic explainability, and extends them to vision-language models. This enables language-grounded, human-interpretable reasoning in autonomous vehicle systems. While vision-language models offer a new paradigm for semantic explainability, their limitations such as hallucinations, misgrounding, and reduced reliability under distribution shifts are also critically examined. Along with the role of road priors in improving alignment and robustness, another key contribution of this work is its quantitative evaluation metrics for road-aware explainability. These evaluation metrics link the explanations to spatial consistency, uncertainty alignment, and graph-structured reasoning. The overall framework connects latent representations, predictions, and explanations within a single formulation, enabling systematic comparison and analysis across models. Based on a PRISMA-guided review of 164 studies, this research identifies gaps in real-world reliability, temporal reasoning, and standardized evaluation, and outlines future directions including human-in-the-loop systems, regulatory readiness, and language-based auditing. Overall, this study advances a mathematically grounded and road-aware perspective on explainable vehicle AI which significantly bridges the gap between high-performance models and transparent, trustworthy autonomous systems. Full article