Search Results (307)

Background subtraction (BS) is a fundamental technique in intelligent transport vision systems, widely used to detect and track moving objects, such as vehicles, pedestrians and obstacles, in driving environments. It plays a crucial role in advanced driver-assistance systems (ADAS) and autonomous driving by enabling scene understanding and real-time motion analysis. However, BS processing must be optimized when targeting real-time processing on resource-constrained embedded systems, which present significant challenges due to limited computational power, memory constraints, and strict real-time requirements. Among the most commonly used BS techniques, the Codebook model and Gaussian Mixture Models (GMM) are known for their higher accuracy and light-model compared to many deep learning-based BS. In this work, we propose a fully heterogeneous CPU and GPU parallel implementation of both Codebook and GMM algorithms with an auto-load balancing over the processing units. This approach has been evaluated on the low-cost Jetson Orin Nano platform from NVIDIA, enabling efficient workload balancing across heterogeneous hardware resources. The suggested solution yields significant performance improvements over the state-of-the-art, achieving 59 frames per second (FPS) for GMM and 66 FPS for the Codebook method on full-HD (1080p) video streams. The results confirm the effectiveness of the proposed method in accelerating BS and demonstrate its suitability for real-time deployment in resource-constrained embedded environments. Full article

The increasing demand for intelligent transportation systems (ITS) and advanced driver assistance system (ADAS) significantly demands a real-time and robust perception to recognize road-side obstacles in varying different weather settings. This paper presents DriveEdgeAI, a lightweight YOLO11 based embedded deep learning framework for efficient road anomaly detection with the emphasis on potholes, speed bumps and relevant traffic sign detection. We have prepared a custom dataset consisting of 17,061 annotated images to train and test the model under different lighting conditions, weather conditions, and roads configurations. The proposed system also managed to demonstrate good convergence and generalization with a precision@50 of 95.8%, recall@50 of 89.7%, mAP@50 of 95.4%, surpassing previous YOLO versions. The stability and robustness of the model at different thresholds were also substantiated by Precision-Recall and F1-Confidence analyses. DriveEdgeAI was also deployed on a number of edge devices, such as Jetson Nano, Raspberry Pi 5, Intel Movidius VPU and Hailo-8L NPU respectively reaching 9.5 FPS/W and 28.5 FPS for the Raspberry Pi 5 + Hailo-8L version. From these results, one can conclude that DriveEdgeAI is an energy-efficient and scalable solution for real-world ADAS applications. Full article

Introduction: Pedestrians lying on the road—collapsed through medical emergency, intoxication, or displacement following a prior collision—represent a disproportionately lethal and underaddressed category in road traffic safety. Forensic database analyses derived from Japan’s national police records document a fatality rate of 33.0% for collisions involving pedestrians lying on the road, more than double the rate for upright pedestrian collisions. Standard Advanced Driver-Assistance Systems (ADAS) yield a True Positive Rate (TPR) of only 21.4% for detecting pedestrians lying on the road under night conditions—a classification gap of 73.3 percentage points. Methods: In simulation trials, we evaluated the Advanced Falling Object Detection System (AFODS—where “falling object” denotes the low-profile human form at road level, distinguishing the prone pedestrian from the upright postures addressed by conventional ADAS) on a composite dataset of 3200 annotated fall events and 12,000 negative samples (training/validation), with 320 independent controlled simulation trials used for performance evaluation, spanning real-world, forensic-reconstruction, and Total Human Body Model for Safety (THUMS)-validated synthetic scenarios. No physical prototype has been evaluated; all performance data are derived from simulation, and 37.5% of positive samples are synthetically generated. These simulation conditions represent a first feasibility demonstration pending real-world hardware validation. This paper introduces three original contributions absent from prior work: a three-stage quantitative injury-risk model, a formal ISO 26262 Hazard Analysis and Risk Assessment (HARA), and a medicolegal SHAP interpretability framework. The injury-risk model translated detection latency via impact velocity to Head Injury Criterion (HIC) and estimated fatal injury probability (AIS ≥ 5); these model outputs should be interpreted as exploratory estimates pending ATD validation. Reporting follows principles consistent with the TRIPOD statement. Results: Under clear daytime conditions, AFODS demonstrated a TPR of 98.2% (95% CI: 97.4–98.8%) in simulation, decreasing to 95.6% under night dry-road conditions and 89.4% under night rain. The system achieved an AUC of 0.981 and a mean end-to-end latency of 46.5 ms, representing a 76.8 percentage-point improvement in simulation over the monocular RGB baseline (p < 0.001). The injury-risk model projects a reduction in estimated fatal head injury probability from 66.2% (Monte Carlo mean) (no detection, 50 km/h full-speed impact) to 0.7% under AFODS worst-case night/rain conditions, and to ≈0% under clear daytime simulation conditions. Conclusions: A 73.3 percentage-point classification gap places pedestrians lying on the road outside the effective detection envelope of current ADAS, compounded by the systematic exclusion of non-upright postures from regulatory test protocols and benchmark datasets. AFODS supports proof-of-concept feasibility under simulation conditions. Three translational steps are required: prototype validation on real-world hardware using instrumented Anthropomorphic Test Devices (ATDs); prone-posture biomechanical injury modelling using HIC and BrIC criteria; and regulatory extension of pedestrian AEB test standards to non-upright scenarios. Full article

Cameras and IMUs on heavy mining trucks supply the visual signal that Advanced Driver Assistance Systems (ADASs) use in open-pit operations. Haul roads in a surface mine are unstructured and unmarked, so a perception model must be both accurate and fast. We address this with a video-based multitask pipeline for a mining Driver Support System (DSS): a single BiSeNetV1 network produces drivable-area segmentation and steering-direction classification in one forward pass. Training used only 100 frames sampled non-sequentially from in-cab recordings of a real open-pit mine; evaluation used two full onboard sequences. To exploit temporal redundancy without annotating video, we propose an Adaptive Clockwork (A-CW) inference scheme: the spatial path runs on every frame, while the context path is refreshed only on keyframes whose cadence is set by the classification output, the same signal shown to the driver as a steering hint. This classification-guided policy increases context updates on curved segments, where the scene changes more rapidly, and reduces them on straight sections, where semantic redundancy is higher. The selected A-CW configuration was evaluated on full temporal test sequences, including one route kept entirely outside the training source. On this unseen route, A-CW achieved 94.70% road-class IoU and 73.68% Top-1 Accuracy. GPU-only throughput increased from about 55 FPS with frame-by-frame inference to 168.01 FPS, and display-excluded end-to-end processing in the simulated ADAS pipeline remained at approximately 37.5 FPS. Full article

Advanced Driver Assistance Systems (ADAS) have significant potential to improve road safety. However, drivers’ perceptions and acceptance of these systems in Thailand have not been explored. This study investigated Thai drivers’ perceptions towards ADAS and examined factors associated with trust and intention to use. A cross-sectional survey was conducted with 849 licenced drivers. The questionnaire measured perceived usefulness, perceived ease of use, trust, barriers and concerns, expectations and preferences, and intention to use ADAS. Data were analyzed using Mann–Whitney U tests, Spearman’s rank correlations, and multiple linear regression. Results indicated that Thai drivers reported positive perceptions of ADAS regarding perceived usefulness, expectations, preferences, and intention to use. Trust was most strongly associated with constructs such as perceived usefulness, perceived ease of use, and intention to use. Multiple regression identified perceived usefulness, trust, and expectations and preferences as significant positive predictors of intention to use ADAS, whereas barriers and concerns were negatively associated with intention to use. Perceived ease of use was not a significant predictor. These findings highlight the importance of perceived usefulness, trust, and user expectations in shaping intention to use ADAS and support the need for new policies regarding driver education and awareness initiatives in Thailand. Full article

This paper presents a Threat Analysis and Risk Assessment (TARA) of the takeover request (TOR) component in Advanced Driver Assistance Systems (ADAS) for SAE Level 2–3 automation. A TOR prompts the human driver to retake control when the system approaches its Operational Design Domain limits or when risk increases; late, false, or muted requests directly impact safety. The study models the TOR pipeline (perception, driver monitoring, decision logic, in-vehicle networks, and Human–Machine Interface) as assets and data flows, applies STRIDE-based threat identification using Microsoft Threat Modeling Tool and Ansys Medini Analyze, and rates risks under ISO/SAE 21434 with traceability to ISO 26262, ISO 21448, and UNECE R155/R157. The assessment produces 165 threat rows, with an initial risk distribution of 1 Critical, 113 High, 34 Medium, and 17 Low. Results show that tampering, denial of service, and spoofing dominate the TOR threat landscape, with the central processing unit, sensor-to-CPU links, and HMI channels as primary trust anchors. After applying mitigation measures including secure boot, message authentication, intrusion detection, redundancy checks, and encrypted communication, the residual post-mitigation security levels were reduced to 0 Critical, 0 High, 13 Medium, 101 Low, and 51 Negligible. Unlike other ADAS TARA studies, this TOR-focused analysis shows that cybersecurity risk is shaped by the interaction between cyber compromise, driver-readiness estimation, HMI delivery, fallback execution, and the limited handover time budget. The results support a defence-in-depth mitigation strategy for secure TOR operation in SAE Level 2–3 vehicles. 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

Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) are becoming convenient modes of transportation; however, their safety remains a critical concern as crashes continue to occur. To reveal crash trends and temporal variations, this study develops time-series forecasting models to predict future crash counts of such vehicles. The crash dataset released by the National Highway Traffic Safety Administration (NHTSA) has been used here. Two univariate forecasting models—the Seasonal Autoregressive Integrated Moving Average (SARIMA) and the Facebook Prophet model—have been used here for different datasets. The models were trained on 30 months of data (July 2021 to December 2023) and validated on 6 months of data (January–June 2024). Validation metrics include Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Theil’s U1 statistic. Results showed that Facebook Prophet significantly outperformed SARIMA for both datasets, achieving an RMSE of 2.71 and an MAPE of 6.9% for ADAS, and an RMSE of 2.24 and an MAPE of 8.85% for ADS. For both systems, the model revealed empirically observed cyclical patterns and consistent rising trends. ADAS crashes exhibit a bimodal temporal pattern, with recurring peaks in January and May–June, alongside notable troughs in February–March and August–September. ADS displays a trimodal pattern, with recurring peaks in April–May, August and October, alongside notable troughs in December and the early winter months. These patterns represent empirically identified temporal regularities rather than causally attributed seasonality. From the future forecasts for July to December 2024, the model showed that ADAS crashes are expected to range between 40 and 80 per month, while ADS crashes are projected to remain between 20 and 40 per month. These findings underscore the need for proactive safety measures and enhanced regulatory oversight during identified high-risk periods to mitigate the growing trend in AV crashes. Full article

Recent advancements in autonomous driving technology are transforming the automotive industry, with advanced driver assistance systems (ADAS) recognized as a crucial transitional technology toward fully autonomous driving. ADAS enhances driver safety and comfort through features such as emergency braking, lane-keeping, and adaptive cruise control, ultimately aiding in traffic accident prevention and reduction in driver fatigue. However, commercial ADAS implementations show substantial variability due to differences in sensor configurations, operational design domain (ODD) definitions, and operational criteria across automakers. To address this gap, this study provides a structured comparative review of commercialized ADAS technologies across 11 major Western and Asian automakers. By encompassing both Western and Asian OEMs, this study compares manufacturer-declared sensor configurations, ODD settings, activation conditions, driver-monitoring requirements, takeover and fallback logic, and update-related characteristics. The review identifies implementation-level differences that affect comparability, user understanding, validation requirements, and standardization needs. Rather than ranking OEM systems by safety performance, this study clarifies the trade-offs among redundancy-oriented, camera-centric, HD-map-dependent, geofenced, and OTA-driven ADAS strategies. The findings support future work on standardized ODD communication, user-centered HMI design, independent validation, and update-aware review frameworks for commercial ADAS. Full article

Despite extensive research on traffic safety, the complex, non-linear spatial discrepancy between risky driving and actual accidents remains a significant challenge to quantify within diverse urban contexts. This study investigates the non-linear relationship between grid-level risky driving patterns and traffic accident occurrence in Daejeon, Korea, examining how these associations vary across different urban contexts. Using data collected from July 2023 to June 2024, the analysis incorporates GPS-based risky driving indicators, including rapid acceleration, deceleration, and sudden maneuvers from general passenger vehicles, thereby overcoming the limitations of previous studies reliant on commercial vehicle data. By adopting an H3-based spatial grid system, the study classifies areas into four quadrants based on median values of risky behaviors and accident counts, further categorizing them into “Matched” and “Mismatched” types to identify spatial discrepancies. Furthermore, the Explainable Artificial Intelligence (XAI) technique is employed to integrate regional variables—including population density, land use, and transport infrastructure—to uncover the key drivers of accident risks. Providing a significant methodological improvement over traditional linear models, the findings demonstrate that identical driving behaviors can yield different safety outcomes depending on local environmental interactions. Specifically, while driver behavioral factors directly explain accident frequency in matched regions, accident risks in mismatched regions are more significantly shaped by spatial environmental factors, such as green spaces and commercial land use, which override direct behavioral impacts. This study provides a robust framework for developing data-driven, region-specific traffic intervention strategies, including context-aware advanced driver assistance systems (ADAS) and spatially tailored traffic calming, to enhance urban safety. Full article

In recent years we have seen large language models (LLMs) demonstrating robust reasoning capabilities comparable to human performance. This makes them increasingly appealing for driver assistance, where adaptation to dynamic human context is essential. Yet, research in this area remains fragmented, often focusing on isolated applications, lacking utilization of LLM’s full potential to deliver integrated, context-specific support and action. This survey synthesizes recent advancements in LLM-driven occupant monitoring systems, focusing on their capabilities for interpreting driver states and acting appropriately, enabling a new generation of intelligent driver assistance. We critically examine pioneering frameworks, benchmarks, and foundational datasets that employ techniques like reasoning chains, multimodality, and human-in-the-loop feedback to create personalized and safe driving experiences. We lay out the current trends, limitations, and emerging patterns, in addition to a novel human-centered evaluation of the field, providing researchers with a roadmap towards transparent and trustworthy in-cabin systems that bridge safety with driver experience. Full article

Advanced Driver Assistance Systems (ADAS) are increasingly shaping urban mobility and road safety, yet their benefits depend not only on technical performance, but also on driver acceptance. This study examines how Hungarian drivers perceive and evaluate key ADAS functions, Adaptive Cruise Control (ACC), Lane Keeping/Centering Assist (LKA/LCA), and Forward Cross Traffic Alert (FCTA), in urban driving contexts. The research is based on qualitative focus group discussions conducted in Győr, Hungary, involving drivers aged 20–50 from different age cohorts. Data were analyzed using thematic analysis. The findings show that the acceptance of ADAS is strongly context-dependent and function specific. ACC was perceived primarily as a comfort-enhancing tool, especially on longer or more monotonous routes, while LCA was often regarded intrusive and less reliable in urban conditions due to poor road markings, potholes, and frequent stop-and-go situations. On the contrary, blind spot and cross-traffic-related functions were evaluated more positively due to their direct safety benefits. Trust, perceived risk, and control emerged as key dimensions of acceptance, with many participants emphasising the importance of warning-based support rather than a strong autonomous intervention. In general, the study concludes that urban acceptance of ADAS is shaped by the interaction of infrastructure conditions, perceived usefulness, and driver trust, highlighting the need for more transparent, context sensitive, and user-centered system design in support of safer urban mobility. Full article

Vulnerable road users in informal urban environments confront a distinct set of hazards that standard computer vision datasets are ill-equipped to represent: artisanal speed bumps constructed without regulatory compliance, deteriorated road markings, and the mototaxi—a three-wheeled motorized vehicle that constitutes the primary informal transport mode in intermediate Andean cities yet is absent from all major international repositories. This paper presents QHAWAY—from Quechua qhaway, a transitive verb meaning “to look; to observe”—an Advanced Driver Assistance System (ADAS) predicated on instance segmentation, monocular distance estimation via the pinhole camera model, and Time-to-Collision (TTC) computation, developed for the road environment of Ayacucho, Peru (2761 m a.s.l.), a city recognised by UNESCO as a Creative City of Crafts and Folk Art since 2019. A hybrid dataset comprising 25,602 images with 127,525 annotated instances across 12 classes was assembled by combining an original local collection of 4598 images (10,701 instances) captured through four complementary acquisition methods across the five urban districts of the Huamanga province with three established international datasets (BDD100K, BSTLD, RLMD; 21,004 images, 116,824 instances). A three-phase progressive training strategy with monotonically increasing resolution (640, 800, and 1024 pixels) was evaluated as an ablation study. A multi-architecture comparison spanning YOLOv8L-seg and the YOLO26 family (nano, small, large) identified YOLO26L-seg as the best-performing model, attaining mAP50 Box of 0.829 and mAP50 Mask of 0.788 at epoch 179. The integration of ByteTrack multi-object tracking with the pinhole equation $D=(H_{\mathrm{real}}\times f)/h_{\mathrm{px}}$ delineates operational risk zones aligned with the NHTSA forward collision warning standard (danger: <3 m; caution: 3–7 m; TTC threshold ≤ 2.4 s). The system sustains processing rates of 19.2–25.4 FPS on an NVIDIA RTX 5080 GPU. A systematic field survey established that 96% of the audited speed bumps fail to comply with MTC Directive No. 01-2011-MTC/14, constituting the first quantitative record of informal road infrastructure non-compliance in the Andean region. Validation was conducted under naturalistic driving conditions without staged scenarios. Grad-CAM explainability analysis, encompassing three complementary visualisation algorithms (Grad-CAM, Grad-CAM++, and EigenCAM), confirmed that model attention concentrates consistently on safety-critical objects. Full article

The present paper proposes an adaptive steering weight allocation strategy based on a non-cooperative Stackelberg game and Model Predictive Control (MPC) for dynamic steering authority allocation in human–machine shared control of intelligent vehicles. First, the human–machine steering interaction is modelled as a Stackelberg game, and the steering control problem is formulated as an MPC optimization problem. The optimal control sequences of the driver and the Advanced Driver Assistance System (ADAS) under game equilibrium are then derived through backward induction. Subsequently, driver behaviour is classified as aggressive, moderate, or conservative according to lateral preview error and lateral acceleration, and the driver state is quantified using parametric indicators. Furthermore, by integrating potential field-based driving risk assessment with human–machine conflict intensity, a fuzzy logic-based dynamic weight adjustment mechanism is constructed. Simulation results show that when the steering intentions of the driver and the ADAS are highly consistent, the proposed strategy can effectively reduce driver workload and improve driving safety. In high-risk driving situations, the strategy automatically transfers more steering authority to the ADAS to enhance safety, whereas under low-risk conditions with strong human–machine steering conflict, greater driver authority is preserved to ensure that the vehicle follows the intended path. Hardware-in-the-loop experiments in lane-changing assistance scenarios further verify the effectiveness of the proposed strategy under different driving styles. Quantitative results show that, compared with manual driving, the proposed strategy reduces the maximum lateral overshoot by 98.75%, 85.54%, and 98.58% for aggressive, moderate, and conservative drivers, respectively. In addition, the peak yaw rate and driver control effort are significantly reduced, indicating smoother vehicle dynamic response and lower steering workload. These results demonstrate that the proposed strategy can effectively improve lane-change stability, reduce driver burden, and maintain safe and coordinated human–machine shared control. Full article

Advanced Driver Assistance Systems (ADAS) are rapidly evolving to improve driving safety and comfort, in line with the European Community’s Vision Zero objectives. However, current validation methods mainly focus on steady-state conditions and regulatory compliance, while standardized KPIs capable of capturing vehicle dynamics during transient maneuvers and relating them to human driving behavior are still lacking. This study proposes a KPI-based methodology to evaluate lane centering systems in both steady-state and transient conditions, using a human driver as a reference. New parameters—aggressiveness and smoothness indices—are introduced to quantify the dynamic response. Simulator and constant-speed vehicle tests highlight the differences between ADAS strategies and human behavior, providing insights into comfort and acceptance. Full article