Search Results (118)

Mind wandering (MW) is a significant safety risk in driving, yet research on its scope, underlying mechanisms, and mitigation strategies remains fragmented across disciplines. In this review guided by the PRISMA framework, we analyze findings from 64 empirical studies to address these factors. The presented study quantifies the prevalence of MW in naturalistic and simulated driving environments and shows its impact on driving behaviors. We document its negative effects on braking reaction times and lane-keeping consistency, and we assess recent advancements in objective detection methods, including EEG signatures, eye-tracking metrics, and physiological markers. We also identify key cognitive and contextual risk factors, including high perceived risk, route familiarity, and driver fatigue, which increase MW episodes. Also, we survey emergent countermeasures, such as haptic steering wheel alerts and adaptive cruise control perturbations, designed to sustain driver engagement. Despite these advancements, the MW research shows persistent challenges, including methodological heterogeneity that limits cross-study comparisons, a lack of real-world validation of detection algorithms, and a scarcity of long-term field trials of interventions. Our integrated synthesis, therefore, outlines a research agenda prioritizing harmonized measurement protocols, on-road algorithm deployment, and rigorous evaluation of countermeasures under naturalistic driving conditions. Full article

The safe and reliable operation of autonomous vehicles is contingent on comprehensive testing. However, the operational scenarios are inexhaustible. Corner cases, which critically influence autonomous vehicle safety, occur at an extremely low probability and follow a long-tail distribution. Corner cases can be defined as combinations of driving task and scenario elements. These scenarios are characterized by low probability, high risk, and a tendency to reveal functional limitations inherent to autonomous driving systems, triggering anomalous behavior. This study constructs a novel corner case dataset using naturalistic driving data, specifically tailored for autonomous vehicle testing. A scenario marginality quantification method is designed to analyze multi-source naturalistic driving data, enabling efficient extraction of corner cases. Heterogeneous scenarios are systematically transformed, resulting in a dataset characterized by diverse interaction behaviors and standardized formatting. The results indicate that the scenario marginality of the dataset constructed in this study is 2.78 times that of mainstream naturalistic driving datasets, and the scenarios exhibit considerable diversity. The trajectory and velocity fluctuations, quantified at 0.013 m and 0.021 m/s, respectively, are consistent with the kinematic characteristics of real-world driving scenarios. These results collectively demonstrate the dataset’s high marginality, diversity, and applicability. Full article

Accurate trajectory prediction of surrounding vehicles is a fundamental challenge in autonomous driving, requiring sophisticated modeling of complex vehicle interactions, traffic dynamics, and contextual dependencies. This paper introduces Adaptive Speed-Aware Prediction (ADSAP), a novel trajectory prediction framework that advances the state of the art through innovative mechanisms for adaptive attention modulation and knowledge transfer. At its core, ADSAP employs an adaptive deformable speed-aware pooling mechanism that dynamically adjusts the model’s attention distribution and receptive field based on instantaneous vehicle states and interaction patterns. This adaptive architecture enables fine-grained modeling of diverse traffic scenarios, from sparse highway conditions to dense urban environments. The framework incorporates a sophisticated speed-aware multi-scale feature aggregation module that systematically combines spatial and temporal information across multiple scales, facilitating comprehensive scene understanding and robust trajectory prediction. To bridge the gap between model complexity and computational efficiency, we propose an adversarial knowledge distillation approach that effectively transfers learned representations and decision-making strategies from a high-capacity teacher model to a lightweight student model. This novel distillation mechanism preserves prediction accuracy while significantly reducing computational overhead, making the framework suitable for real-world deployment. Extensive empirical evaluation on the large-scale NGSIM and highD naturalistic driving datasets demonstrates ADSAP’s superior performance. The ADSAP framework achieves an 18.7% reduction in average displacement error and a 22.4% improvement in final displacement error compared to state-of-the-art methods while maintaining consistent performance across varying traffic densities (0.05–0.85 vehicles/meter) and speed ranges (0–35 m/s). Moreover, ADSAP exhibits robust generalization capabilities across different driving scenarios and weather conditions, with the lightweight student model achieving 95% of the teacher model’s accuracy while offering a 3.2× reduction in inference time. Comprehensive experimental results supported by detailed ablation studies and statistical analyses validate ADSAP’s effectiveness in addressing the trajectory prediction challenge. Our framework provides a novel perspective on integrating adaptive attention mechanisms with efficient knowledge transfer, contributing to the development of more reliable and intelligent autonomous driving systems. Significant improvements in prediction accuracy, computational efficiency, and generalization capability demonstrate ADSAP’s potential ability to advance autonomous driving technology. Full article

Background: Multicultural education and second-language acquisition engaged neural networks, supporting executive function, memory, and social cognition in adulthood, represent powerful forms of brain-inspired multisensory learning. The neuroeducational framework integrates neuroscience with pedagogical practice to understand how linguistically and culturally rich environments drive neuroplasticity and cognitive adaptation in adult learners. Objective: This systematic review synthesizes findings from 80 studies examining neuroplasticity and cognitive outcomes in adults undergoing multicultural and second-language acquisition, focusing on underlying neural mechanisms and educational effectiveness. Methods: The analysis included randomized controlled trials and longitudinal studies employing diverse neuroimaging techniques (fMRI, MEG, DTI) to assess structural and functional brain network changes. Interventions varied in terms of immersion intensity (ranging from limited classroom contact to complete environmental immersion), multimodal approaches (integrating visual, auditory, and kinesthetic elements), feedback mechanisms (immediate vs. delayed, social vs. automated), and learning contexts (formal instruction, naturalistic acquisition, and technology-enhanced environments). Outcomes encompassed cognitive domains (executive function, working memory, attention) and socio-emotional processes (empathy, cultural adaptation). Results: Strong evidence demonstrates that multicultural and second-language acquisition induce specific neuroplastic adaptations, including enhanced connectivity between language and executive networks, increased cortical thickness in frontal–temporal regions, and white matter reorganization supporting processing efficiency. These neural changes are correlated with significant improvements in working memory, attentional control, and cognitive flexibility. Immersion intensity, multimodal design features, learning context, and individual differences, including age and sociocultural background, moderate the effectiveness of interventions across adult populations. Conclusions: Adult multicultural and second-language acquisition represents a biologically aligned educational approach that leverages natural neuroplastic mechanisms to enhance cognitive resilience. Findings support the design of interventions that engage integrated neural networks through rich, culturally relevant environments, with significant implications for cognitive health across the adult lifespan and for evidence-based educational practice. Full article

In the context of the global energy transition, thermal management of electric vehicle batteries faces severe challenges due to temperature rise and energy consumption under dynamic operating conditions. Traditional strategies rely on real-time feedback and suffer from response lag and energy efficiency imbalance. In this study, we propose a neural network-based synergistic optimization method for driving conditions prediction and dynamic thermal management, which collects multi-scenario real-vehicle data (358 60-s condition segments) by naturalistic driving data collection method, extracts four typical conditions (congestion, highway, urban, and suburbia) by combining with K-means clustering, and constructs a BP (backpropagation neural network) model (20 neurons in the input layer and 60 neurons in the output layer) to predict the vehicle speed in the next 60 s. Based on the prediction results, the coupled PID control and temperature feedback mechanism dynamically adjusts the coolant flow rate (maximum reduction of 17.6%), which reduces the maximum temperature of the battery by 3.8 °C, the maximum temperature difference by 0.3 °C, and the standard deviation of temperature fluctuation at ambient temperatures of 25~40 °C is 0.2 °C in AMESim simulation and experimental validation. The results show that the strategy significantly improves battery safety and system economy under complex working conditions by prospectively optimizing heat dissipation and energy consumption, providing an efficient solution for intelligent thermal management. Full article

The paper presents an application of a new, simple approach for the naturalistic assessment of road sign quality from a driver’s perspective, using dashboard camera recordings. This method was used to evaluate signage along a 69.6 km road construction zone in Poland associated with the phased upgrade of a dual carriageway with unlimited access into a motorway. The analysis focused on three distinct phases of the roadwork: the beginning of roadwork, the progress of roadwork, and finishing roadwork. The correctness, visibility, and quality of the road signs were assessed on a specially developed scale. The study found that 1135 road signs were unnecessary, which was equal to 36% of all signs. The majority of all signs (48.1%) indicated prohibition: more than one third (33.6%) of them were speed limit signs, of which 52% were posted without the need. It was demonstrated that the simple method applied in this study can be considered a useful tool to identify deficiencies in signage, which could ultimately improve road safety and make road management more sustainable. Moreover, this study confirmed again that the use of appropriate video recordings makes it faster and easier to conduct an inventory of road signs. Full article

While autonomous vehicles have the potential to mitigate risks associated with dangerous driving behaviors, the safety and stability of autonomous driving technology in real-world applications still require comprehensive tests. Scenario-based virtual simulation testing has emerged as a crucial approach for testing autonomous vehicles, especially in critical scenarios under complex environments, such as merging scenarios at roundabouts with unique traffic features. However, the lack of high-risk scenarios in the real-world traffic domain presents challenges for simulation tests. To address these challenges, this study proposes a scenario generation framework for complex roundabout environments, focusing on merging areas, which is driven by trajectory data and employs generative deep learning techniques to create adversarial hazardous scenarios. Specifically, leveraging real trajectory data from roundabouts, the framework utilizes a time series generative adversarial network (TimeGAN) to generate realistic safety-critical driving trajectories. By creating specific hazardous scenarios, this strategy broadens the library of test scenarios and speeds up the testing process for autonomous vehicles. The significance of the scenarios produced is proven using Simulation of Urban Mobility (SUMO) and CARLA simulation, confirming their necessity in autonomous driving testing. The TimeGAN model effectively captures the spatial–temporal features of merging scenarios, generating high-quality data that enhance the testing scenario library. Findings of this study contribute to solving the problem of scarcity of critical scenarios in virtual testing and accelerate the testing procedure for self-driving automobiles. Full article

The design of passenger-dedicated lane width is essential for freeway reconstruction and expansion projects. However, the technical standard of lane width established in China is based on trucks. This study aims to propose a passenger-dedicated lane width calculation method for freeways based on overtaking behavior. Computer vision technology was used to extract vehicle trajectories and dimensions from videos captured by an unmanned aerial vehicle (UAV). Statistical methods such as cumulative frequency statistics, typical percentile statistics and regression analysis were employed to elaborate on the lateral displacement and distance of vehicles during overtaking. The results show that vehicles’ lateral displacements are mainly related to behaviors such as lane changing, lateral distance maintenance and lane keeping. The body width sum of parallel vehicles has little effect on the geometric center distance but significantly reduces the wheel distance when increasing. The general value of the passenger-dedicated lane width on freeways is recommended to be 3.5 m, and the limit value is 3.25 m. Compared with existing lane width calculation methods, this study pays more attention to the relationship between vehicle width and lateral distance, which can better cope with the challenges caused by vehicle diversity in lane width design. Full article

Indonesia, a Southeast Asian country with a significant number of motorcycles, faces a high rate of motorcycle accidents, predominantly attributed to rider behavior. Various methods are available to study driver behavior, with the Naturalistic Driving Study (NDS) being one of the most advanced approaches. This study employed a vehicle-based NDS method, utilizing an instrumented motorcycle equipped with sensors and cameras to capture detailed riding activities. These sensors recorded data such as speed, throttle position, brake and horn activation, turn signal usage, and motorcycle tilt. These data provided insights into rider behavior in response to surrounding traffic conditions. The purpose of this research was to transform an electric motorcycle into an instrumented motorcycle and designing experiments to collect relevant data. This innovative approach enabled detailed observation and analysis of motorcyclist behavior in Indonesia, contributing valuable insights for developing strategies to reduce motorcycle accidents. Full article

Pedestrian trajectories are crucial for self-driving cars to plan their paths effectively. The sensors implanted in these self-driving vehicles, despite being state-of-the-art ones, often face inaccuracies in the perception of surrounding environments due to technical challenges in adverse weather conditions, interference from other vehicles’ sensors and electronic devices, and signal reception failure, leading to incompleteness in the trajectory data. But for real-time decision making for autonomous driving, trajectory imputation is no less crucial. Previous attempts to address this issue, such as statistical inference and machine learning approaches, have shown promise. Yet, the landscape of deep learning is rapidly evolving, with new and more robust models emerging. In this research, we have proposed an encoder–decoder architecture, the Human Trajectory Imputation Model, coined HTIM, to tackle these challenges. This architecture aims to fill in the missing parts of pedestrian trajectories. The model is evaluated using the Intersection drone the inD dataset, containing trajectory data at suitable altitudes, preserving naturalistic pedestrian behavior with varied dataset sizes. To assess the effectiveness of our model, we utilize L1, MSE, and quantile and ADE loss. Our experiments demonstrate that HTIM outperforms the majority of the state-of-the-art methods in this field, thus indicating its superior performance in imputing pedestrian trajectories. Full article

With the rise in the intelligence levels of automated vehicles, increasing numbers of modules of automated driving systems are being combined to achieve better performance and adaptability by reducing information loss. In this study, an integrated decision and motion planning system is designed for multi-object highways. A two-layer structure is presented to decouple the influence of the traffic environment and the dynamic control of ego vehicles using the cognitive safety area, the size of which is determined by naturalistic driving behavior. The artificial potential field method is used to comprehensively describe the influence of all external objects on the cognitive safety area, the lateral motion dynamics of which are determined by the attention mechanism of the human driver during lane changes. Then, the interaction between the designed cognitive safety area and the ego vehicle can be simplified into a spring-damping system, and the desired dynamic states of the ego vehicle can be obtained analytically for better computational efficiency. The effectiveness of this on improving traffic efficiency, driving comfort, safety, and real-time performance was validated using several comparative tests utilizing complicated scenarios with multiple vehicles. Full article

Red-light running at signalised intersections poses a significant safety risk, necessitating advanced predictive technologies to predict red-light violation behaviour, especially for advanced red-light warning (ARLW) systems. This research leverages Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models to forecast the red-light running and stopping behaviours of drivers in connected vehicles. We utilised data from the Ipswich Connected Vehicle Pilot (ICVP) in Queensland, Australia, which gathered naturalistic driving data from 355 connected vehicles at 29 signalised intersections. These vehicles broadcast Cooperative Awareness Messages (CAM) within the Cooperative Intelligent Transport Systems (C-ITS), providing kinematic inputs such as vehicle speed, speed limits, longitudinal and lateral accelerations, and yaw rate. These variables were monitored at 100-millisecond intervals for durations from 1 to 4 s before reaching various distances from the stop line. Our results indicate that the LSTM model outperforms the GRU in predicting both red-light running and stopping behaviours with high accuracy. However, the pre-trained GRU model performs better in predicting red-light running specifically, making it valuable in applications requiring early violation prediction. Implementing these models can enhance red-light violation countermeasures, such as dynamic all-red extension (DARE), decreasing the likelihood of severe collisions and enhancing road users’ safety. Full article

At present, achieving efficient, sustainable, and safe transportation has led to increasing attention on driving behavior recognition and advancements in autonomous driving. Identifying diverse driving styles and corresponding types is crucial for providing targeted training and assistance to drivers, enhancing safety awareness, optimizing driving costs, and improving autonomous driving systems responses. However, current studies mainly focus on specific driving scenarios, such as free driving, car-following, and lane-changing, lacking a comprehensive and systematic framework to identify the diverse driving styles. This study proposes a novel, data-driven approach to driving-style recognition utilizing naturalistic driving data NGSIM. Specifically, the NGSIM dataset is employed to categorize car-following and lane-changing groups according to driving-state extraction conditions. Then, characteristic parameters that fully represent driving styles are optimized through correlation analysis and principal component analysis for dimensionality reduction. The K-means clustering algorithm is applied to categorize the car-following and lane-changing groups into three driving styles: conservative, moderate, and radical. Based on the clustering results, a comprehensive evaluation of the driving styles is conducted. Finally, a comparative evaluation of SVM, Random Forest, and KNN recognition indicates the superiority of the SVM algorithm and highlights the effectiveness of dimensionality reduction in optimizing characteristic parameters. The proposed method achieves over 97% accuracy in identifying car-following and lane-changing behaviors, confirming that the approach based on naturalistic driving data can effectively and intelligently recognize driving styles. Full article

Among the many technological challenges of automated driving development, there is an increasing focus on the behavior of these systems. Behavior is usually associated with multiple layers of control. In this paper, we focus on motion planning and control, and how these layers can be tailored to produce different behavior. Our review aims to collect and judge the most used techniques in the field of path planning and control. It has been revealed that model predictive planning and control provides high flexibility, with the cost of high computational capacity. There are simpler algorithms, such as pure-pursuit and Stanley controllers, however, these have very few parameters, therefore, the number of possible behavior patterns is limited. Full article

Background/Objectives: This study addresses the gap in methodological guidelines for neuroergonomic attention assessment in safety-critical tasks, focusing on validating EEG indices, including the engagement index (EI) and beta/alpha ratio, alongside subjective ratings. Methods: A novel task-embedded reaction time paradigm was developed to evaluate the sensitivity of these metrics to dynamic attentional demands in a more naturalistic multitasking context. By manipulating attention levels through varying secondary tasks in the NASA MATB-II task while maintaining a consistent primary reaction-time task, this study successfully demonstrated the effectiveness of the paradigm. Results: Results indicate that both the beta/alpha ratio and EI are sensitive to changes in attentional demands, with beta/alpha being more responsive to dynamic variations in attention, and EI reflecting more the overall effort required to sustain performance, especially in conditions where maintaining attention is challenging. Conclusions: The potential for predicting the attention lapses through integration of performance metrics, EEG measures, and subjective assessments was demonstrated, providing a more nuanced understanding of dynamic fluctuations of attention in multitasking scenarios, mimicking those in real-world safety-critical tasks. These findings provide a foundation for advancing methods to monitor attention fluctuations accurately and mitigate risks in critical scenarios, such as train-driving or automated vehicle operation, where maintaining a high attention level is crucial. Full article