Search Results (114)

The development of Advanced Driver Assistance Systems (ADASs) has reached a stage where, in addition to the traditional challenges of path planning and control, there is an increasing focus on the behavior of these systems. Assistance functions shall be personalized to deliver a full user experience. Therefore, driver modeling is a key area of research for next-generation ADASs. One of the most common tasks in everyday driving is lane keeping. Drivers are assisted by lane-keeping systems to keep their vehicle in the center of the lane. However, human drivers often deviate from the center line. It has been shown that the driver’s choice to deviate from the center line can be modeled by a linear combination of preview curvature information. This model is called the Linear Driver Model. In this paper, we fit the LDM parameters to real driving data. The drivers are then clustered based on the individual parameters. It is shown that clusters are not only formed by the numerical similarity of the driver parameters, but the drivers in a cluster actually have similar behavior in terms of path selection. Finally, an Extended Kalman Filter (EKF) is proposed to learn the model parameters at run-time. Any new driver can be classified into one of the driver type groups. This information can be used to modify the behavior of the lane-keeping system to mimic human driving, resulting in a more personalized driving experience. Full article

This study proposes an automatic gear shift classification algorithm in M1 category vehicles using data acquired through the onboard diagnostic system (OBD II) and GPS. The proposed approach is based on the analysis of identification parameters (PIDs), such as manifold absolute pressure (MAP), revolutions per minute (RPM), vehicle speed (VSS), torque, power, stall times, and longitudinal dynamics, to determine the efficiency and behavior of the vehicle in each of its gears. In addition, the unsupervised K-means algorithm was implemented to analyze vehicle gear changes, identify driving patterns, and segment the data into meaningful groups. Machine learning techniques, including K-Nearest Neighbors (KNN), decision trees, logistic regression, and Support Vector Machines (SVMs), were employed to classify gear shifts accurately. After a thorough evaluation, the KNN (Fine KNN) model proved to be the most effective, achieving an accuracy of 99.7%, an error rate of 0.3%, a precision of 99.8%, a recall of 99.7%, and an F1-score of 99.8%, outperforming other models in terms of accuracy, robustness, and balance between metrics. A multiple linear regression model was developed to estimate instantaneous fuel consumption (in L/100 km) using the gear predicted by the KNN algorithm and other relevant variables. The model, built on over 66,000 valid observations, achieved an R² of 0.897 and a root mean square error (RMSE) of 2.06, indicating a strong fit. Results showed that higher gears (3, 4, and 5) are associated with lower fuel consumption. In contrast, a neutral gear presented the highest levels of consumption and variability, especially during prolonged idling periods in heavy traffic conditions. In future work, we propose integrating this algorithm into driver assistance systems (ADAS) and exploring its applicability in autonomous vehicles to enhance real-time decision making. Such integration could optimize gear shift timing based on dynamic factors like road conditions, traffic density, and driver behavior, ultimately contributing to improved fuel efficiency and overall vehicle performance. Full article

Urban planners are progressively recognizing the significant effects of the built environment and land use on PM_2.5 levels. However, in analyzing the drivers of PM_2.5 levels, researchers’ reliance on annual mean and seasonal means may overlook the monthly variations in PM_2.5 levels, potentially impeding accurate predictions during periods of high pollution. This study focuses on the area within the Sixth Ring Road of Beijing, China. It utilizes gridded monthly and annual mean PM_2.5 data from 2019 as the dependent variable. The research selects 33 independent variables from the perspectives of the built environment and land use. The Extreme Gradient Boosting (XGBoost) method is employed to reveal the driving impacts of the built environment and land use on PM_2.5 levels. To enhance the model accuracy and address the randomness in the division of training and testing sets, we conducted twenty comparisons for each month. We employed Shapley Additive Explanations (SHAP) and Partial Dependence Plots (PDP) to interpret the models’ results and analyze the interactions between the explanatory variables. The results indicate that models incorporating both the built environment and land use outperformed those that considered only a single aspect. Notably, in the test set for April, the R² value reached up to 0.78. Specifically, the fitting accuracy for high pollution months in February, April, and November is higher than the annual mean, while July shows the opposite trend. The coefficient of variation for the importance rankings of the seven key explanatory variables exceeds 30% for both monthly and annual means. Among these variables, building density exhibited the highest coefficient of variation, at 123%. Building density and parking lots density demonstrate strong explanatory power for most months and exhibit significant interactions with other variables. Land use factors such as wetlands fraction, croplands fraction, park and greenspace fraction, and forests fraction have significant driving effects during the summer and autumn seasons months. The research on time scales aims to more effectively reduce PM_2.5 levels, which is essential for developing refined urban planning strategies that foster healthier urban environments. Full article

Drivers’ visual characteristics have an important impact on traffic safety, but existing studies are mostly limited to single-scene analyses and lack a systematic study on the dynamic changes in drivers’ eye tracking characteristics on different road sections. In this study, 23 drivers were recruited to wear the aSee Glasses eye tracking device and driving tests were conducted on four typical road sections, namely, straight ahead, turning, climbing, and downhill. The average fixation duration, pupil diameter, and the saccade amplitude of the eye tracking were collected, one-way analysis of variance (ANOVA) was used to explore the differences between the different road sections, and a mathematical model of changes in the visual characteristics over time was constructed, based on the fitting of the data. Computerized fitting models of changes over time were also constructed using the Origin 2021 software. The results show that different road sections had significant effects on drivers’ visual tasks: the longest average fixation duration was found in the straight road section, the largest pupil diameter was found in the curved road section, and the highest saccade amplitude was found in the downhill road section, reflecting the influence of the complexity of the driving task on the cognitive load. The fitted model further reveals the dynamic change law of eye tracking indicators over time, providing a quantitative basis for modeling driving behavior and visual tasks. This study provides a theoretical basis and practical reference for the optimal design of advanced driver assistance systems, traffic safety management, and road planning. Full article

Physics-assisted machine learning is a powerful framework that enhances data efficiency by integrating the strengths of conventional machine learning with physical knowledge. This paper applies this concept and focuses on the design of a driver evaluator using physics-assisted unsupervised learning, which serves as a virtual reference generator that provides different driving modes for vehicles equipped with active actuators. A strategy that applies sensitivity analysis regarding the vehicle handling performance, aiming to reduce the computational workload of the clustering algorithms, is proposed. First, a bicycle model with nonlinear Pacejka’s tire models is established for the analysis of lateral dynamics. Next, mathematical interpretations of sensitivity analysis are derived to evaluate the contribution of physical parameters to the system response and build the reduced parameters set. Then, Gaussian mixture models are fitted to a database generated with the full parameters set and another with the reduced set, respectively. Finally, step-steer and constant radius tests are performed to assess the handling performance with respect to the two validated centroids. Comparisons of lateral dynamics and understeer characteristics indicate that the proposed method can accurately distinguish driving modes in a much faster manner compared to traditional machine learning. This methodology has significant potential for practical applications with large databases and more complex systems. Full article

Pine caterpillar (Dendrolimus) infestations threaten pine forests, causing severe ecological and economic impacts. Identifying the driving factors behind these infestations is essential for effective forest management. This study uses the APCIRD framework combined with an improved random forest model to analyze spatiotemporal changes in infestation risk and the driving effects of habitat factors in Northeast China. From 2019 to 2024, we applied SHapley Additive exPlanations (SHAP), frequency analysis, fitting functions, and GeoDetector to quantify the impact of key drivers, such as snow cover and soil, on infestation risk. The findings include (1) the APCIRD framework with the MLP-random forest model (MRF) accurately assesses infestation risks. MRF is composed of MLP and random forest. Between 2019 and 2024, areas with high infestation risk declined, shifting from higher to lower levels, with Eastern Heilongjiang and Southwest Liaoning remaining as key concern areas; (2) snow cover and soil factors are critical to infestation risk, with eight key habitat factors significantly affecting the risk. Their relationships with infestation risk follow complex, non-monotonic quartic and cubic patterns; (3) factors triggering high infestation risks are mostly at low to moderate levels. High-risk areas tend to have low to moderate elevation (<800 m), moderate to high solar radiation and temperature, gentle slopes (<30°), low to moderate evaporation, shallow snow depth (<0.02), moderate snow temperature (266.73–275), low to moderate soil moisture (0.2–0.3), moderate to high soil temperature (276.73–286.92), low to moderate rainfall, moderate wind speed, low leaf area index, high vegetation type, low vegetation cover, low population density, and low surface runoff. Interactions between factors provide a stronger explanation of infestation risk than individual factors. The APCIRD framework, combined with MRF, offers valuable insights for understanding the drivers of pine caterpillar infestations. Full article

The latest developments in Advanced Driver Assistance Systems (ADAS) have greatly enhanced the comfort and safety of drivers. These technologies can identify driver abnormalities like fatigue, inattention, and impairment, which are essential for averting collisions. One of the important aspects of this technology is automated traffic accident detection and prediction, which may help in saving precious human lives. This study aims to explore critical features related to traffic accident detection and prevention. A public US traffic accident dataset was used for the aforementioned task, where various machine learning (ML) models were applied to predict traffic accidents. These ML models included Random Forest, AdaBoost, KNN, and SVM. The models were compared for their accuracies, where Random Forest was found to be the best-performing model, providing the most accurate and reliable classification of accident-related data. Owing to the black box nature of ML models, this best-fit ML model was executed with explainable AI (XAI) methods such as LIME and permutation importance to understand its decision-making for the given classification task. The unique aspect of this study is the introduction of explainable artificial intelligence which enables us to have human-interpretable awareness of how ML models operate. It provides information about the inner workings of the model and directs the improvement of feature engineering for traffic accident detection, which is more accurate and dependable. The analysis identified critical features, including sources, descriptions of weather conditions, time of day (weather timestamp, start time, end time), distance, crossing, and traffic signals, as significant predictors of the probability of an accident occurring. Future ADAS technology development is anticipated to be greatly impacted by the study’s conclusions. A model can be adjusted for different driving scenarios by identifying the most important features and comprehending their dynamics to make sure that ADAS systems are precise, reliable, and suitable for real-world circumstances. Full article

To enhance road safety and optimize intelligent driving systems, this study introduces the concept of “driving style tendency” to characterize short-term driver behavior, particularly lane-changing patterns. A multidimensional framework is established to analyze driving roles and behaviors, utilizing a Hidden Semi-Markov Model and Hierarchical Dirichlet Process for the unsupervised segmentation of driving trajectory data into behavioral primitives. By systematically analyzing driver behaviors in leading and following scenarios, characteristic thresholds are derived through distribution fitting, enabling the development of a non-parametric Bayesian-based scoring method for driving style tendency. The K-means clustering algorithm is employed to transform primitive segments into quantifiable semantic information, facilitating the interpretation of driver behavior preferences. This research contributes to improved collision risk prediction in complex traffic environments, supports the design of personalized driving assistance systems, and provides valuable insights for autonomous driving technology development. Full article

The ability to respond swiftly and accurately to visual stimuli is critical for safe driving. The traditional Psychomotor Vigilance Test (PVT) primarily assesses response time (RT) using finger inputs, but these do not directly evaluate foot responses essential for vehicle control. This study introduces a novel Foot Psychomotor Vigilance Test (Foot PVT) designed to measure the RTs of the foot in response to simulated traffic lights. The Foot PVT integrates a traffic light display interface with a three-pedal system, simulating basic driving conditions. RTs are recorded for three colors (blue, yellow, red) displayed in a randomized order, and the response accuracy is evaluated based on the pedal input. The system also measures correction times for errors, offering insights into a driver’s ability to recover from mistakes. Validation experiments were conducted with eleven healthy younger (25 ± 3 years) and eleven healthy older adult participants (73 ± 4 years). The results showed that the older adult participants (818 ± 84 ms) exhibited significantly longer RTs than the younger participants (725 ± 74 ms, p = 0.016), consistent with age-related cognitive and motor decline. Interestingly, the older participants had fewer false starts, suggesting a compensatory cautious approach to responding. The Foot PVT has the potential to serve as a low-cost, efficient screening tool for evaluating driving fitness, particularly for older adult individuals and novice drivers. Full article

Bus drivers have an important role in ensuring road safety, as their driving circumstances fluctuate due to the combined influence of physiological, psychological, and environmental dynamics, which can cause complex and varied driving dangers. Quantifying and assessing drivers’ risk characteristics under various scenarios, as well as finding the best fit with their work schedules, is critical for enhancing bus safety. This research first uses the entropy weight method, which is based on historical warning data, to examine the risk characteristics of bus drivers in various complicated contexts. It then creates an objective function targeted at minimizing the operational risk for a specific bus route. This function uses the quasi-Vogel approach and an improved simulated annealing algorithm to optimize and restructure the scheduling table, taking individual driver risk characteristics into account. Finally, the analysis is confirmed and examined with actual operational data from the Zhenjiang Bus Line 3. The data show that enhanced bus operations resulted in a 7.22% gain in overall safety and a 33.76% improvement in balancing levels. These insights provide valuable theoretical guidance as well as practical references for the safe operation and administration of public buses. Full article

Improving the accuracy of parking space recognition is crucial in the fields for Automated Valet Parking (AVP) of autonomous driving. In AVP, accurate free space recognition significantly impacts the safety and comfort of both the vehicles and drivers. To enhance parking space recognition and annotation in unknown environments, this paper proposes an automatic parking space annotation approach with tight coupling of Lidar and Inertial Measurement Unit (IMU). First, the pose of the Lidar frame was tightly coupled with high-frequency IMU data to compensate for vehicle motion, reducing its impact on the pose transformation of the Lidar point cloud. Next, simultaneous localization and mapping (SLAM) were performed using the compensated Lidar frame. By extracting two-dimensional polarized edge features and planar features from the three-dimensional Lidar point cloud, a polarized Lidar odometry was constructed. The polarized Lidar odometry factor and loop closure factor were jointly optimized in the iSAM2. Finally, the pitch angle of the constructed local map was evaluated to filter out ground points, and the regions of interest (ROI) were projected onto a grid map. The free space between adjacent vehicle point clouds was assessed on the grid map using convex hull detection and straight-line fitting. The experiments were conducted on both local and open datasets. The proposed method achieved an average precision and recall of 98.89% and 98.79% on the local dataset, respectively; it also achieved 97.08% and 99.40% on the nuScenes dataset. And it reduced storage usage by 48.38% while ensuring running time. Comparative experiments on open datasets show that the proposed method can adapt to various scenarios and exhibits strong robustness. Full article

In the field of autonomous driving, it is important to protect vulnerable road users (VRUs) and ensure the safety of autonomous driving effectively by improving the detection accuracy of VRUs in the driver’s field of vision. However, due to the strong temporal similarity between pedestrians and cyclists, the insensitivity of the traditional least squares method to their differences results in its suboptimal classification performance. In response to this issue, this paper proposes an algorithm for classifying pedestrian and cyclist targets based on the micro-Doppler effect. Firstly, distinct from conventional time-frequency fusion methods, a preprocessing module was developed to solely perform frequency-domain fitting on radar echo data of pedestrians and cyclists in forward motion, with the purpose of generating fitting coefficients for the classification task. Herein, wavelet threshold processing, short-time Fourier transform, and periodogram methods are employed to process radar echo data. Then, for the heightened sensitivity to inter-class differences, a fractional polynomial is introduced into the extraction of micro-Doppler characteristics of VRU targets to enhance extraction precision. Subsequently, the support vector machine technique is embedded for precise feature classification. Finally, subjective comparisons, objective explanations, and ablation experiments demonstrate the superior performance of our algorithm in the field of VRU target classification. Full article

One of the most significant and widely used features currently in autonomous vehicles is the cruise control system that not only deals with constant vehicle velocities but also aims to optimize the safety and comfortability of drivers and passengers. The accuracy and precision of system responses are responsible for cruise control system efficiency via control techniques and algorithms. This study presents the dynamic cruise control system model, then investigates a genetic algorithm of the proportional–integral–derivative (PID) controller with the linear quadratic regulator (LQR) based on four fitness functions, the mean squared error (MSE), the integral squared error (ISE), the integral time squared error (ITSE) and the integral time absolute error (ITAE). Then, the response of the two controllers, PID and LQR, with the genetic algorithm was compared to the response performance of the fuzzy and fuzzy integral (Fuzzy-I) controllers. The MATLAB 2024a program simulation was employed to represent the system time response of each proposed controller. The output simulation of these controllers shows that the type of system stability response was related to the type of controller implemented. The results show that the Fuzzy-I controller outperforms the other proposed controllers according to the least Jmin function, which represents the minimum summation of the overshoot, settling time, and steady-state error of the cruise control system. This study demonstrates the effectiveness of driving accuracy, safety, and comfortability during acceleration and deceleration due to the smoothness and stability of the Fuzzy-I controller with a settling time of 5.232 s and when converging the steady-state error to zero. Full article

Mission-based routes for various occupations play a crucial role in occupational driver safety, with accident causes varying according to specific mission requirements. This study focuses on the development of a system to address driver distraction among law enforcement officers by optimizing the Driver–Vehicle Interface (DVI). Poorly designed DVIs in law enforcement vehicles, often fitted with aftermarket police equipment, can lead to perceptual-motor problems such as obstructed vision, difficulty reaching controls, and operational errors, resulting in driver distraction. To mitigate these issues, we developed a driving simulation platform specifically for law enforcement vehicles. The development process involved the selection and placement of sensors to monitor driver behavior and interaction with equipment. Key criteria for sensor selection included accuracy, reliability, and the ability to integrate seamlessly with existing vehicle systems. Sensor positions were strategically located based on previous ergonomic studies and digital human modeling to ensure comprehensive monitoring without obstructing the driver’s field of view or access to controls. Our system incorporates sensors positioned on the dashboard, steering wheel, and critical control interfaces, providing real-time data on driver interactions with the vehicle equipment. A supervised machine learning-based prediction model was devised to evaluate the driver’s level of distraction. The configured placement and integration of sensors should be further studied to ensure the updated DVI reduces driver distraction and supports safer mission-based driving operations. Full article

Clonal hematopoiesis (CH), the relative expansion of mutant clones, is derived from hematopoietic stem cells (HSCs) with acquired somatic or cytogenetic alterations that improve cellular fitness. Individuals with CH have a higher risk for hematological and non-hematological diseases, such as cardiovascular disease, and have an overall higher mortality rate. Originally thought to be restricted to a small fraction of elderly people, recent advances in single-cell sequencing and bioinformatics have revealed that CH with multiple expanded mutant clones is universal in the elderly population. Just a few years ago, phylogenetic reconstruction across the human lifespan and novel sensitive sequencing techniques showed that CH can start earlier in life, decades before it was thought possible. These studies also suggest that environmental factors acting through aberrant inflammation might be a common theme promoting clonal expansion and disease progression. However, numerous aspects of this phenomenon remain to be elucidated and the precise mechanisms, context-specific drivers, and pathways of clonal expansion remain to be established. Here, we review our current understanding of the cellular mechanisms driving CH and specifically focus on how pro-inflammatory factors affect normal and mutant HSC fates to promote clonal selection. Full article