Search Results (99)

The increasing integration of autonomous driving technologies into heavy-duty road transport requires a clear understanding of how these systems affect professional drivers’ working time, vehicle utilization, and regulatory compliance. This study develops a model-based comparative analysis to assess the cooperation between human drivers and autonomous trucks at SAE Levels 3 and 4. Using EU Regulation (EC) No 561/2006 as a legal framework, single-driver, double-driver, and ego vehicle scenarios were simulated to evaluate changes in working time classification and vehicle movement. The results indicate that Level 3 automation enables up to 13.25 h of daily vehicle movement while complying with working time regulations, compared with the 10-h limit for conventional operation. Level 4 automation further extends the effective movement time to 14.25 h in double-crew configurations, offering opportunities for increased efficiency without violating labor codes. The novelty of this work lies in the quantitative modeling of human–machine collaboration in professional transport under real regulatory constraints. These findings provide a foundation for regulatory updates, tachograph adaptation to AI-driven vehicles, and the design of hybrid driver roles. Future research will focus on validating these models in real-world transport operations and assessing the implications of Level 5 autonomy for logistics networks and labor markets. Full article

Autonomous vehicles and intelligent traffic transportation are widely investigated for road networks. Context-aware traffic light scheduling algorithms determine signal phases by analyzing the real-time characteristics and contextual information of competing traffic flows. The context of traffic flows mainly considers the existence of regular, emergency, or heavy vehicles. This is an important factor in setting the phases of the traffic light schedule and assigning a high priority for emergency vehicles to pass through the signalized intersection first. VANET technology, through its communication capabilities and the exchange of data packets among moving vehicles, is utilized to collect real-time traffic information for the analyzed road scenarios. This introduces an attractive environment for hackers, intruders, and criminals to deceive drivers and intelligent infrastructure by manipulating the transmitted packets. This consequently leads to the deployment of less efficient traffic light scheduling algorithms. Therefore, ensuring secure communications between traveling vehicles and verifying the integrity of transmitted data are crucial. In this work, we investigate the possible attacks on the integrity of transferred messages and vehicles’ identities and their effects on the traffic light schedules. Then, a new secure context-aware traffic light scheduling system is proposed that guarantees the integrity of transmitted messages and verifies the vehicles’ identities. Finally, a comprehensive series of experiments were performed to assess the proposed secure system in comparison to the absence of security mechanisms within a simulated road intersection. We can infer from the experimental study that attacks on the integrity of vehicles have different effects on the efficiency of the scheduling algorithm. The throughput of the signalized intersection and the waiting delay time of traveling vehicles are highly affected parameters. Full article

This study investigates how three technical factors—steering wheel tilt, torque, and cabin vibration frequency—affect driver posture. Heavy-truck drivers often suffer from musculoskeletal disorders (MSDs), mainly due to poor cabin ergonomics and prolonged postures during work. In countries like Ecuador, making major structural changes to cabin design is not feasible. These factors were identified through video analysis and surveys from drivers at two Ecuadorian trucking companies. An experimental system was developed using a simplified cabin to control these variables, while posture and muscle activity were recorded in 16 participants using motion capture, inertial sensors, and electromyography (EMG) on the upper trapezius, middle trapezius, triceps brachii, quadriceps muscle, and gastrocnemius muscle. The test protocol simulated key truck-driving tasks. Data were analyzed using ANOVA (${\displaystyle p<0.05}$), with technical factors and mass index as independent variables, and posture metrics as dependent variables. Results showed that head mass index significantly affected head abduction–adduction (${\displaystyle -}$8.12 to ${\displaystyle -}$2.18°), and spine mass index influenced spine flexion–extension (${\displaystyle -}$0.38 to 6.99°). Among technical factors, steering wheel tilt impacted trunk flexion–extension (${\displaystyle -}$13.56 to 16.99°) and arm rotation (${\displaystyle -}$31.1 to 19.7°). Steering wheel torque affected arm rotation (${\displaystyle -}$30.49 to ${\displaystyle -}$6.77°), while vibration frequency influenced forearm flexion–extension (3.76 to 16.51°). EMG signals showed little variation between muscles, likely due to the protocol’s short duration. These findings offer quantitative support for improving cabin ergonomics in low-resource settings through targeted, cost-effective design changes. 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

To enhance riding comfort for drivers of heavy-duty vehicles, this paper introduces a novel adaptive prescribed performance control (APPC) for active seat suspension systems. The model incorporates dynamic friction and hysteresis damping effects to capture the complex behavior of the seat suspension. The accuracy of the proposed model is validated through experimental data. The controller utilizes a prescribed performance function (PPF) to regulate the dynamic response of the system, combined with an adaptive backstepping control (ABC) method to account for system uncertainties, such as variations in driver weight, friction, suspension stiffness, and damping coefficients. A set of parameter estimators, governed by innovative adaptive laws, compensates for estimation errors. Furthermore, the stability of the controlled system is rigorously demonstrated. Both simulation and experimental tests, including bump and random excitation tests, are conducted to assess the controller performance in both time and frequency domains. The results confirm that the proposed controller effectively mitigates vibrations in the driver–seat system and demonstrates robustness against system uncertainties. Full article

Road transport remains a dominant mode of transportation in Europe, yet it significantly contributes to fatalities and injuries, particularly in crashes involving heavy goods vehicles and trucks. Advanced Driver Assistance Systems (ADAS) are widely recognized as a promising solution for improving truck safety. However, given that the average age of the EU truck fleet is 12 years and ADAS technologies is mandatory for new vehicles from 2024, their full impact on crash reduction may take over a decade to materialize. To address this delay, retrofitting ADAS onto existing truck fleets presents a viable strategy for enhancing road safety more promptly. This study integrates a systematic literature review, cost–benefit analysis, and a pilot field test to assess the feasibility and effectiveness of retrofitting ADAS. The literature review categorizes ADAS technologies based on their crash prevention potential, cost-effectiveness, market availability, and overall efficacy. A cost–benefit analysis applied to the Italian context estimates that ADAS retrofitting could save over 250 lives annually and reduce societal costs by more than €350 million. Moreover, the economic analysis indicates that the installation cost of retrofitted ADAS is outweighed by the societal savings associated with prevented crashes. Finally, pilot field testing suggests high user acceptance, providing a foundation for further large-scale studies. In conclusion, retrofitting ADAS onto existing truck fleets represents an effective and immediate strategy for significantly reducing truck-related crashes in Europe, bridging the gap until newer, ADAS-equipped vehicles dominate the fleet. Full article

Conducting research on the effectiveness of driving simulators is of great significance for enhancing the availability of driving simulator systems. However, in risky environments, traditional methods have the limitation of making it difficult to conduct real vehicle experiments. Therefore, this study proposes a method based on driver physiological indicators to evaluate the effectiveness of driving simulators in risky environments. On the one hand, the two-dimensional extended time to collision theoretical model (2D-TTC) was used to calculate the risk degree. Then, the similarity between the risk degree and the drivers’ electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) data sequences was calculated based on the dynamic time warping (DTW) model. On the other hand, we used the complexity and sample entropy of ECG and EMG as indicators to assess the drivers’ physiological load. This paper used intersections as risk scenarios to conduct driving simulation experiments to verify the feasibility of the above method. It was found that changes in drivers’ physiological indicators were consistent with changes in risk degree, with the DTW values of risk degree and drivers’ EDA tending to become smaller and the two sequence values closer to being similar. It was also found that the complexity and the sample entropy of the driver’s ECG and EMG showed higher values in the simulated poor sight intersection scenario compared to the intersection with good sight. In addition, in the simulated heavy traffic intersection scenario, physiological parameters such as EMG complexity and sample entropy, as well as ECG complexity, were higher than in the low traffic flow intersection. These findings are highly consistent with the characteristics of physiological responses in real driving environments, fully demonstrating the effectiveness of the test-driving simulation system in simulating risky traffic scenarios. The method proposed in this paper overcomes the limitations of traditional approaches and effectively validates the effectiveness of driving simulation systems in risky environments. The research results can drive further development and application of driving simulation technology. Full article

One of the key reasons why the road capacity of urban roads in China often fails to meet the designed capacity is the mixture of heavy vehicles (slow-moving) and light vehicles (fast-moving). This paper presents a two-lane cellular automaton model suitable for simulating urban road traffic that includes intersections, based on the NaSch model. The model comprehensively takes into account multiple key factors, such as vehicle safety distance, speed differences between adjacent vehicles, the acceleration and deceleration performance of different types of vehicles, and driver reaction time, enabling it to more realistically reproduce the complex characteristics of mixed traffic flows on urban roads. The paper investigates and analyzes the influence of traffic flow density and the proportion of heavy vehicles on the moving bottleneck effect in urban roads from several aspects, including space–time evolution diagrams, traffic flow, average speed, and lane-changing rates. The results indicate that the model established in this paper successfully simulates the characteristics of mixed traffic flows on urban roads, and the simulation results align with actual traffic conditions, achieving the expected simulation effects. Before the traffic volume reaches saturation, the higher the proportion of heavy vehicles on urban roads, the stronger the moving bottleneck effect. This confirms the necessity of conducting research on the phenomenon of moving bottlenecks and the mechanisms of traffic impacts in urban roads, providing a scientific basis for formulating effective traffic dispersion measures and alleviating traffic congestion in the future. This research possesses significant practical meaning and value. Full article

Currently, the control strategies of electric power-steering (EPS) systems mainly focus on power-steering torque control. There is no direct relationship between steering torque and steering motion intensity, which makes steering wheel adjustment difficult and does not easily meet the driver’s expectations. This paper proposes a method to represent the driver’s steering intention (steering torque) in the form of steering motion intensity based on the analysis of the dynamic characteristics of the EPS system and vehicle motion dynamics. This method establishes the optimal relationship between steering torque and motion intensity according to Stevens’s law of psychology, providing a theoretical basis for optimizing the driving feel. The study uses lateral acceleration and steering wheel steering angles as intermediate variables to connect the driver’s input information with vehicle dynamics and calculates the steering torque through the inverse dynamics of the steering system and the inverse dynamics of vehicle motion. The nonlinear relationship of steering assistance torque with vehicle speed and steering torque is analyzed into three functional modules. A new comprehensive model is proposed to analyze the characteristics of EPS steering assist based on a “comfortable driving style”, “sporty driving style”, and “multi-level driving style—comfortable driving style at low speed, sporty at medium speed, and heavy at high speed”, corresponding to three different power-steering characteristic maps. Full article

The globalization of electric vehicle development and production is a significant goal. The availability of charging stations helps to encourage the global transition to electric vehicles, which may lead to a decrease in traditional fuel consumption. Nevertheless, the rise in the number of electric vehicles is accompanied by sustainability issues, such as managing the grid’s electrical demand, building more charging stations, and providing electricity from renewable resources in an efficient and sustainable manner, especially in Saudi Arabia. This work focused on three challenges regarding the installation of fast charging stations (FCSs) for electric vehicles (EVs) on highways. The first challenge is choosing optimal locations on highways to address the range of anxiety of EV drivers. The second challenge is to fuel these FCSs using renewable resources, such as photovoltaic (PV) panels, to make FCSs sustainable. The last challenge is to design FCSs by considering both highway driving behavior and the available renewable energy resources in order to cover charging demand. All of these challenges should be considered while planning the EV charging infrastructure of Saudi highways from both technical and economic perspectives. Thus, using the HOMER^® Grid software (version 1.10.1 June 2023), locations on Saudi Arabian highways were selected based on the renewable resources of several roads that support a large number of vehicles traveling on them. These roads were the Makkah to Riyadh, Makkah to Abha, Riyadh to Dammam, Riyadh to NEOM, and Jeddah to NEOM roads. Electric vehicle charging stations with a capacity of 200 kW, 300 kW, and 500 kW were designed on these roads based on their natural renewable resources, which is PV energy. These roads are the most important roads in the Kingdom and witness heavy traffic. An economic study of these stations was carried out in addition to considering their efficiency. This study revealed that the 500 kW station is ideal for charging electric vehicles, with an annual energy production of 3,212,000 kWh. The 300 kW station had better efficiency but higher capital expenses. The 200 kW station could charge 6100 vehicles annually. The three stations on the Makkah to Riyadh, Makkah to Abha, and Riyadh to Dammam roads can charge 65,758 vehicles annually. The total cost of the project was USD 2,786,621, with the 300 kW plant having the highest initial investment, which can be potentially justified due to its higher power output. This study provides a comprehensive overview of the project costs and the potential returns of using solar power plants for charging electric vehicles. Full article

PURPOSE: Job profiles such as heavy vehicle drivers and transportation office workers that involve prolonged static and inappropriate postures and forceful exertions often impact an individual’s health, leading to various disorders, most commonly musculoskeletal disorders (MSDs). In the present study, various individual risk factors, such as age, weight, height, BMI, sleep patterns, work experience, smoking status, and alcohol intake, were undertaken to see their influence on MSDs. METHODS: The modified version of the Nordic Questionnaire was administered in the present cross-sectional study to collect data from 48 heavy vehicle drivers and 40 transportation office workers. RESULTS: The analysis revealed low back pain (LBP), knee pain (KP), and neck pain (NP) to be the dominant pains suffered by the participants from both occupational groups. LBP, KP, and NP were suffered by 56%, 43.75%, and 39% heavy vehicle drivers and 47.5%, 40%, and 27.5% transport office workers, respectively. From the insignificant value of Chi-square, it can be inferred that the participants from both occupations experience similar levels of LBP, KP, and NP. The Bayesian model applied to the total sample showed that NP influenced KP, which further influenced the LBP of the workers. Age was predicted as LBP’s most significant risk factor by the logistic regression model when applied to the total sample, while NP was found to decrease with an increase in per unit sleep. CONCLUSIONS: The overall results concluded that heavy vehicle drivers and office workers, irrespective of their different job profiles, endured pain similarly. Full article

The safety of construction and maintenance work zones has been highlighted as a crucial aspect of construction management that requires special attention due to the increasing number of fatal and non-fatal injuries in recent years. Temporary traffic control (TTC) is required by the Occupational Safety and Health Administration (OSHA) to improve overall safety performance during road construction and maintenance projects. The fact that speeding and distracted drivers may overlook TTC warning signs and directions has been reported as one of the leading causes of work zone incidents. This study aimed to examine both the impact of temporary portable rumble strips (TPRSs) on traffic speeds and the response of different vehicle types in road work zones, including trucks and cars. Accordingly, field experiments were conducted in collaboration with the Road Commission for Oakland County (RCOC) in Michigan. The findings indicate that TPRSs have a statistically significant impact on the driving speed of light vehicle drivers but not on medium and heavy vehicles, such as trucks. This study contributes to the existing literature by quantifying the safety benefits of TPRS use, providing valuable data for policymakers and construction professionals. By demonstrating the effectiveness of TPRSs in reducing the speed of light vehicles, this research supports the implementation of these systems as a practical measure for enhancing safety within road construction work zones. Additionally, this study highlights the need for tailored approaches to address the limited impact on larger vehicles, underscoring the importance of developing complementary strategies to ensure comprehensive safety improvements across all vehicle types. Full article

Accidents involving heavy road vehicles are often destructive, causing operational losses, human casualties, infrastructure losses, and negative environmental impacts. The risk is especially high in wintertime traffic. The Eureka Xecs SafeTrucks project (Heavy traffic safety improvements by advanced dynamics and road weather services) develops real-time vehicle-specific weather and safety services tailored to each vehicle, based on the vehicle’s own sensor-based observations combined with data from weather service systems and an analysis of the vehicle’s own dynamics. The services will also be analyzed by Digital Twin modeling in Hardware-in-the-Loop (HIL) and Driver-In-the-Loop (DIL) scenarios, in order to evaluate and refine them in a controlled environment. This paper focuses on operative fleet piloting, while the Digital Twin approach will be presented in future work. The pilot services are ultimately tested in a pilot system within operative heavy traffic. This paper presents the concept and architecture of the platform, with preliminary results of pilot services operation, alternative communication analysis, and system evaluation. Full article

The rising frequency of heavy truck accidents in China poses a significant public safety risk, endangering lives and property. However, current research based on data from heavy truck accidents in China remains limited, making it challenging to support the formulation of traffic management measures. To mitigate the severity of these accidents, this study analyzed five years of heavy truck accident data from a specific region in China and developed logistic regression models for different road conditions. The aim was to identify the key factors influencing accident severity and understand the underlying mechanisms. The findings revealed that, under urban road conditions, the severity of heavy truck accidents is significantly impacted by factors such as lighting conditions, road safety attributes, driver age, and vehicle driving status. On highways, accident severity is largely influenced by visibility, roadside protection measures, intersection and section types, vehicle driving status, inter-vehicle accident types, and road safety features. On expressways, critical factors include inter-vehicle accident types, driver violations, visibility, and road alignment. In conclusion, the factors contributing to the severity of heavy truck accidents vary according to road conditions, which necessitates tailored traffic management strategies. The study’s findings offer theoretical support for more targeted approaches to preventing and controlling heavy truck traffic accident severity under different road conditions in China. Full article

When launching a heavy-duty vehicle, torque and position control during automatic clutch engagement is critical, and the driver’s intention to launch and changes in the vehicle’s launching resistance make clutch control more complex. This paper analyses the automatic engagement process of automated mechanical transmission (AMT) clutches and proposes an optimal control of the clutch torque for launching heavy-duty vehicles. Firstly, a fuzzy neural network (FNN)-based vehicle launching states recognition (LSR) system is designed for distinguishing the driver’s launching intention and the vehicle’s launching equivalent moment of resistance. Secondly, jerk, friction work, and launching reserve power are taken as the performance indexes for clutch torque optimization, the weight coefficients of each performance index are adjusted according to the LSR results, and the optimal clutch torque is solved by using the minimum value principle based on the shooting method. Finally, simulations and tests are conducted to validate the strategy of optimizing clutch torque, and the impact of torque optimization on the position change during the engagement process is analyzed. The results indicate that under different driver’s intentions, vehicle masses, and road gradient conditions, the jerk, friction work, and slipping time of heavy vehicles during the launching process are improved by applying the optimization strategy. Full article