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Human factors, including the level of cognitive functioning, are the most influential factors in road traffic crashes. Among cognitive abilities, executive functions (EFs) of the brain play a pivotal role in driving performance and outcomes, including crash numbers. The current study was aimed to explore, for the first time, the ability of EF tests to predict success on the official driving tests in applicants of driving license in Iran. We administered a relevant set of commonly used EF tests, including a computerized Stroop test and six tests from the Delis-Kaplan Executive Function System (D-KEFS) and the Behavioral Assessment of the Dysexecutive Syndrome (BADS), to 87 healthy new drivers applying for a driver's license (Mage = 25.9 years, SD = 8.2; 43 female). We also administered a series of demographic and psychological questionnaires. The data regarding the participants' official driving tests were extracted from the official records. To determine the relations of the EF tests with success on the driving tests, several correlation and regression analyses were conducted. Most of the EF measures had significant correlations with the road test success, while having no significant relations with the theory test success. Moreover, in our regression analyses, The EF measures predicted success on the official driving road test, but not success on the driving theory test, even after controlling for the effects of previous unlicensed driving experience and stress symptoms. The results demonstrate the predictability of the driving road test success from executive functioning. [ABSTRACT FROM AUTHOR]

This paper focuses on the real-time obstacle avoidance and safe navigation of autonomous ground vehicles (AGVs). It introduces the Selective MPC-PF-PSO algorithm, which includes model predictive control (MPC), Artificial Potential Fields (APFs), and particle swarm optimization (PSO). This approach involves defining multiple sets of coefficients for adaptability to the surrounding environment. The simulation results demonstrate that the algorithm is appropriate for generating obstacle avoidance paths. The algorithm was implemented on the ROS platform using NVIDIA's Jetson Xavier, and driving experiments were conducted with a steer-type AGV. Through measurements of computation time and real obstacle avoidance experiments, it was shown to be practical in the real world. [ABSTRACT FROM AUTHOR]

Copyright of Lubrication Engineering (0254-0150) is the property of Editorial Office of LUBRICATION ENGINEERING and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to obtain the quantitative relationship between multisource data from road landscape and driving visual comfort in two‐lane mountainous road, we collected the driver's eye movement data and road environment data through real vehicle tests. The threshold segmentation method and the target landscape area ratio method are used to process the visual image, and the spatial enclosure degree is used to express the driver's sense of spatial closure. In addition, an HSV color model is established to characterize the driver's visual perception of the mountain highway landscape environment. Combining the target landscape area ratio of road landscape with the amount of color information, the calculation method of road landscape information is obtained. According to the characteristics of normal distribution of pupil area ratio, five grades of driver's visual comfort level are proposed, and the regression relationship between comfort level and landscape information is established. The study found that there is a significant negative correlation between the amount of road landscape information and the driver's visual comfort. When the amount of road landscape information is less than 0.338, the driving comfort is higher, which is conducive to driving safety. However, when the amount of landscape information in the road area exceeds 0.644, the driving comfort decreases significantly, which is detrimental to driving safety. [ABSTRACT FROM AUTHOR]

The paper describes the method of determination of exhaust emission characteristics from a vehicle engine based on the results obtained in a driving test simulated on an engine dynamometer. These characteristics are the relations between the specific distance emissions and the zero-dimensional characteristics of the process of vehicle velocity: the average velocity value and the average value of the absolute value of the product of vehicle velocity and acceleration. The exhaust emission characteristics are used to simulate the emissions from vehicles operating in different types of traffic conditions. The engine operating states in the engine dynamometer tests were determined by the operating conditions of the vehicle during the test. The authors applied the Monte Carlo method in order to determine the characteristics of different values of the zero-dimensional characteristics of the vehicle velocity process. This enabled the determination of the characteristics based on the test results from a single realization of the process of vehicle velocity. Additionally, the developed method allowed a replacement of the empirical research on the chassis dynamometer with the one performed on the engine dynamometer. The obtained exhaust emission characteristics are in line with the characteristics obtained on the chassis dynamometer in multiple tests. [ABSTRACT FROM AUTHOR]

Featured Application: Local path-planning simulation and experiment using potential field considering calculation time. Recently, various studies related to the development of unmanned vehicles have been conducted around the world. In particular, unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) have been developed and utilized for various purposes. In this study, we developed a method for the path generation of UGVs in a system in which one operator controls many different types of unmanned vehicles. In the driving control system (DCS), it is necessary to process sensor data such as GPS/INS and LiDAR when generating a path by receiving the target waypoint from the ground control station. In addition, the DCS must upload the current location, posture, state, etc., as well as save driving log. Therefore, in order to recognize obstacles in real time and generate a path, a safe path generation algorithm with a short computation time is required. Among the various path generation methods, the potential field algorithm was selected, and the algorithm was modified to reduce the computation time. The computation time before and after modification of the algorithm was obtained and compared through simulation, and the algorithm was verified through application to an actual system by performing an obstacle avoidance experiment and a simultaneous control experiment for two UGVs. [ABSTRACT FROM AUTHOR]

To solve the last mile problem in rural areas, we deal with compact ride‐sharing model. For reducing the computational cost, we use a simple GA method and compared the performance of the parameters. To simplify the problem, our algorithm use a simple linear distance and minimize the total traveling distance. A data set used for GA was based on the Inami area in Nanto City, Toyama Prefecture, that is a real case study region. The performance comparison experiment of the algorithm by changing parameters was carried out. Experimental results show that the algorithm is likely to give correct answers for up to four vehicles. It was also shown that the combination of population size and elite proportion for GA could reduce computational costs while ensuring accuracy. In addition, by ride‐sharing was actually carried out using the obtained experimental result, it was shown that it could be applied to the actual route even if the calculation was carried out in the linear distance. [ABSTRACT FROM AUTHOR]

The effects of nano-copper additives were investigated through a thermogravimetric (TG) test, diesel engine bench test, and road driving test. The tests demonstrated that the nano-copper additive presented a good catalytic effect on the combustion of soot particles, which reduced the complete combustion temperature from 580 to 520 °C. The diesel engine bench test showed that particulate matter (PM), CO, and HC emissions were reduced by 99.9%, 81.30%, and 33.33%, respectively. The NOx emissions slightly increased due to the lack of carbon as a reducing substance in the exhaust gas. The road driving test with a diesel vehicle demonstrated that more stringent emission standards can be met without any upgrade to the engine. [ABSTRACT FROM AUTHOR]

Mini vehicles with a small wheelbase are extremely sensitive to road roughness. The aim of this study is to explore the influence of road information on ride comfort and enhance the ride comfort of mini vehicles. According to the 5-degree-of-freedom vibration model of an electric mini off-road vehicle, the partial differential matrix equation of system motion is established using the Lagrange method, and the frequency response characteristic of the system is analyzed. The input matrix of pavement unevenness is obtained by considering the mutual power spectrum density between front and rear wheels. Road surface roughness information is obtained using an instrument for measuring road roughness. A comprehensive objective function and a constraint condition are established for comfort and safety. Based on the parameters obtained through the optimum design of a 1/4 vehicle model, the optimized stiffness and damping coefficient of suspension are obtained using a MATLAB optimization program. The law of the vibration performance of off-road vehicles with respect to suspension stiffness and the damping coefficient is obtained through the analysis of optimized results. A driving simulation and a test are conducted on the electric mini off-road vehicle. The results show that the use of the measured pavement data as simulation input is closer to the actual situation and provides higher accuracy compared to the simulated pavement model. According to the optimization and test results, the parameters optimized by a 1/2 vehicle (5 degrees of freedom) vibration model are better than those optimized by the 1/4 vehicle vibration model. The optimization results confirm reduction in acceleration, acceleration power spectrum density, and the root mean square of the weighted acceleration of the seat. This shows that the electric mini off-road vehicle provides better ride comfortability after optimization. [ABSTRACT FROM AUTHOR]

Featured Application: The applications of this work are related to the road safety analysis and the investigation of user behavior. In particular, the described method can be used as a support for the preventive analysis of road accidents. A driver's behavior and their psychophysical state are the most common causes of road accidents. The research presented in the paper proposes a method that allows the identification of highly dangerous road stretches/intersections in advance, based on the localization of stressful/relaxing situations measured on drivers. These were measured through the collection of physiological parameters using wearable devices. A correlation between stressful/relaxing situations and locations with high accident rates, based on a historical statistical database (black spots), was investigated. A series of driving tests was conducted in the city of Milan. The first set was mostly oriented to the research and validation of the parameters related to the driver's psychophysical state. Subsequent tests allowed the definition of a correlation between black spots and relaxing/stressful areas. The results showed that the most stressful areas for drivers fell mainly within those with high accident rates. Furthermore, 80% of the most dangerous zones of the route were identified using this method, thus confirming the validity of the approach as a support tool for a priori preventive analysis for road safety. The wearable devices allowed the study and the integration of specific elements relating to human behavior in the field of road safety, which typically involves a technical-engineering approach. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Theoretical Educational Science / Kuramsal Eğitimbilim Dergisi is the property of Afyon Kocatepe University, Faculty of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The disk cover is installed to protect brake disk and calliper and it's removed right before delivering to customers. The temperature of disk cover was measured driving test vehicles(2000cc, diesel) in this study. The highest temperature measured for the driving test(120km/h-braking(0.3G)-stop-120km/h-braking(0.5G)-stop) was 260~270℃ in the upper part of the disk cover and the temperature varied considerably around the disk cover. It can be inferred from this temperature distribution around the cover that the major heat transfer from hot disk to cover was through convection. In other words, the hot air generated by braking friction moved up to the upper part of the disk cover. And only the upper area of the disk cover was melted down during this driving test. The thickness of disk cover was increased to 1.0mm from 0.7mm and 1 paper of masking tape was pasted in the upper region of the disk cover. Then the cover endured the heated air formed by braking friction during the driving test. [ABSTRACT FROM AUTHOR]

Objective: This study separately examined the impact of the outcomes of a practical on-road driving test and a hazard perception test on the likelihood of traffic crashes among a cohort of newly licensed young drivers in New South Wales (NSW), Australia. Methods: The DRIVE study is a prospective cohort study of drivers aged 17 to 24 years holding their first-year provisional driver license in NSW. Information obtained from 20,822 participants in a detailed baseline questionnaire was linked to information on the number of attempts at a mandatory practical on-road driving test and hazard perception test as well as police-reported traffic crashes. Results: After controlling for a number of sociodemographic and behavioral factors as well as factors related to driver learning experiences, multivariate analysis showed that those who failed the practical on-road test at least 4 times had an increased risk of involvement in a traffic crash compared to those who passed the test at first attempt (relative risk [RR]: 1.79, 95% confidence interval [CI]: 1.20–2.65). The crash risk among those who failed the practical on-road test at least 4 times was particularly high in females (RR: 2.10, 95% CI: 1.20–3.68). Similarly, those who failed the hazard perception test at least twice had an increased risk of involvement in a traffic crash (RR: 1.83, 95% CI: 1.27–2.63) compared to those who passed the test on the first attempt. The crash risk of those who failed the hazard perception test at least twice was particularly high in males (RR: 2.5, 95% CI: 1.5–4.1) and among those from rural and remote areas (RR: 5.53, 95% CI: 1.63–18.71). Conclusions: The findings have implications on licensing practices and suggest the need for adequate strategies to assist young drivers with multiple failures in the driving and hazard perception tests. [ABSTRACT FROM AUTHOR]

The aim of this study was to compare and evaluate the production of exhaust emissions from a vehicle with a petrol engine with the Euro 4 emission standard and powered by petrol and LPG (liquefied petroleum gas). The paper presents new possibilities for monitoring exhaust emissions using an exhaust gas analyzer. At the same time, it points out the topicality and significance of the issue in the monitored area. It examines the impact of a change in fuel on emissions. This change is monitored in various areas of vehicle operation. Measurements were performed during real operation, which means that the results are fully usable and applicable in practice. The driving simulation as well as the test conditions correspond to the RDE (Real Driving Emissions) test standard. A commercially available car was first selected to perform the tests, which was first measured in the original configuration (petrol drive). Based on real-time RDE driving tests, it is possible to determine the number of exhaust emissions. Subsequently, the same measurements were performed with the same vehicle, but the vehicle's propulsion was changed to LPG. The vehicle was equipped with an additional system that allowed the vehicle to be powered by LPG. The results from the individual driving tests allowed the determination of the exhaust emissions. Emissions of CO (carbon monoxide), CO2 (carbon dioxide), HC (hydrocarbons), and NOx (nitrogen oxides) were monitored as a matter of priority. Through the driving tests, it was found that the gasoline combustion produced higher CO (1.926 g/km) and CO2 (217.693 g/km) emissions compared to the combustion of liquefied gas, where the concentration of the CO emissions was 1.892 g/km and that of the CO2 emissions was 213.966 g/km. In contrast, the HC (0.00397 g/km) and NOx (0.03107 g/km) emissions were lower when petrol was burned. During LPG combustion, the HC emissions reached 0.00430 g/km, and the NOx emissions reached 0.05134 g/km. At the end of the research, the authors compared the emissions determined by real driving (in g/km) with the emission values produced by the emission standard EURO 4 and the certificate of conformity (COC). Practical measurements showed that the vehicle produced excessive amounts of CO when burning gasoline. This production is 0.926 g/km higher and 0.892 g/km higher when burning LPG compared to the limit set by the Euro 4 Emission Standard. The difference is even greater than the limit value stated in the COC document. For other substances, the monitored values are in the norm and are even far below the permitted value [ABSTRACT FROM AUTHOR]

This article is considered relevant because: 1) car driving is an everyday and safety-critical task; 2) simulators are used to an increasing extent for driver training (related topics: training, virtual reality, human - machine interaction); 3) the article addresses relationships between performance in the simulator and driving test results-a relevant topic for those involved in driver training and the virtual reality industries; 4) this article provides new insights about individual differences in young drivers' behaviour. Simulators are being used to an increasing extent for driver training, allowing for the possibility of collecting objective data on driver proficiency under standardised conditions. However, relatively little is known about how learner drivers' simulator measures relate to on-road driving. This study proposes a theoretical framework that quantifies driver proficiency in terms of speed of task execution, violations and errors. This study investigated the relationships between these three measures of learner drivers' (n = 804) proficiency during initial simulation-based training and the result of the driving test on the road, occurring an average of 6 months later. A higher chance of passing the driving test the first time was associated with making fewer steering errors on the simulator and could be predicted in regression analysis with a correlation of 0.18. Additionally, in accordance with the theoretical framework, a shorter duration of on-road training corresponded with faster task execution, fewer violations and fewer steering errors (predictive correlation 0.45). It is recommended that researchers conduct more large-scale studies into the reliability and validity of simulator measures and on-road driving tests. [ABSTRACT FROM AUTHOR]

Background: Driving is a complex form of activity involving especially cognitive and psychomotor functions. These functions may be impaired by Parkinson's disease. The relation between Parkinson's disease and driving ability is still obscure and clinicians have to make decisions concerning the driving ability of their patients based on insufficient information. Until now no studies have compared different methods for evaluating the driving ability of patients with Parkinson's disease.Methods: The driving ability of 20 patients with idiopathic Parkinson's disease and 20 age and sex matched healthy control subjects was evaluated by a neurologist, psychologist, vocational rehabilitation counsellor, and driving instructor using a standard 10 point scale. The patients and controls also evaluated their own driving ability. Cognitive and psychomotor laboratory tests and a structured on road driving test were used for evaluating the subjects' driving ability.Results: The patients with Parkinson's disease performed worse than the controls both in the laboratory tests and in the driving test. There was a high correlation between the laboratory tests and driving test both in the patient group and in the control group. Disease indices were not associated with the driving test. The neurologist overestimated the ability of patients with Parkinson's disease to drive compared with the driving ability evaluated by the structured on road driving test and with the driving related laboratory tests. Patients themselves were not capable of evaluating their own ability reliably.Conclusion: Driving ability is greatly decreased in patients with even mild to moderate Parkinson's disease. The evaluation of patients' driving ability is very difficult to carry out without psychological and psychomotor tests and/or a driving test. [ABSTRACT FROM AUTHOR]

This study was conducted to develop a simulation model of a 120 kW class electric all-wheel-drive (AWD) tractor and verify the model by comparing the measurement and simulation results. The platform was developed based on the power transmission system, including batteries, electric motors, reducers, wheels, and a charging system composed of a generator, an AC/DC converter, and chargers on each axle. The data measurement system was installed on the platform, consisting of an analog (current) and a digital part (rotational speed of electric motors and voltage and SOC (state of charge) level of batteries) by a CAN (controller area network) bus. The axle torque was calculated using the current and torque curves of the electric motor. The simulation model was developed by 1D simulation software and used axle torque and vehicle velocity data to create the simulation conditions. To compare the results of the simulation, a driving test using the platform was performed at a ground speed of 10 km/h in off- and on-road conditions. The similarities between the results were analyzed using statistical software and we found no significant difference in axle torque data. The simulation model was considered to be highly reliable given the change rate and average value of the SOC level. Using the simulation model, the workable time of driving operation was estimated to be about six hours and the workable time of plow tillage was estimated to be about 2.4 h. The results showed that the capacity of the battery is slightly low for plow tillage. However, in future studies, the electric AWD tractor performance could be improved through battery optimization through simulation under various conditions. [ABSTRACT FROM AUTHOR]