Search

The validity of driving simulators is a topic that has been addressed ever since their first use. In practice, a comparison of field data and simulator data is often used for validation purposes. A systematic literature search was conducted which identified large heterogeneity in both study designs and statistical methods used in research on simulator validity. Most of the included studies concluded simulator validity (thus equivalence of field data and simulator data) from non-significant results in Null-Hypothesis Significance Testing (NHST), which is a well-known yet somewhat controversial practice. In this work, the consequences of both study design choice and the choice of the statistical method are outlined and alternatives such as using Bayes factor hypothesis testing and equivalence tests are discussed and applied to a real data example. The present paper is intended to shed light on the influence of the statistical method on conclusions drawn from experimental data. For research on driving simulator validity, we highly encourage the use of equivalence tests or Bayes factor hypothesis tests, in particular when evidence of the data suggests that an effect might be absent. [ABSTRACT FROM AUTHOR]

This paper examines the feasibility of incorporating visual cueing systems within vehicles to mitigate the risk of experiencing motion sickness. The objective is to enhance passenger awareness and the ability to anticipate the forces associated with car travel motion. Through a comprehensive literature review, the findings demonstrate that visual cues can mitigate motion sickness for particular in-vehicle configurations, whereas their influence on situational awareness is not clear yet. Each type of visual cue proved more effective when presented in the peripheral field of view rather than solely in the central vision. Promising applications can be found within interactive screens and ambient lighting, while the use of extended reality shows potential for future investigations. In addition, integrating such systems into highly automated vehicles shows potential to improve their overall user acceptance. This paper focuses on the integration of visual cues in vehicles to alleviate motion sickness. The work provides an overview of visual cueing strategies and different types of systems for display. The findings suggest that incorporating visual cues in highly automated vehicles can reduce motion sickness. Recommendations are provided for the design and integration of these displays in vehicle interiors. [ABSTRACT FROM AUTHOR]

Emotion is an important factor that can lead to the occurrence of aggressive driving. This paper proposes an association rule mining-based method for analysing contributing factors associated with aggressive driving behaviour among online car-hailing drivers. We collected drivers' emotion data in real time in a natural driving setting. The findings show that 29 of the top 50 association rules for aggressive driving are related to emotions, revealing a strong relationship between driver emotions and aggressive driving behaviour. The emotions of anger, surprised, happy and disgusted are frequently associated with aggressive driving behaviour. Negative emotions combined with other factors (for example, driving at high speeds and high acceleration rates and with no passengers in the vehicle) are more likely to lead to aggressive driving behaviour than negative emotions alone. The results of this study provide practical implications for the supervision and training of car-hailing drivers. PRACTITIONER SUMMARY: Based on the association rule mining method, we found a close connection between drivers' emotional states and the manifestation of aggressive driving behaviours. The findings indicate that the combination of negative emotions and various contributing factors significantly amplifies the likelihood of aggressive driving. [ABSTRACT FROM AUTHOR]

Involuntary autobiographical memories (IAMs) have been typically studied with paper diaries, kept for a week or longer. However, such studies are unable to capture the true frequency of IAMs, nor the level of detail that would give new insights into the mechanisms of IAMs. To address this gap, a new audio-recording method was developed and tested on the first author who recorded 674 IAMs while driving a car on a 30–40-minute-long habitual route on 20 occasions. Results revealed very high frequency of IAMs (almost 34 per journey) that were reported more often in response to dynamic (one-off) than static cues. Moreover, a substantial number of memory chains and long-term priming of IAMs by previously encountered incidental stimuli were also recorded. Based on these results, a new theoretical model is proposed in which the occurrence of IAMs is determined by an interplay of factors at the time of the IAM, such as the type of ongoing activity and internal or external triggers, as well as different types of long-term priming. The results also have practical implications for studying mind-wandering and safety issues in driving and aircraft-flying, where periods of concentration are followed by monotony and less demanding tasks. [ABSTRACT FROM AUTHOR]

The role of traditional analysis methods in improving complex socio-technical system safety has reached a ceiling, and thus systems theory has been utilised to support the investigations and countermeasures for road traffic accidents. As two widely applied systems accident analysis models, STAMP (systems theoretic accident model and process) and HFACS (human factors analysis and classification system) have their own advantages in accident analysis and safety improvement. Therefore, this study develops a new hybrid systems method integrating STAMP and HFACS for road traffic accident (SH-RTA), which can adopt HFACS to enhance the identification and analysis ability of STAMP for human factors and employ control concepts and elements of STAMP to cement the characteristic of HFACS. To illustrate the applicability of the hybrid method, a case study of '9·22' major road traffic accident in China is thoroughly analysed. Finally, preventive countermeasures and suggestions are presented. Practitioner Summary: This paper proposes a new hybrid systems method integrating STAMP and HFACS for road traffic accident. The new method reveals dysfunctional interactions within the parallel level and across levels, and identifies additional human and organisational factors. The recommendations for preventing road traffic accident are provided from higher levels of system. [ABSTRACT FROM AUTHOR]

Road transport is experiencing disruptive change from new first-of-a-kind technologies. While such technologies offer safety and operational benefits, they also pose new risks. It is critical to proactively identify risks during the design, development and testing of new technologies. The Systems Theoretic Accident Model and Processes (STAMP) method analyses the dynamic structure in place to manage safety risks. This study applied STAMP to develop a control structure model for emerging technologies in the Australian road transport system and identified control gaps. The control structure shows the actors responsible for managing risks associated with first-of-a-kind technologies and the existing control and feedback mechanisms. Gaps identified related to controls (e.g. legislation) and feedback mechanisms (e.g. monitoring for behavioural adaptation). The study provides an example of how STAMP can be used to identify control structure gaps requiring attention to support the safe introduction of new technologies. This paper considers emerging risks associated with new technologies in the road transport system. It demonstrates a novel approach using STAMP to identify gaps in control and feedback mechanisms within the existing control structure which should be addressed to mitigate risk. [ABSTRACT FROM AUTHOR]

Patients with right hemisphere damage (RHD) may exhibit mild unilateral spatial neglect (USN), which is difficult to detect in general assessments performed during driving rehabilitation. We compared driving simulator performance, practical driving performance, and neuropsychological test results between patients with RHD who were able and unable to return to driving to predict driving fitness based on driving simulator performance. This unmatched case-control study included 29 patients with RHD who were able (return-to-driving group, n = 16) and unable (non-return-to-driving group, n = 13) to return to driving. Patient demographics, motor function, attention, driving simulator performance (participants' reaction time and rate to green lamps appearing in any of the three displays and average lane position), and practical driving performance were compared between the groups. Receiver operating characteristic (ROC) analysis was performed to examine the predictive performance of driving fitness in reaction rate and paper-and-pencil tests. The non-return-to-driving group had a significantly lower reaction rate than the return-to-driving group (p = 0.027; 95% confidence interval [CI], 0–7; r = 0.407). No significant difference in reaction time or lane position in either the left or right lane during driving simulation was observed. ROC analysis of the reaction rate in the driving simulator task showed sensitivity of 0.692, specificity of 0.812, and area under the curve of 0.738 [95% CI, 0.541–0.935]. Decreases in reaction rates during simulated driving assessments are associated with an inability to resume driving in patients with RHD. Such assessments may aid in predicting fitness for driving in these patients. [ABSTRACT FROM AUTHOR]

Driver death rates per million registered vehicles are often used to compare the real-world crash experiences of vehicles of different types and sizes. However, these rates are affected by the risky behavior of drivers (e.g., speeding, impaired driving, lack of restraint use) that differs by age and gender. This paper presents a method for adjusting driver death rates to account for differences in driver age and gender. Driver death rates per million registered vehicles per year were calculated for passenger vehicles of model year 2020. To account for potential differences in driver exposure by age and gender, which can affect motor vehicle crash and injury experience, an algebraic method was used to standardize these rates to a common distribution of female drivers ages 25–64. The standardization depends upon an independent estimate of the relative fatality risk for female drivers ages 25–64. The smallest vehicles tended to have higher driver death rates compared with larger vehicles. However, for sports cars, this trend was reversed; larger sports cars had higher driver death rates. Vehicle types popular with male drivers, such as sports cars and pickups, had standardized death rates that were much lower than the raw rates. The adjustment for driver age and gender greatly reduced the variability of driver death rates among vehicle types. The procedure used here to adjust driver death rates for driver age and gender can be extended to any situation where it is desired to compare the crash rates of two or more subgroups of a population. [ABSTRACT FROM AUTHOR]

The purpose of this study is to investigate the change in body dimensions over time in both Western (US) and Eastern (Korea) populations. In order to analyse the change of body dimension between the past and present and between western and eastern population, 13 body dimensions relating to automobile driver seat design were extracted from the ANSUR and Size Korea datasets at two time points, the past (ANSUR I: 1988, Size Korea: 1992) and the present (ANSUR II: 2012, Size Korea: 2012). Most of the dimensions differed significantly between past and present, as well as between the US and Korea. Overall, the data show an increasing trend of body dimensions over time for both genders. Based on the results, all countries should be encouraged to conduct periodic and national anthropometric research because body dimensions are continuously changing over time worldwide. Practitioner summary: This paper describes a study that investigates the changes in body dimensions over time in Western (US) and Eastern (Korean) populations. Findings indicate increasing trends in most dimensions for both populations, crucial for user-friendly product design and preventing hazards caused by faulty products. [ABSTRACT FROM AUTHOR]

Objectives. This study examines which of the heart rate variability (HRV) and morphologic variability (MV) metrics may have the highest accuracy in different stress detection during real-world driving. Methods. The cross-sectional study was carried out among 93 intercity mini-bus male drivers aged 22–67 years. The Trillium 5000 Holter Recorder and GARMIN Virb Elite camera were used to determine heart rate and vehicle speed measurements along the path, respectively. We considered the HRV and MV metrics of electrocardiogram (ECG) signals including the mean RR interval (mRR), mean heart rate (mHR), normalized low-frequency spectrum (nLF), normalized high-frequency spectrum (nHF), normalized very low-frequency spectrum (nVLF), difference of normalized low-frequency spectrum and normalized high-frequency spectrum (dLFHF), and sympathovagal balance index (SVI). Results. The analysis showed that the HRV metrics mHR, mRR, nVLF, nLF, nHF, dLFHF and SVI are effective in mental stress detection while driving as compared to rest time. We obtained a high accuracy of stress detection for MV metrics as compared to the traditional HRV analysis, of approximately 92%. Conclusions. Our findings indicate that driver stress could be detected with an accuracy of 92% using MV metrics as an accurate physiological index of the driver's state. [ABSTRACT FROM AUTHOR]

This study aims to identify the non-value-adding activities during vehicle stowage operations in automobile terminals and propose a connected automated vehicle (CAV) loading system, a self-driving-car-loading system. Furthermore, the productivity of the CAV loading system is compared with the current loading system. A simulation model of an actual loading system was developed using the software FlexSim. The simulation results showed that the walking time of workers, operation time of shuttle vans, and waiting time occupied a large part of the cycle time in the current operation system. The proposed CAV loading system has eliminated these inefficiencies, and increased productivity by 26.78%. This is the first study to (1) present a self-driving-car-loading system in a simulated automobile terminal of a real-world size, and (2) propose a CAV loading system. Results provide useful insights for the integration of self-driving technology into future automobile port operations. [ABSTRACT FROM AUTHOR]

The article introduces a novel approach for designing a DC-to-DC converter utilizing the single-ended primary inductor converter topology for wide conversion operation in Electric Automobiles (E-Automobiles), with a focus on soft switching operation. A maximum power point tracking approach based on hierarchical multi-heuristic chicken swarm optimization is used to optimize the performance of the solar. The output of the converter is then fed into a three-phase inverter, where pulse width modulation is generated using a fuzzy logic controller (FLC). The inverted output is filtered using a low-pass filter before being supplied to the load, which is a brushless direct current motor in this case. The performance of the system is evaluated using traditional proportional-integral-derivative controllers and FLC controllers, considering parameters such as maximum amplitude (rise time), the time at which the system achieves its peak value (peak time), the time delay between input and output (delay time), the amplitude of the output voltage and current (output voltage and output current), and the discrepancy between the desired and actual values in a stable state (steady-state error). Comparative analysis indicates that the proposed design incorporating an FLC controller yields superior results compared to other approaches. [ABSTRACT FROM AUTHOR]

The safety of increasingly automated vehicles is of great concern to regulators, yet crash rates are generally reported by manufacturers using proprietary metrics with limited source data. Without consistent definitions of crashes and exposure, automated vehicle crash rates cannot be meaningfully compared with baseline datasets. The objective of this study was to establish methods to normalize automated vehicle crash rates using one manufacturer's crash reports as a case study. The manufacturer's quarterly crash rates for vehicles using SAE Level 1 and Level 2 automation were compared. Road type was controlled for using data from a naturalistic driving study with the same model vehicles, while driver age was controlled for using demographic ownership surveys. Although Level 2 vehicles were claimed to have a 43% lower crash rate than Level 1 vehicles, their improvement was only 10% after controlling for different rates of freeway driving. Direct comparison with general public driving was impossible due to unclear crash severity thresholds in the manufacturer's reports, but analysis showed that controlling for driver age would increase reported crash rates by 11%. These results establish the need for detailed crash data, crash definitions, and exposure and demographic data in order to accurately assess automated vehicle safety. [ABSTRACT FROM AUTHOR]