With increasing vehicle automation, mitigating the performance degradation associated with non-driving-related tasks (NDRTs) is critical for safe transitions to manual control. This study investigated the impact of cognitive load from NDRTs on takeover and post-takeover driving performance in a challenging takeover scenario over a 5-min period. Twenty participants completed simulated drives involving low (resting) or high (two-back) cognitive load NDRTs, with and without amplitude-modulated haptic vibration applied to the lumbar region. NDRTs significantly delayed hands-on response time and impaired lateral control after takeover. Haptic stimulation attenuated these impairments, reducing lane departures and enhancing stability, demonstrating its role as a mutual-assistance mechanism in human-automation collaboration. Heart rate variability analysis showed that the NDRT-related workload persisted after takeover and was partially mitigated by vibration. NASA-TLX confirmed increased subjective workload from NDRTs, with vibration reducing perceived effort. The results support haptic stimulation as a collaborative aid in SAE Level 3 automation.

INTRODUCTION:Drowsiness is a significant cause of crashes in the various transport industries, including automotive, aviation, and rail. Our previous study investigated the differential induction of drowsiness in drivers caused by specific whole-body vibration (WBV) frequency ranges, with an amplitude of 0.2 m/s2 r.m.s. The present companion study investigates the effects of different vibration amplitudes on the induction of driver drowsiness. METHOD:This study includes two experiments. In the first, 15 participants engaged in six 1-hour sessions of simulated driving, experiencing WBV at frequencies of 0 Hz (no vibration), 1-4 Hz, 4-8 Hz, 8-16 Hz, 16-32 Hz, or 32-64 Hz, with an amplitude of 0.1 m/s2 r.m.s. In the second experiment, another group of 15 participants underwent three 1-hour sessions of simulated driving, exposed to WBV at frequencies of 0 Hz, 1-4 Hz, and 4-8 Hz, with an amplitude of 0.05 m/s2 r.m.s. RESULTS:As indicated by driving performance, reaction time evaluations, and subjective reports, the extent of drowsiness decreased with decreasing vibration amplitude. PRACTICAL APPLICATIONS:Based on the results of this and our previous paper, discrete contours for WBV-induced drowsiness were developed. These contours will assist the transport industry in reducing the incidence of vehicle crashes caused by drowsiness.

Crash safety barriers are essential safety measures on public roadways and in motorsports. The crashworthiness of these barriers is often predicted using the finite element (FE) method. However, employing the FE method for large-scale crash analysis is time-consuming and computationally expensive. This paper introduces a novel application of the soft-body physics simulation method for crashworthiness analysis, comparing it with the popular FE simulation method and physical crash tests. The study details the ‘bottom-up’ approach for developing a mass-spring model for soft-body simulation and the development of an FE model of the tyre barrier system used in Formula One racetracks. Results from both numerical methods are validated against physical test results by comparing the vehicle's peak acceleration, rebound velocity, maximum intrusion into the barrier, and CORA rating. Both methods demonstrated a close correlation with physical crash tests, predicting the peak g-force on a crash vehicle within 1 g accuracy and the maximum intrusion within 8 cm accuracy. While the FE method exhibited higher accuracy, the mass-spring model offered superior computational efficiency, making it particularly suitable for simulating a variety of crash configurations. The research concludes that although FE simulation remains a mainstay for crash simulations, soft-body physics simulation should not be overlooked because of its efficiency and versatility. This novel engineering application can significantly expedite the analysis of safety barriers for testing various crash scenarios, including different vehicle types, impact speeds, and angles.

Drivers will be free to engage in Non-Driving Related Tasks (NDRTs) during Level 3 conditionally automated driving. However, drivers may not be able to respond to takeover requests quickly and flawlessly if the level of mental workload invested in the NDRTs is non-optimal. Heart Rate Variability (HRV) has been reported to be a sensitive indicator of the mental workload during NDRT engagement, and some HRV parameters have been used in Machine Learning models that attempt to predict takeover performance. However, until now, the selection of HRV parameters has been ad hoc. The present study constructed an artificial intelligence model to conduct an unbiased evaluation of 35 HRV parameters for predicting takeover performance in various contexts. The model used performance data from 19 drivers collected under 3 NDRTs x 2 time intervals (6 conditions) in a driving simulator. The HRV parameters were ranked by the predictiveness of takeover performance, using two ground truths. The optimal data ranges for the nine most influential HRV parameters were identified, enabling the optimal level of mental workload during NDRT engagement to be inferred. The present study introduced four innovations: 1) the unbiased use of all HRV parameters; 2) treating each NDRT as a separate condition; 3) using SHAP analysis to identify the most influential parameters; 4) using SHAP analysis to identify the range of values for a given parameter that are associated with an optimal TOR. While previous researchers have focused on time-domain HRV parameters, this study demonstrated that frequency-domain and non-linear parameters offer comparable predictive power. Our novel approach optimises HRV parameters in an unbiased manner, enhancing the prediction of driver takeover performance and improving the development of driver monitoring and warning systems.

As the semi-automation of motor vehicles advances, the prevalence of multitasking and task switching while driving has increased. In the next phase, known as conditional automated driving (level 3 automation), drivers will be able to fully engage in distracting tasks, yet they must be prepared to promptly resume control of the vehicle and maintain safe driving if requested to by the vehicle. In such situations, the driver's ability to flawlessly switch between the distracting task and the driving task becomes vitally important. This narrative review discusses conditional automated driving within the framework of cognitive psychology concepts of attention and task switching. Delayed reaction time and deteriorated driving performance are attributed to cognitive overload and switch cost. Factors that contribute to driving switch cost are identified and categorized, and several road safety concerns are raised, including: i) switch cost may last for between 20 s to 5 min; ii) inexperienced drivers may be unable to adequately control the vehicle after resuming manual control; and iii) low- and high-intensity non-driving tasks have a greater impact on takeover performance. To minimise the risk to road safety, suggestions have been provided to vehicle manufacturers, road users and regulatory authorities.

Since driving while drowsy is a significant cause of vehicle accidents, road safety could be improved if more effective methods were available for improving driver alertness. The present paper investigated whether it is possible to improve alertness via a wearable device that applied somatosensory vibration to the driver's wrist. The vibration used modulation frequencies ranging from 12 Hz to 50 Hz and a carrier frequency of 250 Hz. Random on-and-off intervals and variable vibration amplitudes were used to minimise sensory adaptation. Fifteen participants undertook a sixty-minute simulated driving task that caused drowsiness. Karolinska Sleepiness Scale (KSS), Power Spectral Density (PSD) and Higuchi's Fractal Dimension (HFD) analyses of brainwave signals were used to identify variations in driver alertness. The vibration stimulus was found to significantly improve alertness when compared to a no-vibration condition. Participants experienced an immediate improvement in alertness that reached significance within 9 minutes and was then sustained at the level seen at the beginning of the experiment, indicating a full restoration of alertness. This result demonstrates that wearable vibration devices have the potential to improve alertness in drivers.

Driver drowsiness is a factor in at least 20% of serious motor vehicle accidents. Although research has shown that Whole-Body Vibration (WBV) can induce drowsiness in drivers, it is unknown whether particular frequencies are more problematic. The present study systematically investigated the influence of WBV frequency on driver drowsiness. Fifteen participants each undertook six 1-h sessions of simulated driving while being subjected to WBV of either 0 Hz (no vibration), 1-4 Hz, 4-8 Hz, 8-16 Hz, 16-32 Hz or 32-64 Hz. Subjective sleepiness, as measured by the Karolinska Sleepiness Scale (KSS), confirmed that drivers felt drowsier when exposed to the two lowest frequency ranges (1-4 Hz and 4-8 Hz). Reaction time, which measures attention and alertness, was significantly impaired by the two lowest frequency ranges. Objective driving performance measures (Standard Deviation of Lane Position (SDLP), Standard Deviation of (SD) Steering Angle, Time in Unsafe Zone) also showed significant degradation due to exposure to the two lowest frequency ranges. Exposure to 1-4 Hz or 4-8 Hz vibration caused attention to become significantly impaired within 15-20 min and driving performance to be significantly impaired by 30-35 min. The other frequency ranges had little or no effect. These findings point to a need to develop equivalent vibration-induced drowsiness contours that can be adopted as transportation safety standards.

RMIT University discovered that the reverberation time measurement module of new commercial hardware and software was not as fully automated as it should have been.The author wrote Visual Basic for Applications software that fully automated the new commercial software.CSIRO discovered that the same commercial hardware and software sometimes produced reverberation times that were too long due to the software deciding that the decay had started before the sound was turned off.It was discovered that one of the reasons why this occurred was that the firmware random noise generator produced the same random noise each time it was started, which made decay curve averaging useless.The author was able to convince the commercial supplier to fix this problem.Another reason was that the software sometimes produced undefined levels and these undefined levels sometimes caused the software to think that the decay had started before the sound was turned off.The commercial supplier was unable to fix this problem.RMIT University has had problems when using linear averaging to measure reverberation time.These errors and inadequacies are some of the reasons why CSIRO has started the development of its own signal processing software which is described in this paper.

CSIRO purchased an open application programming interface for hardware with an ethernet connection from a major acoustical instrument manufacturer. Using this interface, the author developed software for fractional octave band filtering with linear and exponential averaging. He then extended this software to automatically measure reverberation time. Fast Fourier Transform and Stepped Sine software was also developed. He then realized that CSIRO also owned two professional audio external USB sound cards. One of these had eight analogue inputs and ten analogue outputs. The other had two analogue inputs and two analogue outputs. Both could supply 48 V phantom power on their analogue inputs. It is possible to purchase phantom power preamplifiers for professional measurement microphones, but CSIRO decided to purchase phantom power to IEPE pre-amplifier adaptors. This enabled the use of professional pre-polarized microphones with IEPE microphone preamplifiers and accelerometers with built-in IEPE preamplifiers. CSIRO also had external USB hardware from another acoustical instrument manufacturer. Upon opening this instrument, it consisted of a professional audio external USB sound card and an IEPE front end for the two analogue input channels. The author decided to produce a version of his software that could be used with these three external USB sound cards.

Road accidents resulting from the loss of driving alertness cause significant social and economic damage. In recent years, studies have revealed that whole-body vibration affects driver drowsiness directly. However, the impact of different frequency ranges of vibration on driver alertness has not been extensively explored. This driving simulator study aimed to investigate the impact of low frequency (1-4 Hz) and high frequency (16-32 Hz) whole-body vibration on a driver's reaction time during a monotonous highway driving task. Nine participants completed the task for one hour, with reaction tasks presented every 8-12 seconds. After 25 minutes of exposure to whole-body vibration, participants exhibited significantly longer reaction times for both low and high frequency ranges when compared to the control condition. The low frequency range had a stronger effect on driver vigilance, with reaction times increasing by as much as 135 ms within 35 minutes. Subjective evaluations using the Karolinska Sleepiness Scale (KSS) were consistent with the reaction time results. Overall, this study confirms that both low and high frequency vibration can reduce driver alertness, with the effect of low frequency vibration being more pronounced. Further research is suggested with larger sample sizes and wider frequency ranges.

In conditionally automated driving, drivers are required to respond to takeover requests (TORs) and resume manual driving of the vehicle in situations where the conditionally automated driving systems are ineffective. Prior to TORs, the driver may be engaged in Non-Driving Related Tasks (NDRTs), and their manual driving performance after the takeover transition (post-automation) may require time to return to normal. This study investigated the influences of NDRTs on manual driving performance during the post-automation period. Seventeen volunteers participated in this driving simulator study. There were three NDRTs: writing business emails (working condition), watching videos (entertaining condition), and taking a break with eyes closed (resting condition). The duration of engagement in each NDRT before resuming manual driving was either 5 min (short interval) or 30 min (long interval). When TORs were made, drivers were given 10 s (TOR lead time) to switch from an NDRT to manual driving and then continue driving on a straight highway for 5 min. The results demonstrated that driving performance was impaired during the post-automation period. A significant detrimental effect on driving performance was observed for all three NDRT conditions and both task engagement durations. This effect was particularly evident for lane control, where drivers on average spent 4-8 s per minute outside of their lane for each of the five minutes following the TOR. These results indicate that driver engagement in other tasks, even for brief periods, can increase accident risk during the minutes following a TOR. Analysis of individual driving performance revealed a subset of 5 drivers who were strongly impaired, spending 10-25 s per minute outside of their lane throughout the post-automation period. These unsafe drivers could be accurately identified from their driving behaviour during the pre-automation (control) period. Surprisingly, participants were unaware that their degraded driving performance following the takeover. These findings extend the understanding of the disruptive effect of cognitive set-switching while driving and have important implications for the design and safety of autonomous vehicles.

Mechanical noise identification and classification are essential for automotive and machinery fault diag-nosis. The scarcity of labelled audio data for noise-related mechanical issues has constrained the utilisa-tion of complex, high-capacity machine learning models. In this research, the application of augmentation methods for labelled squeak and rattle datasets has proven that the accuracy of deep con-volutional neural networks can be improved. Data augmentation has eliminated common machine learn-ing issues such as overfitting observed in the models trained from a small dataset. The influence of different augmentation methods for the dataset was evaluated and compared based on classification accuracy. Different data augmentation methods have been tested to classify audio classes of different mechanical noises. The use of class-specific data augmentation leads to the development of more accu-rate machine learning models. Different combinations of data augmentations were investigated. This research showed that the new combined augmentation process could significantly improve the classi-fiers' accuracy. The proposed combined data augmentation technique achieved the highest precision with the lowest error rate for both squeak and rattle datasets. The proposed method can be applied to any type of mechanical noise identification and classification.(c) 2023 Elsevier Ltd. All rights reserved.

The current ISO attempt to revise ISO 354 has ended in failure because the experts on the working group could not reach agreement in the required time frame. However, several important issues were identified. Some of these issues are discussed in this paper. The current ISO 354:2003 changed from using 30 dB of decay to using 20 dB of decay to determine the reverberation time. Unfortunately, the conversion of the theoretical formulae for the spatial and ensemble standard deviation from 30 dB to 20 dB of decay was not correct. The current version of the standard incorrectly changed the maximum linear averaging time and changed from specifying the maximum exponential averaging time to specifying the maximum exponential time constant but did not halve the maximum value. There is also evidence that the maximum exponential averaging time can be increased by 50% without biasing the measured reverberation time. When a discrete decay curve is analyzed, there are issues when deciding the first and last point to use when determining the reverberation time. During the attempted revision of the standard, it was proposed to specify a minimum amount of empty room sound absorption. This did not reduce the spread of results in the round robin and this paper gives theoretical results which suggest that this is to be expected.

This study presents several methods to determine the characteristic impedance and the complex wave number of porous materials. These methods include the two-cavity method by Utsuno et al., the two-thickness method by Dunn and Davern, the four-microphone transfer-matrix method by Song and Bolton, the empirical method by Miki and theoretical calculation by the inverse method of Johnson-Champoux-Allard (JCA) model. Glass wool and polyester materials are used to compare different approaches. The results indicate a clear correlation among all methods. The theoretical calculation based on the inverse method of the JCA model can predict well without direct measurement of the non-acoustical parameters. However, there are disadvantages to several methods including sharp peaks in some frequency ranges for the two-cavity method, data fluctuation at low frequency for the four-microphone method, and inaccuracy in predicting the characteristic impedance of thick materials at middle and high frequencies for the two-thickness method. The results imply that the inverse method of the JCA model is a reliable method to predict the characteristic impedance and the complex wave number with comparable accuracy to traditional experimental methods.

This paper investigated the acoustic properties of a multi-layer vehicle interior trim acoustic material. In this study, a thin fiber layer was inserted between the vehicle carpet and the porous sound-absorbing material layer, and backed by the vehicle floor. The theoretical prediction method was based on the Johnson-Champoux-Allard (JCA) model, and the sound absorption coefficient of the multi-layer vehicle interior trim acoustic material was calculated by using the transfer matrix method (TMM). An impedance tube was used to measure the normal incidence sound absorption coefficient of the vehicle interior trim with different combinations of acoustical materials. The comparison of the theoretical and the experimental results showed that the sound absorption coefficient curves predicted using the JCA model are consistent with the measurement data across the mid and high-frequency range (500-6400 Hz). In addition, a car cabin Statistical Energy Analysis (SEA) model was developed and used to predict the effect of the acoustic properties of the multi-layer vehicle interior trim acoustic material. The results presented in this paper, are useful for enhancing vehicle interior acoustic design and refining vehicle cabin sound quality in future vehicles.

The sound absorption coefficient (SAC) of materials measured in a reverberation room is affected by both the intrinsic properties of the material and geometrical dimensions of the sample. A different size of the same material may produce a different SAC primarily due to the edge effect phenomenon. In this research, the experimental data from multiple laboratories was analyzed to evaluate the influence of the edge effect. An empirical function was established based on these measurement data and the linear relationship between the SAC and the relative edge length. Thomasson's method, the two geometric methods, and the analytical method were used to estimate the SAC of an absorber from measurements on a different size sample and compared with results obtained using the empirical function. The results show that the proposed empirical method is a reliable way to predict the SAC of a sample from measurements on a different size sample of the same material, which only requires the thickness, density, and size of the material.

Introduction: In conditionally automated driving, drivers are allowed to engage in non-driving related tasks (NDRTs) and are occasionally requested to take over vehicle control in situations that the automation system cannot handle. Drivers may not be able to adequately perform such requests if they have limited driving experience. This study investigates the influence of driving experience on takeover performance in conditionally automated driving. Method: Nineteen subjects participated in this driving simulator study. The NDRTs consisted of three tasks: writing business emails (working condition), watching videos (entertaining condition), and taking a break with eyes closed (resting condition). These three NDRTs require drivers to invest high, moderate, and low levels of mental workload, respectively. The duration of engagement in each NDRT before a takeover request (TOR) was either 5 minutes (short interval) or 30 minutes (long interval). Results: Drivers' driving experience and performance during the control period are highly correlated with their TOR performance. Furthermore, the type and duration of NDRT influence TOR performance, and inexperienced drivers exhibit poorer TOR performance than experienced drivers. Conclusions and Practical Applications: These findings have relevance for the types of NDRTs that ought to be permitted during automated driving, the design of automated driving systems, and the formulation of regulations regarding the responsible use of automated vehicles.

The sound absorption coefficient (SAC) of a composite multi-cell sound absorber in the low- and mid-frequency range is investigated by using experiment and numerical method. The composite sound absorber includes a MPP (micro-perforated panel) layer, a porous material layer, and an air cavity layer. The sandwich acoustic structure consists of an air cavity layer in between two MPP layers, which is backed by another air cavity layer. Maa’s model was used to describe the MPP layer, and the porous material layer was established by using Delany and Bazley’s model. The transfer matrix method (TMM) was used to calculate the surface impedance of each acoustic unit-cell of the composite multi-cell sound absorber, and the SAC of the composite multi-cell sound absorber was predicted by using the equivalent circuit method. Finite element (FE) models of the composite multi-cell sound absorbers are presented, and their sound absorption coefficient was measured by using an impedance tube method. The measurement data demonstrate the validity of the prediction results and are used to analyse various acoustic characteristics that depend on the structural parameters of each acoustic unit-cell. Furthermore, an optimal combination of the structural parameters of the composite multi-cell sound absorber can be realized by using the genetic algorithm (GA). The effect of the number of the acoustic unit-cells with different perforation ratios of the MPP layer and the depth of the air cavity layer is presented. They are the main design parameters that can control the SAC in different frequency ranges. The results also show that the SAC of the composite multi-cell sound absorber can be adjusted by increasing the number of the acoustic unit-cells and using the optimized design of the air cavity layer.

Introduction: Loss of attention leads to less steady driving within the lane and is one of the main causes of road accidents. To improve road safety, vehicle-based parameters such as steering wheel angle and lateral position are used to objectively assess driving performance, especially in monotonous driving tasks. Method: The present driving simulator study investigated the extent to which eight commonly used parameters are independent indicators of driving performance. Fifteen participants undertook a monotonous highway driving task for 1 h. Four steering angle parameters were examined: average steering angle (ASA), standard deviation of steering angle (SDSA), steering angle range (SAR), and steering reversal rate (SRR); as well as four lateral position parameters: mean lateral position (MLP), standard deviation of lateral position (SDLP), lateral position range (LPR), and the out-of-lane duration. Measurements were averaged across 2-minute epochs. Repeated measures correlation analysis evaluated the similarity between each parameter, and the variance inflation factor test evaluated the multicollinearity of all the parameters. Results: The results demonstrated that some parameters are highly correlated and should not be used together to assess driving performance. It is recommended that the optimal combination is ASA and SAR to assess steering angle, and SDLP and out-of-lane to assess lateral position. Out-of-lane, as a factor directly contributing to road safety, is recommended because it has the least correlation with other parameters. Practical Applications: If implemented, these recommendations may improve the assessment of driving performance in future studies.