The Effect of Time Window Length on EEG-Based Emotion Recognition.

Various lengths of time window have been used in feature extraction for electroencephalogram (EEG) signal processing in previous studies. However, the effect of time window length on feature extraction for the downstream tasks such as emotion recognition has not been well examined. To this end, we investigate the effect of different time window (TW) lengths on human emotion recognition to find the optimal TW length for extracting electroencephalogram (EEG) emotion signals. Both power spectral density (PSD) features and differential entropy (DE) features are used to evaluate the effectiveness of different TW lengths based on the SJTU emotion EEG dataset (SEED). Different lengths of TW are then processed with an EEG feature-processing approach, namely experiment-level batch normalization (ELBN). The processed features are used to perform emotion recognition tasks in the six classifiers, the results of which are then compared with the results without ELBN. The recognition accuracies indicate that a 2-s TW length has the best performance on emotion recognition and is the most suitable to be used in EEG feature extraction for emotion recognition. The deployment of ELBN in the 2-s TW can further improve the emotion recognition performances by 21.63% and 5.04% when using an SVM based on PSD and DE features, respectively. These results provide a solid reference for the selection of TW length in analyzing EEG signals for applications in intelligent systems.

Deep Learning Approaches on Defect Detection in High Resolution Aerial Images of Insulators.

By detecting the defect location in high-resolution insulator images collected by unmanned aerial vehicle (UAV) in various environments, the occurrence of power failure can be timely detected and the caused economic loss can be reduced. However, the accuracies of existing detection methods are greatly limited by the complex background interference and small target detection. To solve this problem, two deep learning methods based on Faster R-CNN (faster region-based convolutional neural network) are proposed in this paper, namely Exact R-CNN (exact region-based convolutional neural network) and CME-CNN (cascade the mask extraction and exact region-based convolutional neural network). Firstly, we proposed an Exact R-CNN based on a series of advanced techniques including FPN (feature pyramid network), cascade regression, and GIoU (generalized intersection over union). RoI Align (region of interest align) is introduced to replace RoI pooling (region of interest pooling) to address the misalignment problem, and the depthwise separable convolution and linear bottleneck are introduced to reduce the computational burden. Secondly, a new pipeline is innovatively proposed to improve the performance of insulator defect detection, namely CME-CNN. In our proposed CME-CNN, an insulator mask image is firstly generated to eliminate the complex background by using an encoder-decoder mask extraction network, and then the Exact R-CNN is used to detect the insulator defects. The experimental results show that our proposed method can effectively detect insulator defects, and its accuracy is better than the examined mainstream target detection algorithms.

Hybrid Path Planning Combining Potential Field with Sigmoid Curve for Autonomous Driving.

The traditional potential field-based path planning is likely to generate unexpected path by strictly following the minimum potential field, especially in the driving scenarios with multiple obstacles closely distributed. A hybrid path planning is proposed to avoid the unsatisfying path generation and to improve the performance of autonomous driving by combining the potential field with the sigmoid curve. The repulsive and attractive potential fields are redesigned by considering the safety and the feasibility. Based on the objective of the shortest path generation, the optimized trajectory is obtained to improve the vehicle stability and driving safety by considering the constraints of collision avoidance and vehicle dynamics. The effectiveness is examined by simulations in multiobstacle dynamic and static scenarios. The simulation results indicate that the proposed method shows better performance on vehicle stability and ride comfortability than that of the traditional potential field-based method in all the examined scenarios during the autonomous driving.

Efficient and robust estimation of single-vehicle crash severity: A mixed logit model with heterogeneity in means and variances.

This study delves into the factors that contribute to the severity of single-vehicle crashes, focusing on enhancing both computational speed and model robustness. Utilizing a mixed logit model with heterogeneity in means and variances, we offer a comprehensive understanding of the complexities surrounding crash severity. The analysis is grounded in a dataset of 39,788 crash records from the UK's STATS19 database, which includes variables such as road type, speed limits, and lighting conditions. A comparative evaluation of estimation methods, including pseudo-random, Halton, and scrambled and randomized Halton sequences, demonstrates the superior performance of the latter. Specifically, our estimation approach excels in goodness-of-fit, as measured by ρ2, and in minimizing the Akaike Information Criterion (AIC), all while optimizing computational resources like run time and memory usage. This strategic efficiency enables more thorough and credible analyses, rendering our model a robust tool for understanding crash severity. Policymakers and researchers will find this study valuable for crafting data-driven interventions aimed at reducing road crash severity.

Input modality matters: A comparison of touch, speech, and gesture based in-vehicle interaction.

Innovative input devices are being available for in-vehicle information systems (IVISs). While they have the potential to provide enjoyable driving by enabling drivers to perform non-driving related tasks (NDRTs) in more natural ways, the associated distracting effects should be paid with more attention. The purpose of this exploratory study was to compare the effects of three novel input modalities, i.e., touchscreen-based interaction (TBI), speech-based interaction (SBI), and gesture-based interaction (GBI), on driving performance and driver visual behaviors. Moreover, we examined if the influence of different modalities would be moderated by the difficulty level of NDRTs. A total of 36 participants were invited to a simulated driving experiment where they were randomly assigned to one of the four groups (TBI, GBI, SBI or baseline) and completed three driving trials. The results showed that TBI led to the worse driving performance, as indicated by the significantly prolonged reaction time, reduced minimum time-to-collision, and increased variations in both longitudinal and lateral vehicle control. The deteriorated driving performance could be attributed, at least partially, to the intense visual demand induced by looking towards the touchscreen, as indicated by more and longer off-the-road glances. The adverse impacts of GBI were relatively smaller, but it still posed great crash risk by leading to a shorter minimum time-to-collision and less stable vehicle control compared to the baseline. SBI, although not completely equivalent to the baseline group, showed the minimum influence on driving and visual performance. Only very few interaction effects were found, suggesting that the effects of modality were quite robust across different NDRTs. It was concluded that SBI and GBI provided safer alternatives to in-vehicle interaction than TBI.

Mitigating the impact of outliers in traffic crash analysis: A robust Bayesian regression approach with application to tunnel crash data.

Traffic crash datasets are often marred by the presence of anomalous data points, commonly referred to as outliers. These outliers can have a profound impact on the results obtained through the application of traditional methods such as logit and probit models, commonly used in the domain of traffic safety analysis, resulting in biased and unreliable estimates. To mitigate this issue, this study introduces a robust Bayesian regression approach, the robit model, which utilizes a heavy-tailed Student's t distribution to replace the link function of these thin-tailed distributions, effectively reducing the influence of outliers on the analysis. Furthermore, a sandwich algorithm based on data augmentation is proposed to enhance the estimation efficiency of posteriors. The proposed model is rigorously tested using a dataset of tunnel crashes, and the results demonstrate its efficiency, robustness, and superior performance compared to traditional methods. The study also reveals that several factors such as night and speeding have a significant impact on the injury severity of tunnel crashes. This research provides a comprehensive understanding of the outliers treatment methods in traffic safety studies and offers valuable recommendations for the development of appropriate countermeasures to effectively prevent severe injuries in tunnel crashes.

A multimodal psychological, physiological and behavioural dataset for human emotions in driving tasks.

Human emotions are integral to daily tasks, and driving is now a typical daily task. Creating a multi-modal human emotion dataset in driving tasks is an essential step in human emotion studies. we conducted three experiments to collect multimodal psychological, physiological and behavioural dataset for human emotions (PPB-Emo). In Experiment I, 27 participants were recruited, the in-depth interview method was employed to explore the driver's viewpoints on driving scenarios that induce different emotions. For Experiment II, 409 participants were recruited, a questionnaire survey was conducted to obtain driving scenarios information that induces human drivers to produce specific emotions, and the results were used as the basis for selecting video-audio stimulus materials. In Experiment III, 40 participants were recruited, and the psychological data and physiological data, as well as their behavioural data were collected of all participants in 280 times driving tasks. The PPB-Emo dataset will largely support the analysis of human emotion in driving tasks. Moreover, The PPB-Emo dataset will also benefit human emotion research in other daily tasks.

The efficacy of modified HuangLian JieDu decoction for early enteral nutrition in patients with sepsis: A randomized controlled study.

OBJECTIVE: This study aimed to evaluate the efficacy of modified HuangLian JieDu decoction (MHLJDD) as a supplementary medication for early enteral nutrition in septic patients. METHODS: This study was designed as a randomized controlled preliminary study. Septic patients were randomly divided into control (treated with the base treatment) and intervention (co-treated with MHLJDD and the base treatment) groups. The primary outcomes of this study were 60-day (d) mortality rate, length of mechanical ventilation (MV), and length of stay in the intensive care unit (ICU). RESULTS: Of the 86 included patients, 44 and 42 were allocated to the intervention and control groups, respectively. Lengths of MV and ICU stay were significantly shorter in the intervention group than in the control group (10.31 ±â€…3.92 d vs 8.66 ±â€…2.84 d, P = .028; and 11.88 ±â€…5.25 d vs 10.41 ±â€…3.14 d, P = .029; respectively). However, the difference in 60-d mortality rate between the 2 groups was not statistically significant (20.45% vs 38.10%, P = .071). The enteral-nutrition tolerance score of the control group was higher than that of the intervention group (6.81 ±â€…4.28 vs 4.68 ±â€…4.04, P = .020). Incidence of hyperglycemia and gastric retention (gastric residual volume > 250 mL) was higher in the control group than in the intervention group (59.52% vs 29.55%, P = .005; and 28.57% vs 11.36%, P = .020, respectively). CONCLUSIONS: MHLJDD can shorten the MV and ICU stay of septic patients.

Traffic Crash Characteristics in Shenzhen, China from 2014 to 2016.

Road traffic crashes cause fatalities and injuries of both drivers/passengers in vehicles and pedestrians outside, thus challenge public health especially in big cities in developing countries like China. Previous efforts mainly focus on a specific crash type or causation to examine the crash characteristics in China while lacking the characteristics of various crash types, factors, and the interplay between them. This study investigated the crash characteristics in Shenzhen, one of the biggest four cities in China, based on the police-reported crashes from 2014 to 2016. The descriptive characteristics were reported in detail with respect to each of the crash attributes. Based on the recorded crash locations, the land-use pattern was obtained as one of the attributes for each crash. Then, the relationship between the attributes in motor-vehicle-involved crashes was examined using the Bayesian network analysis. We revealed the distinct crash characteristics observed between the examined levels of each attribute, as well the interplay between the attributes. This study provides an insight into the crash characteristics in Shenzhen, which would help understand the driving behavior of Chinese drivers, identify the traffic safety problems, guide the research focuses on advanced driver assistance systems (ADASs) and traffic management countermeasures in China.

What drives people to use automated vehicles? A meta-analytic review.

Lack of consumer acceptance is a prominent barrier to the large-scale adoption of automated vehicles (AVs). This study investigated the underlying mechanisms for AV acceptance and how the mechanisms differed across subgroups by reviewing and synthesizing existing literature. We proposed AV acceptance models by extending the basic Technology Acceptance Model (TAM) with trust and perceived risk factors. Data from 36 studies were extracted to fit the models using meta-analytic structural equation modeling technique. The results suggested that trust contributed most in determining AV acceptance, followed by perceived usefulness and perceived risk, and perceived ease of use makes the least contribution. The subgroup analyses showed that the model parameters differed across the levels of three variables, i.e., sample origin (Europe/Asia/America), automation level (full/partial), and age (young/middle-aged). Specifically, trust was unanimously identified as the most important determinant of AV acceptance across all subgroups. Perceived risk only remained significant in America, fully AVs, and middle-aged subgroups. Perceived ease of use was insignificant in the above-mentioned three subgroups while remained significant in the rest subgroups. Building trust could be the most useful and universal way to improve AV acceptance, and policy makers should consider the characteristics of consumers when making AV promotion strategies.

Association between Crash Attributes and Drivers' Crash Involvement: A Study Based on Police-Reported Crash Data.

Understanding the association between crash attributes and drivers' crash involvement in different types of crashes can help figure out the causation of crashes. The aim of this study was to examine the involvement in different types of crashes for drivers from different age groups, by using the police-reported crash data from 2014 to 2016 in Shenzhen, China. A synthetic minority oversampling technique (SMOTE) together with edited nearest neighbors (ENN) were used to solve the data imbalance problem caused by the lack of crash records of older drivers. Logistic regression was utilized to estimate the probability of a certain type of crashes, and odds ratios that were calculated based on the logistic regression results were used to quantify the association between crash attributes and drivers' crash involvement in different types of crashes. Results showed that drivers' involvement patterns in different crash types were affected by different factors, and the involvement patterns differed among the examined age groups. Knowledge generated from the present study could help improve the development of countermeasures for driving safety enhancement.

Influence of traffic congestion on driver behavior in post-congestion driving.

Traffic congestion is more likely to lead to aggressive driving behavior that is associated with increased crash risks. Previous studies mainly focus on driving behavior during congestion when studying congestion effects. However, the negative effects of congestion on driving behavior may also affect drivers' post-congestion driving. To fill this research gap, this study examined the influence of traffic congestion on driver behavior on the post-congestion roads (i.e., the roads travelled right after congestion). Twenty-five subjects participated in a driving simulation study. They were asked to complete two trials corresponding to post-congestion and non-congestion conditions, respectively. Driver behavior quantified by driving performance measures, eye movement measures, and electroencephalogram (EEG) measures was compared between the two conditions. Ten features were selected from the measures with statistical significance. The selected features were integrated to characterize drivers' response patterns using a hierarchical clustering method. The results showed that driver behavior in post-congestion situations became more aggressive, more focused in the forward area but less focused in the dashboard area, and was associated with lower power of the ß-band in the temporal brain region. The clustering results showed more aggressive and lack-of-aware response patterns while driving in post-congestion situations. This study revealed that traffic congestion negatively affected driver behavior on the post-congestion roads. Practical implications for driving safety education was discussed based on the findings from the present study.

Drivers' visual scanning behavior at signalized and unsignalized intersections: A naturalistic driving study in China.

INTRODUCTION: Intersections are the most dangerous locations in urban traffic. The present study aims to investigate drivers' visual scanning behavior at signalized and unsignalized intersections. METHOD: Naturalistic driving data at 318 green phase signalized intersections and 300 unsignalized ones were collected. Drivers' glance allocations were manually categorized into 10 areas of interest (AOIs), based on which three feature subsets were extracted including glance allocation frequencies, durations and AOI transition probabilities. The extracted features at signalized and unsignalized intersections were compared. Features with statistical significances were integrated to characterize drivers' scanning patterns using the hierarchical clustering method. Andrews Curve was adopted to visually illustrate the clustering results of high-dimensional data. RESULTS: Results showed that drivers going straight across signalized intersections had more often glances at the left view mirror and longer fixation on the near left area. When turning left, drivers near signalized intersections had more frequent glances at the left view mirror, fixated much longer on the forward and rearview mirror area, and had higher transition probabilities from near left to far left. Compared with drivers' scanning patterns in left turning maneuver at signalized intersections, drivers with higher situation awareness levels would divide more attention to the forward and right areas than at unsignalized intersections. CONCLUSIONS: This study revealed that intersection types made differences on drivers' scanning behavior. Practical applications: These findings suggest that future applications in advanced driver assistance systems and driver training programs should recommend different scanning strategies to drivers at different types of intersections.

Effect of Low-Frequency Vibration on Muscle Response under Different Neurointact Conditions.

Stretch reflex is an important factor that influences the biomechanical response of the human body under whole-body vibration. However, there is a lack of quantitative evaluation at lower frequencies. Thus, the aim of this study was to investigate the effects of vibration on the stretch reflex and, in particular, to explore the quantitative relationship between dynamic muscle responses and low-frequency vibrations. The gastrocnemius muscle of 45 Sprague-Dawley rats was dissected. Sinusoidal vibrations of five discrete frequencies (2~16 Hz) with peak-to-peak amplitudes of 1 mm were applied to the gastrocnemius muscles with 2 mm or 3 mm prelengthening. Variables including dynamic muscle force, vibration acceleration, and displacement were recorded in two conditions, with and without the stretch reflex. Results showed that the dynamic muscle forces decreased by 20% on average for the 2 mm prelengthening group after the stretch reflex was blocked and by 24% for the 3 mm prelengthening group. Statistical analysis indicated that the amplitude of dynamic muscle force in the "with stretch reflex" condition was significantly larger than that in the "without stretch reflex" condition (p < 0.001). The tension-length curve was found to be a nonlinear hysteresis loop that changed with frequency. The phase difference between the dynamic muscle force and the length change was affected significantly by vibration frequency (p < 0.01), and the minimum frequency was 4-8 Hz. Experimental results of this study could benefit musculoskeletal model by providing a theoretical support to build a stretch reflex model for low-frequency vibration.

Driver braking behavior analysis to improve autonomous emergency braking systems in typical Chinese vehicle-bicycle conflicts.

Bicycling is one of the fundamental modes of transportation especially in developing countries. Because of the lack of effective protection for bicyclists, vehicle-bicycle (V-B) accident has become a primary contributor to traffic fatalities. Although AEB (Autonomous Emergency Braking) systems have been developed to avoid or mitigate collisions, they need to be further adapted in various conflict situations. This paper analyzes the driver's braking behavior in typical V-B conflicts of China to improve the performance of Bicyclist-AEB systems. Naturalistic driving data were collected, from which the top three scenarios of V-B accidents in China were extracted, including SCR (a bicycle crossing the road from right while a car is driving straight), SCL (a bicycle crossing the road from left while a car is driving straight) and SSR (a bicycle swerving in front of the car from right while a car is driving straight). For safety and data reliability, a driving simulator was employed to reconstruct these three scenarios and some 25 licensed drivers were recruited for braking behavior analysis. Results revealed that driver's braking behavior was significantly influenced by V-B conflict types. Pre-decelerating behaviors were found in SCL and SSR conflicts, whereas in SCR the subjects were less vigilant. The brake reaction time and brake severity in lateral V-B conflicts (SCR and SCL) was shorter and higher than that in longitudinal conflicts (SSR). The findings improve their applications in the Bicyclist-AEB and test protocol enactment to enhance the performance of Bicyclist-AEB systems in mixed traffic situations especially for developing countries.