Vigilance monitoring for operator safety: A simulation study on highway driving

How drivers perform under different scenarios: Ability-related driving style extraction for large-scale dataset

The extraction and analysis of driving style are essential for a comprehensive understanding of human driving behaviours. Most existing studies rely on subjective questionnaires and specific experiments, posing challenges in accurately capturing authentic characteristics of group drivers in naturalistic driving scenarios. As scenario-oriented naturalistic driving data collected by advanced sensors becomes increasingly available, the application of data-driven methods allows for a exhaustive analysis of driving styles across multiple drivers. Following a theoretical differentiation of driving ability, driving performance, and driving style with essential clarifications, this paper proposes a quantitative determination method grounded in large-scale naturalistic driving data. Initially, this paper defines and derives driving ability and driving performance through trajectory optimisation modelling considering various cost indicators. Subsequently, this paper proposes an objective driving style extraction method grounded in the Gaussian mixture model. In the experimental phase, this study employs the proposed framework to extract both driving abilities and performances from the Waymo motion dataset, subsequently determining driving styles. This determination is accomplished through the establishment of quantifiable statistical distributions designed to mirror data characteristics. Furthermore, the paper investigates the distinctions between driving styles in different scenarios, utilising the Jensen-Shannon divergence and the Wilcoxon rank-sum test. The empirical findings substantiate correlations between driving styles and specific scenarios, encompassing both congestion and non-congestion as well as intersection and non-intersection scenarios.

A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents

Sleep-deprived fatigued person is likely to commit more errors that may even prove to be fatal. Thus, it is necessary to recognize this fatigue. The novelty of the proposed research work for the detection of this fatigue is that it is nonintrusive and based on multimodal feature fusion. In the proposed methodology, fatigue is detected by obtaining features from four domains: visual images, thermal images, keystroke dynamics, and voice features. In the proposed methodology, the samples of a volunteer (subject) are obtained from all four domains for feature extraction, and empirical weights are assigned to the four different domains. Young, healthy volunteers (n = 60) between the age group of 20 to 30 years participated in the experimental study. Further, they abstained from the consumption of alcohol, caffeine, or other drugs impacting their sleep pattern during the study. Through this multimodal technique, appropriate weights are given to the features obtained from the four domains. The results are compared with k-nearest neighbors (kNN), support vector machines (SVM), random tree, random forest, and multilayer perceptron classifiers. The proposed nonintrusive technique has obtained an average detection accuracy of 93.33% in 3-fold cross-validation.

Modeling the Impact of Driving Styles on Crash Severity Level Using SHRP 2 Naturalistic Driving Data

Previous studies have examined driving styles and how they are associated with crash risks relying on self-report questionnaires to categorize respondents based on pre-defined driving styles. Naturalistic driving studies provide a unique opportunity to examine this relationship differently. The current study aimed to study how driving styles, derived from real-road driving, may relate to crash severity. To study the relationship, this study retrieved safety critical events (SCEs) from the SHRP 2 database and adopted joint modelling of the number of the aggregated crash severity levels (crash vs. non-crash) using the Diagonal Inflated Bivariate Poisson (DIBP) model. Variables examined included driving styles and various driver characteristics. Among driving styles examined, styles of maintenance of lower speeds and more adaptive responses to driving conditions were associated with fewer crashes given an SCE occurred. Longer driving experiences, more miles driven last year, and being female also reduced the number of crashes. Interestingly, older drivers were associated with both an increased number of crashes and increased number of non-crash SCEs. Future work may leverage more variables from the SHRP 2 database and widen the scope to examine different traffic conditions for a more complete picture of driving styles.

Take-Over Requests after Waking in Autonomous Vehicles

Autonomous vehicles (AVs) enable drivers to devote their primary attention to non-driving-related tasks (NDRTs). Consequently, AVs must provide intelligibility services appropriate to drivers’ in-situ states and in-car activities to ensure driver safety, and accounting for the type of NDRT being performed can result in higher intelligibility. We discovered that sleeping is drivers’ most preferred NDRT, and this could also result in a critical scenario when a take-over request (TOR) occurs. In this study, we designed TOR situations where drivers are woken from sleep in a high-fidelity AV simulator with motion systems, aiming to examine how drivers react to a TOR provided with our experimental conditions. We investigated how driving performance, perceived task workload, AV acceptance, and physiological responses in a TOR vary according to two factors: (1) feedforward timings and (2) presentation modalities. The results showed that when awakened by a TOR alert delivered >10 s prior to an event, drivers were more focused on the driving context and were unlikely to be influenced by TOR modality, whereas TOR alerts delivered <5 s prior needed a visual accompaniment to quickly inform drivers of on-road situations. This study furthers understanding of how a driver’s cognitive and physical demands interact with TOR situations at the moment of waking from sleep and designs effective interventions for intelligibility services to best comply with safety and driver experience in AVs.

Predicting Fatigue in Long Duration Mountain Events with a Single Sensor and Deep Learning Model

AIM

To determine whether an AI model and single sensor measuring acceleration and ECG could model cognitive and physical fatigue for a self-paced trail run.

METHODS

A field-based protocol of continuous fatigue repeated hourly induced physical (~45 min) and cognitive (~10 min) fatigue on one healthy participant. The physical load was a 3.8 km, 200 m vertical gain, trail run, with acceleration and electrocardiogram (ECG) data collected using a single sensor. Cognitive load was a Multi Attribute Test Battery (MATB) and separate assessment battery included the Finger Tap Test (FTT), Stroop, Trail Making A and B, Spatial Memory, Paced Visual Serial Addition Test (PVSAT), and a vertical jump. A fatigue prediction model was implemented using a Convolutional Neural Network (CNN).

RESULTS

When the fatigue test battery results were compared for sensitivity to the protocol load, FTT right hand (R² 0.71) and Jump Height (R² 0.78) were the most sensitive while the other tests were less sensitive (R² values Stroop 0.49, Trail Making A 0.29, Trail Making B 0.05, PVSAT 0.03, spatial memory 0.003). The best prediction results were achieved with a rolling average of 200 predictions (102.4 s), during set activity types, mean absolute error for 'walk up' (MAE200 12.5%), and range of absolute error for 'run down' (RAE200 16.7%).

CONCLUSIONS

We were able to measure cognitive and physical fatigue using a single wearable sensor during a practical field protocol, including contextual factors in conjunction with a neural network model. This research has practical application to fatigue research in the field.

Vehicle driver drowsiness detection method using wearable EEG based on convolution neural network

Vehicle drivers driving cars under the situation of drowsiness can cause serious traffic accidents. In this paper, a vehicle driver drowsiness detection method using wearable electroencephalographic (EEG) based on convolution neural network (CNN) is proposed. The presented method consists of three parts: data collection using wearable EEG, vehicle driver drowsiness detection and the early warning strategy. Firstly, a wearable brain computer interface (BCI) is used to monitor and collect the EEG signals in the simulation environment of drowsy driving and awake driving. Secondly, the neural networks with Inception module and modified AlexNet module are trained to classify the EEG signals. Finally, the early warning strategy module will function and it will sound an alarm if the vehicle driver is judged as drowsy. The method was tested on driving EEG data from simulated drowsy driving. The results show that using neural network with Inception module reached 95.59% classification accuracy based on one second time window samples and using modified AlexNet module reached 94.68%. The simulation and test results demonstrate the feasibility of the proposed drowsiness detection method for vehicle driving safety.

A Review of Research on Driving Styles and Road Safety

OBJECTIVE

The aim of this study was to outline a conceptual framework for understanding driving style and, on this basis, review the state-of-the-art research on driving styles in relation to road safety.

BACKGROUND

Previous research has indicated a relationship between the driving styles adopted by drivers and their crash involvement. However, a comprehensive literature review of driving style research is lacking.

METHOD

A systematic literature search was conducted, including empirical, theoretical, and methodological research, on driving styles related to road safety.

RESULTS

A conceptual framework was proposed whereby driving styles are viewed in terms of driving habits established as a result of individual dispositions as well as social norms and cultural values. Moreover, a general scheme for categorizing and operationalizing driving styles was suggested. On this basis, existing literature on driving styles and indicators was reviewed. Links between driving styles and road safety were identified and individual and sociocultural factors influencing driving style were reviewed.

CONCLUSION

Existing studies have addressed a wide variety of driving styles, and there is an acute need for a unifying conceptual framework in order to synthesize these results and make useful generalizations. There is a considerable potential for increasing road safety by means of behavior modification. Naturalistic driving observations represent particularly promising approaches to future research on driving styles.

APPLICATION

Knowledge about driving styles can be applied in programs for modifying driver behavior and in the context of usage-based insurance. It may also be used as a means for driver identification and for the development of driver assistance systems.

Visual vigilance in drivers with obstructive sleep apnea

OBJECTIVE

To determine the effects of obstructive sleep apnea (OSA) on visual vigilance during simulated automobile driving.

METHODS

Twenty-five drivers with OSA and 41 comparison drivers participated in an hour-long drive in a high-fidelity driving simulator. Drivers responded to light targets flashed at seven locations across the forward horizon. Dependent measures were percent correct [hit rate (HR)] and reaction time (RT). Self-assessment of sleepiness used the Stanford Sleepiness Scale (SSS) before and after the drive and the Epworth Sleepiness Scale (ESS).

RESULTS

OSA drivers showed reduced vigilance based on lower HR than comparison drivers, especially for peripheral targets (80.7+/-14.8% vs. 86.7+/-8.8%, P=.03). OSA drivers were sleepier at the end of the drive than comparison drivers (SSS=4.2+/-1.2 vs. 3.6+/-1.2, P=.03), and increased sleepiness correlated with decreased HR only in those with OSA (r=-0.49, P=.01). Lower HR and higher post-drive SSS predicted greater numbers of driving errors in all subjects. Yet, ESS, predrive SSS, and most objective measures of disease severity failed to predict driving and vigilance performance in OSA.

CONCLUSIONS

Reduced vigilance for peripheral visual targets indicates that OSA drivers have restriction of their effective field of view, which may partly explain their increased crash risk. This fatigue-related decline in attention is predicted by increased subjective sleepiness during driving. These findings may suggest a means of identifying and counseling high-risk drivers and aid in the development of in-vehicle alerting and warning devices.

Predicting driver drowsiness using vehicle measures: recent insights and future challenges

INTRODUCTION

Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.

METHOD

The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.

RESULTS

Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.

CONCLUSIONS

Further research is necessary to examine more complex functions, as well as individual differences between drivers.

IMPACT ON INDUSTRY

A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.