Computer vision and driver distraction: developing a behaviour-flagging protocol for naturalistic driving data

Application of naturalistic driving data: A systematic review and bibliometric analysis

The application of naturalistic driving data (NDD) has the potential to answer critical research questions in the area of driving behavior assessment, as well as the impact of exogenous and endogenous factors on driver safety. However, the presence of a large number of research domains and analysis foci makes a systematic review of NDD applications challenging in terms of information density and complexity. While previous research has focused on the execution of naturalistic driving studies and on specific analysis techniques, a multifaceted aggregation of NDD applications in Intelligent Transportation System (ITS) research is still unavailable. In spite of the current body of work being regularly updated with new findings, evolutionary nuances in this field remain relatively unknown. To address these deficits, the evolutionary trend of NDD applications was assessed using research performance analysis and science mapping. Subsequently, a systematic review was conducted using the keywords "naturalistic driving data" and "naturalistic driving study data". As a result, a set of 393 papers, Published between January 2002-March 2022, was thematically clustered based on the most common application areas utilizing NDD. the results highlighted the relationship between the most crucial research domains in ITS, where NDD had been incorporated, and application areas, modeling objectives, and analysis techniques involving naturalistic databases.

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

Determining Anxiety in Obsessive Compulsive Disorder through Behavioural Clustering and Variations in Repetition Intensity

BACKGROUND AND OBJECTIVES

Over the last decade, the application of computer vision techniques to the analysis of behavioural patterns has seen a considerable increase in research interest. One such interesting and recent application is the visual behavioural analysis of mental disorders. Despite the very recent surge in interest in this area, relatively little has been done thus far to assist individuals living with Obsessive Compulsive Disorder. The work proposed herein represents a proof of concept system designed to demonstrate the efficacy of such an approach, from the computational perspective. The specific focus of this work lies in demonstrating a mechanism for clustering different kinds of Obsessive Compulsive Disorder behaviours and then comparing new behaviours to existing behaviours to determine the approximate level of anxiety represented by a compulsive behaviour.

METHODS

The proposed system uses Temporal Motion Heat Maps, SURF descriptors, a visual bag of words model and SVM-based classification to categorise representations of various behaviours commonly seen in OCD. Moreover, we apply a set of statistical measures to the images in a given category in order to derive an approximate anxiety level for a given compulsive behaviour. This proof of concept is an essential step in the direction of integrating computational approaches into the treatment of psychiatric conditions such as Obsessive Compulsive Disorder, for more effective recovery.

RESULTS

Results gleaned from experimental simulations indicate that the proposed system is capable of correctly classifying different types of simulated Obsessive Compulsive Disorder behaviour classes 75% of the time, with the misclassifications almost exclusively occurring when two behavioural clusters appear highly similar. Based on this information the proposed system is then able to assign an approximate behavioural anxiety level to the compulsive behaviours that meets the approval of a mental health professional.

CONCLUSIONS

The proposed system demonstrates a good ability to categorise various types of simulated OCD behaviour, in addition to establishing an approximate anxiety level for a given compulsive behaviour. This research demonstrates strong potential for the use of such systems in assisting mental health professionals looking to better understand their patients' condition and treatment progress across time.

Can child restraint product information developed using consumer testing sustain correct use 6 months after child restraint purchase? Study protocol for a cluster randomised controlled trial

BACKGROUND

With long-standing and widespread high rates of errors in child restraint use, there is a need to identify effective methods to address this problem. Information supplied with products at the point of sale may be a potentially efficient delivery point for such a countermeasure. The aim of this study is to establish whether product materials developed using a consumer-driven approach reduce errors in restraint use among purchasers of new child restraint systems.

METHODS

A cluster randomised controlled trial (cRCT) will be conducted. Retail stores (n=22) in the greater Sydney area will be randomised into intervention sites (n=11) and control sites (n=11), stratified by geographical and socioeconomic indicators. Participants (n=836) will enter the study on purchase of a restraint. Outcome measures are errors in installation of the restraint as observed by a trained researcher during a 6-month follow-up home assessment, and adjustment checks made by the parent when the child is placed into the restraint (observed using naturalistic methods). Process evaluation measures will also be collected during the home visit. An intention-to-treat approach will be used for all analyses. Correct use and adjustment checks made by the parent will be compared between control and intervention groups using a logistic regression model. The number of installation errors between groups will be compared using Poisson regression.

DISCUSSION

This cRCT will determine the effectiveness of targeted, consumer-driven information on actual error rates in use of restraints. More broadly, it may provide a best practice model for developing safety product information.

TRIAL REGISTRATION NUMBER

ACTRN12617001252303p; Pre-results.

Modeling Driving Performance Using In-Vehicle Speech Data From a Naturalistic Driving Study

OBJECTIVE

We aimed to (a) describe the development and application of an automated approach for processing in-vehicle speech data from a naturalistic driving study (NDS), (b) examine the influence of child passenger presence on driving performance, and (c) model this relationship using in-vehicle speech data.

BACKGROUND

Parent drivers frequently engage in child-related secondary behaviors, but the impact on driving performance is unknown. Applying automated speech-processing techniques to NDS audio data would facilitate the analysis of in-vehicle driver-child interactions and their influence on driving performance.

METHOD

Speech activity detection and speaker diarization algorithms were applied to audio data from a Melbourne-based NDS involving 42 families. Multilevel models were developed to evaluate the effect of speech activity and the presence of child passengers on driving performance.

RESULTS

Speech activity was significantly associated with velocity and steering angle variability. Child passenger presence alone was not associated with changes in driving performance. However, speech activity in the presence of two child passengers was associated with the most variability in driving performance.

CONCLUSION

The effects of in-vehicle speech on driving performance in the presence of child passengers appear to be heterogeneous, and multiple factors may need to be considered in evaluating their impact. This goal can potentially be achieved within large-scale NDS through the automated processing of observational data, including speech.

APPLICATION

Speech-processing algorithms enable new perspectives on driving performance to be gained from existing NDS data, and variables that were once labor-intensive to process can be readily utilized in future research.