The use of navigation systems in naturalistic driving

Objective: In this study, we assessed the use of portable navigation systems in everyday driving by applying in-vehicle naturalistic driving.Method: Experienced users of navigation systems, 7 females and 14 males, were provided with a specially equipped vehicle for approximately 1 month. Their trips were recorded using 4 cameras, Global Positioning System (GPS) data, and other sensor data. The drivers' navigation system use data were coded from the video recordings, which showed how often and for how long the system was activated and how often and for how long a driver operated the system.Results: The system was activated for 23% of trips, predominantly on longer and unique trips. Analyses of the percentage of time for which the speed limit was exceeded showed no evidence of differences between trips for which the navigation system was used or not used. On trips for which the navigation system was activated, participants spent about 5% of trip time interacting with the device. About 40% of interacting behavior took place in the first 10% of the trip time, and about 35% took place while the car was standing still or moving at a very low speed; that is, 0-10km/h.Conclusion: These results shed light on how and when drivers use navigation systems. They suggest that although drivers regulate their use of such systems to some extent, they often perform risky tasks while driving.</p

The potential of naturalistic driving studies with simple data acquisition systems (DAS) for monitoring driver behaviour

This report addresses the important question regarding the potential of simple and low-cost technologies to address research questions such as the ones dealt with in UDrive. The resources and efforts associated with big naturalistic studies, such as the American SHRP II and the European UDrive, are tremendous and can not be repeated and supported frequently, or even more than once in a decade (or a life time..). Naturally, the wealth and richness of the integrated data, gathered by such substantial studies and elaborated DAS, cannot be compared to data collected via simpler, nomadic data collection technologies. The question that needs to be asked is how many Research Questions (RQs) can be addressed, at least to some extent, by other low-cost and simple technologies? This discussion is important, not only in order to replace the honourable place (and cost!) of naturalistic studies, but also to complement and enable their continuity after their completion. Technology is rapidly evolving and almost any attempt to provide a comprehensive and complete state of the art of existing technologies (as well as their features and cost) is doomed to fail. Hence, in chapter 1 of this report, we have created a framework for presentation, on which the various important parameters associated with the question at hand, are illustrated, positioned and discussed. This framework is denoted by “Framework for Naturalistic Studies” (FNS) and serves as the back bone of this report. The framework is a conceptual framework and hence, is flexible in the sense that its dimensions, categories and presentation mode are not rigid and can be adjusted to new features and new technologies as they become available. The framework is gradually built using two main dimensions: data collection technology type and sample size. The categories and features of the main dimensions are not rigidly fixed, and their values can be ordinal, quantitative or qualitative. When referring to parameters that are not numerical –even the order relation among categories is not always clear. In this way –the FNS can be, at times, viewed as a matrix rather than a figure with order relation among categories presented along its axes. On the two main dimensions of the FNS –data collection technology type and sample size –other dimensions are incorporated. These dimensions include: cost, data access, specific technologies and research questions that can be addressed by the various technologies. These other dimensions are mapped and positioned in the plot area of the FNS. Other presentations, in which the axes and the plot area are interchanged, or 3 -dimensional presentations are performed, can be incorporated to highlight specific angles of the involved dimensions. The various technologies for data collection were mapped on the FNS. The technology groups include: mobile phone location services, mobile phone applications, telematics devices, built -in data loggers, dash cameras and enhanced dash cameras, wearable technologies, compound systems, eye trackers and Mobileyetype technologies. After this detailed illustrations of analyses that can be conducted using simple low-cost technologies are described. It is demonstrated how temporal and spatial analysis can reveal important aspects on the behavioural patterns of risky drivers. Also one stand alone smartphone app can be used to monitor and evaluate smartphone us age while driving. Most of the simple systems relate to specific behaviour that is monitored (i.e. speeding , lane keeping etc.). Additionally, certain thresholds or triggers are used to single out risky situations, which are related to that behaviour. However, once those instances are detected, no information on the circumstances leading or accompanying this behaviour are available. Typically, visual information (discrete or preferably continuous) is needed in order to fully understand the circumstances. Hence, upgrading simple (single-task oriented) technologies by other technologies (most typically by cameras), can significantly improve researchers' ability to obtain information on the circumstances, which accompany the detected risky behaviour. One of the most conceptually straightforward integrated systems is a system, for which the basic technology detects the desired behaviour (e.g. harsh braking) and triggers a simple continuous dashboard camera to save the relevant information, which occurs together with that behaviour. Many RQs can be addressed using this type of combined systems

Recommendations for a large-scale European naturalistic driving observation study. PROLOGUE Deliverable D4.1.

Naturalistic driving observation is a relatively new research method using advanced technology for in-vehicle unobtrusive recording of driver (or rider) behaviour during ordinary driving in traffic. This method yields unprecedented knowledge primarily related to road safety, but also to environmentally friendly driving/riding and to traffic management. Distraction, inattention and sleepiness are examples of important safety-related topics where naturalistic driving is expected to provide great added value compared to traditional research methods. In order to exploit the full benefits of the naturalistic driving approach it is recommended to carry out a large-scale European naturalistic driving study. The EU project PROLOGUE has investigated the feasibility and value of carrying out such a study, and the present deliverable summarises recommendations based on the PROLOGUE project

Recommendations for safety and sustainability measures of the EU FP7 Project UDRIVE

The aim of Task 5.1 is to identify and select, among the outcomes of SP4, the results that are relevant to infer recommendations for measures improving road safety and sustainability. Due to time constraint, the analyses and the recommendations have been done in less time that it was planned at the beginning of the project. The key outcomes of the SP4 work with particular reference to crash risk, unsafe driving, and eco-driving will be studied and organized in terms of relevance to safety and sustainability policies and potential actions towards road users, vehicle and road. Recommendations have been developed to propose actions to stakeholders that can be implemented in the near future to increase safety and sustainability of road transport. This work integrates several reviews of different measures implemented previously in France, Germany, Netherlands and United Kingdom in terms of road safety measures. Then, the recommendations consider possible updates of existing measures and the development of new measures. They will include four kinds of areas: • Recommendations in terms of regulation and enforcement measures; • Recommendations for awareness campaigns and training; • Recommendations for design of road infrastructure; • Recommendations for vehicle safety. Looking at road fatalities statistics, we have identified vulnerable road users as a topic which is important to create recommendations for. We have also identified factors that can have an influence on fatality occurrence like age and infrastructure. A report by the World Health Organization in 2015 (WHO, 2015) identified some area’s wherein there is a need for recommendations to improve road safety. We have selected from the by WHO recommended topics, 3 topics which could be explored by naturalistic studies: seat belt, speed, distraction. Another topic that we are looking into is critical situations. The difficulties with investigating critical situations with road fatalities data bases, is that these databases often do not provide fully detailed information about the dynamic of the accident. Naturalistic studies have the ability to explore incidents more in-depth. Another objective of UDRIVE is to improve sustainability by looking into eco-driving. We will look at recommendations for this topic in this report as well

The study design of UDRIVE: the Naturalistic Driving Study across Europe for cars, trucks and scooters

Purpose: UDRIVE is the first large-scale European Naturalistic Driving Study on cars, trucks and powered two wheelers. The acronym stands for "European naturalistic Driving and Riding for Infrastructure & Vehicle safety and Environment". The purpose of the study is to gain a better understanding of what happens on the road in everyday traffic situations. Methods: The paper describes Naturalistic Driving Studies, a method which provides insight into the actual real-world behaviour of road users, unaffected by experimental conditions and related biases. Naturalistic driving can be defined as a study undertaken to provide insight into driver behaviour during everyday trips by recording details of the driver, the vehicle and the surroundings through unobtrusive data gathering equipment and without experimental control. Data collection will take place in six EU Member States. Results: Road User Behaviour will be studied with a focus on both safety and environment. The UDRIVE project follows the steps of the FESTA-V methodology, which was originally designed for Field Operational Tests. Conclusions: Defining research questions forms the basis of the study design and the specification of the recording equipment. Both will be described in this paper. Although the project has just started collecting data from drivers, we consider the process of designing the study as a major result which may help other initiatives to set up similar studies

An early Phase II randomised controlled trial testing the effect on persecutory delusions of using CBT to reduce negative cognitions about the self: the potential benefits of enhancing self confidence

Background Research has shown that paranoia may directly build on negative ideas about the self. Feeling inferior can lead to ideas of vulnerability. The clinical prediction is that decreasing negative self cognitions will reduce paranoia. Method Thirty patients with persistent persecutory delusions were randomised to receive brief CBT in addition to standard care or to standard care (ISRCTN06118265). The six session intervention was designed to decrease negative, and increase positive, self cognitions. Assessments at baseline, 8 weeks (posttreatment) and 12 weeks were carried out by a rater blind to allocation. The primary outcomes were posttreatment scores for negative self beliefs and paranoia. Secondary outcomes were psychological well-being, positive beliefs about the self, persecutory delusions, social comparison, self-esteem, anxiety, and depression. Results Trial recruitment and retention were feasible and the intervention highly acceptable to the patients. All patients provided follow-up data. Posttreatment there was a small reduction in negative self beliefs (Cohen's d = 0.24) and a moderate reduction in paranoia (d = 0.59), but these were not statistically significant. There were statistically significant improvements in psychological well-being (d = 1.16), positive beliefs about the self (d = 1.00), negative social comparison (d = 0.88), self-esteem (d = 0.62), and depression (d = 0.68). No improvements were maintained. No adverse events were associated with the intervention. Conclusions The intervention produced short-term gains consistent with the prediction that improving cognitions about the self will reduce persecutory delusions. The improvement in psychological well-being is important in its own right. We recommend that the different elements of the intervention are tested separately and that the treatment is lengthened

Heart Rate Analysis for Human Factors: Development and Validation of an Open Source Toolkit for Noisy Naturalistic Heart Rate Data

Heart rate data are collected often in human factors studies. Advances in open hardware platforms and offtheshelf photoplethysmogram (PPG) sensors allow the nonintrusive collection of heart rate data at very low cost. However, the signal is not trivial to analyse, since the morphology of PPG waveforms differs from electrocardiogram (ECG) waveforms and shows different noise patterns. PPG is often preferable because it can be collected less intrusively. However, few validated open source available algorithms exist that handle PPG data well, as most of these algorithms are specifically designed for ECG data. We have developed a novel algorithm specifically for PPG data collected in noisy fieldor simulatorbased settings. The main aim of this paper is to present the validation of a novel algorithm on a PPG dataset collected in a recent driving simulator experiment. The dataset was manually annotated, and performance of the algorithm compared to two other popular open source available algorithms. We show that the algorithm performs well and displays superior performance on the PPG dataset. Implications and further steps are discussed