Evaluating the Safety Implications and Benefits of an In-Vehicle Data Recorder to Young Drivers

Young drivers in Israel, as in other parts of the world, are involved in car crashes more than any other age group. Green Light for Life is a new program that seeks to improve the quality of the experience of young drivers during the mandatory accompanied driving period. As part of the efforts to evaluate the effectiveness of this program a novel experiment, which uses information gathered from an in-vehicle data recorder (IVDR) is conducted. The DriveDiagnostics IVDR system, which is used in this study, can identify over 20 different maneuver types in raw measurements and use this information to indicate overall trip safety. Drivers receive feedback through various summary reports, real-time text messages or an in-vehicle display unit. Preliminary validation tests with the system demonstrate promising potential. In the experiment, the DriveDiagnostics system is installed in the primary vehicle driven by the young driver in 120 families. The experiment is designed to test the impact on driving behavior of participation in the program and the type of feedback drivers receive from the system. The data collection part of the experiment is scheduled to run for 8 months for each family

Modeling the Behavior of Novice Young Drivers Using Data from In- Vehicle Data Recorders

Novice young drivers suffer from increased crash risk that translates into over-representation in road injuries. A better understanding of the driving behavior of novice young drivers and of their determinants is needed to tackle this problem. To this extent, this study analyzes the behavior of novice young drivers within a Graduated Driver Licensing (GDL) program. Data on driving behavior of novice drivers and their parents is collected using in-vehicle data recorders, which calculate compound risk indices as measures of the risk taking behavior of the various drivers. Data is used to estimate a negative binomial model to identify the major factors that affect the driving behavior of the young drivers. Estimation results suggest that the risk taking behavior of young drivers is influenced by that of their parents and decreases with higher levels of supervised driving and stricter monitoring by the parents

Integrating Kinematic- and Vision-Based Information to Better Understand Driving Behaviour

This study explored the use of two types of advanced driver assistance systems (ADAS) as tools for observing driving behavior. The first was a kinematic-based ADAS that uses speed and acceleration data to detect driving events such as hard braking, speeding and sharp turning. The second was a visionbased ADAS that uses video data to provide lane departure warnings (LDW), headway warnings (HW) and forward collision warnings (FCW). Data was collected for more than 4,500 trips and 2,200 driving hours during a period of 70 days. The sample consisted of 10 drivers that used both types of ADAS simultaneously. The information collected also included more than 17,000 records of various types of driving events. First, the events rates were estimated by the Poisson and the Poisson-lognormal models. Then, Pearson correlation and factor analysis were implemented to study the relationships among the events and to evaluate whether different types of events converged to describe the same behaviors. Significant correlations were observed between the braking and turning kinematic-based events and the FCW vision-based event, which converged under the same factor. High rates of these events may indicate that the person is driving in an urban style. The LDW, HW and speeding events converged to the second factor, which is more relevant in inter-urban areas. These findings, although based on a small-scale study, point to a potential for the use of commercial ADAS for driving behavior analysis. The integration of kinematic-based and vision-based information can provide deeper understanding of the measured behavior

Evaluating Changes in the Driving Behavior of Young Drivers a Few Years After Licensure Using In-Vehicle Data Recorders

This paper aims to evaluate how young drivers drive a few years after licensure. Driving behavior in the fourth year of driving is compared to that of the first year, based on data from In-Vehicle Data Recorders (IVDR). Young drivers\u27 cars were equipped with the same IVDR systems in both study periods. The comparison revealed that, in general, driving patterns did not change significantly. The difference in risky behaviour between weekdays and weekends was more prominent in the fourth year than in the first year. In addition, an interesting improvement occurred at the end of the fourth-year study period. The analysis results obtained should also be considered an example of the potential of what may be done with this kind of data

The Potential for IVDR Feedback and Parental Guidance to Improve Novice Young Drivers’ Behavior

Young male drivers are well known for their increased involvement in road crashes when moving to the independent driving phase. This study examines the potential of IVDR (In-Vehicle Data Recorder) systems, which provide feedback on driving performances, and parental monitoring to restrain young male drivers’ aggressive driving behavior. The IVDR system was installed in the family car of young drivers for a period of 12 months, starting in the accompanied driving phase and continuing to the first nine months of independent driving. The system documents events based on measurements of extreme G-forces in the vehicles. 242 families of young male drivers participated in the study. They were randomly allocated into 4 groups: (1) FFNG- Family Feedback No Guidance- all members of the family were exposed to feedback on their own driving behavior and that of the other family members; (2) FFPG- Family Feedback Parental Guidance - similar to the previous group with the addition of personal guidance given to parents on ways to enhance their involvement and monitoring of their sons’ driving; (3) IFNG- Individual Feedback No Guidance- each driver received feedback only on his own driving behavior; (4) CNTL- a control group that received no feedback or parental guidance. The collected data from the IVDR was analyzed and the results indicate substantial benefits to drivers in the FFPG group in which parents received personal guidance to enhance their parental involvement and feedback on their son’s driving behavior, compared to the CNTL group which did not receive any feedback

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

Exploring Digitalization of Animal-Assisted Reading

Animal-assisted reading is a form of animal-assisted activity where children interact with and read aloud to specially trained therapy dogs. Such activities have been shown to have positive impact on literacy skills, reading motivation and sense of empowerment and self-esteem in children. Mobile technologies and facets of engagement in digital reading are increasingly studied in the context of child learning and education. This short paper presents some first results of our ongoing study exploring potential ways in which mobile technology can be used to enhance and support animal-assisted reading activities for children

Towards a large scale European Naturalistic Driving study: final report of PROLOGUE: deliverable D4.2

Naturalistic Driving (ND) studies represent the state of the art in traffic safety research and can be defined as studies undertaken to provide insight into driver behaviour during every day trips by recording details of the driver, the vehicle and the surroundings through unobtrusive data gathering equipment and without experimental control. Typically, in an ND study passenger cars, preferably the subjects' own cars, are equipped with several small cameras and sensors. For several months to several years, these devices continuously and inconspicuously register vehicle manoeuvres (like speed, acceleration/deceleration, direction), driver behaviour (like eye, head and hand manoeuvres), and external conditions (like road, traffic and weather characteristics). Thus, the ND approach allows us to observe and analyse the interrelationship between driver, vehicle, road and other traffic in normal situations, in conflict situations and in actual crashes. This type of information is not just useful for reducing road transport casualties, but also for reducing the environmental burden of road transport, and for reducing congestion. ND studies are not limited to passenger cars since vans and trucks can also be studied in a naturalistic way. Similarly, motorcycles can be equipped: naturalistic riding. The specific problems of pedestrians and cyclists can be studied based on observations from the vehicle. However for this application, naturalistic site-based observations can be a useful addition

Modeling route choice behavior in the presence of information using concepts from fuzzy set theory and approximate reasoning

Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Civil Engineering, 1992.Includes bibliographical references (leaves 167-171).by Tsippy Lotan.Sc.D