Warning reliability and driver performance in naturalistic driving

The goal of this study is to examine how driver behavior is influenced by the reliability of an in-vehicle warning system under naturalistic driving conditions. Driver responses to lateral drift warnings were examined to determine if the latency to initiate a corrective steering response was related to prior system reliability. An operational definition of subjective reliability was developed based on measures of the proportion of lateral drift warnings that the drivers responded to in the 24-hour period preceding a warning event that elicited a steering correction (called the day proportion). Age, gender, weather condition, light level, road class, and day proportion were used to model response latency to make a steering correction in a linear model. A main effect of day proportion was found, suggesting that reaction time to respond decreases by about 300 msecs as the day proportion increases from 0 to 1. The study illustrates a method of quantifying subjective reliability and performance using naturalistic data. The results suggest that latency to make a steering correction is inversely related to the perceived reliability of the warning system in the 24 hours preceding the warning.University of Michigan Industry Affiliation Program for Human Factors in Transportation Safetyhttp://deepblue.lib.umich.edu/bitstream/2027.42/58732/1/100248.pd

Glance behaviours when using an in-vehicle smart driving aid : a real-world, on-road driving study

In-vehicle information systems (IVIS) are commonplace in modern vehicles, from the initial satellite navigation and in-car infotainment systems, to the more recent driving related Smartphone applications. Investigating how drivers interact with such systems when driving is key to understanding what factors need to be considered in order to minimise distraction and workload issues while maintaining the benefits they provide. This study investigates the glance behaviours of drivers, assessed from video data, when using a smart driving Smartphone application (providing both eco-driving and safety feedback in real-time) in an on-road study over an extended period of time. Findings presented in this paper show that using the in-vehicle smart driving aid during real-world driving resulted in the drivers spending an average of 4.3% of their time looking at the system, at an average of 0.43 s per glance, with no glances of greater than 2 s, and accounting for 11.3% of the total glances made. This allocation of visual resource could be considered to be taken from ‘spare’ glances, defined by this study as to the road, but off-centre. Importantly glances to the mirrors, driving equipment and to the centre of the road did not reduce with the introduction of the IVIS in comparison to a control condition. In conclusion an ergonomically designed in-vehicle smart driving system providing feedback to the driver via an integrated and adaptive interface does not lead to visual distraction, with the task being integrated into normal driving

Data collection, analysis methods and equipment for naturalistic studies and requirements for the different application areas. PROLOGUE Deliverable D2.1

Naturalistic driving observation is a relatively new method for studying road safety issues, a method by which one can objectively observe various driver- and accident related behaviour. Typically, participants get their own vehicles equipped with some sort of data logging device that can record various driving behaviours such as speed, braking, lane keeping/variations, acceleration, deceleration etc., as well as one or more video cameras. In this way normal drivers are observed in their normal driving context while driving their own vehicles. Optimally, this allows for observation of the driver, vehicle, road and traffic environments and interaction between these factors. The main objective of PROLOGUE is to demonstrate the usefulness, value, and feasibility of conducting naturalistic driving observation studies in a European context in order to investigate traffic safety of road users, as well as other traffic related issues such as eco-driving and traffic flow/traffic management. The current deliverable aims to develop an inventory of the current and appropriate data collection and data analysis equipment for naturalistic observation studies together with a theoretical analysis of the requirements for different application areas. The deliverable also discusses data quality issues and top level data base management requirements. Among the reviewed literature, maximal use is made of the extensive knowledge and experience that comes from the EU projects FESTA and EuroFOT, the 100car study and the SHRP2 preparatory safety

Reliability prediction for the vehicles equipped with advanced driver assistance systems (ADAS) and passive safety systems (PSS)

The human error has been reported as a major root cause in road accidents in today’s world. The human as a driver in road vehicles composed of human, mechanical and electrical components is constantly exposed to changing surroundings (e.g., road conditions, environment)which deteriorate the driver’s capacities leading to a potential accident. The auto industries and transportation authorities have realized that similar to other complex and safety sensitive transportation systems, the road vehicles need to rely on both advanced technologies (i.e., Advanced Driver Assistance Systems (ADAS)) and Passive Safety Systems (PSS) (e.g.,, seatbelts, airbags) in order to mitigate the risk of accidents and casualties. In this study, the advantages and disadvantages of ADAS as active safety systems as well as passive safety systems in road vehicles have been discussed. Also, this study proposes models that analyze the interactions between human as a driver and ADAS Warning and Crash Avoidance Systems and PSS in the design of vehicles. Thereafter, the mathematical models have been developed to make reliability prediction at any given time on the road transportation for vehicles equipped with ADAS and PSS. Finally, the implications of this study in the improvement of vehicle designs and prevention of casualties are discussed

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

Driver Distraction: A Naturalistic Observation of Secondary Behaviors With the Use of Driver Assistance Systems

This report describes the naturalistic observation of secondary behaviors performed by 66 drivers who took part in the Automotive Collision Avoidance System Field Operational Test (ACAS FOT). The ACAS FOT included two driver assistance systems, adaptive cruise control (ACC) and forward collision warning (FCW). Each driver participated in both baseline (no driver assistance systems for one week) and treatment conditions (both ACC and FCW available for 3 weeks). The method employed was to sub-sample video data, and code drivers’ secondary behaviors using 4 s video clips of the driver, collected every 5 minutes. Eight-hundred and ninety video clips were reviewed and coded while participants drove manually, with conventional cruise control, ACC, and FCW. The results show that drivers who took part in the field test were no more likely to engage in secondary behaviors when driving with ACC and FCW in comparison to manual control. When the driver assistance systems became available to the participants, there was an increase in the number of conversations drivers had with passengers, probably related to the driver explaining the novel ACAS system to passengers. The results have important implications in that, at least for the duration of exposure examined, they counter the concern often raised that driver assistance systems will promote driver distraction, lapses in attention or modification in perceived risk