Naturalistic driving observations within ERSO, deliverable 6.1

This deliverable reports the outcome of the first task which was to generate an inventory of variables and measurement tools necessary to monitor road safety through Naturalistic Driving Observations. This was achieved by performing the following activities: 1. Generating an inventory of relevant variables to monitor road safety within ERSO. 2. Generating an inventory of relevant variables to monitor through naturalistic driving observation. 3. Combining 1 and 2 to define the variables to be measured within ERSO by naturalistic driving observation

Driver Inattention During Vehicle Automation: How Does Driver Engagement Affect Resumption Of Control?

This driving simulator study, conducted as part of the EC-funded AdaptIVe project, investigated the effect of level of distraction during automation (Level 2 SAE) on drivers’ ability to assess automation uncertainty and react to a potential collision scenario. Drivers’ attention to the road was varied during automation in one of two driving screen manipulation conditions: occlusion by light fog and occlusion by heavy fog. Vehicle-based measures, drivers’ eye movements and response profiles to events after an automation uncertainty period were measured during a highly automated drive containing one of these manipulations, and compared to manual driving. In two of seven uncertainty events, a lead vehicle braked, causing a critical situation. Drivers' reactions to these critical events were compared in a between-subjects design, where the driving scene was manipulated for 1.5 minutes. Results showed that, during automation, drivers’ response profile to a potential collision scenario was less controlled and more aggressive immediately after the transition, compared to when they were in manual control. With respect to screen manipulation in particular, drivers in the heavy fog condition collided with the lead vehicle more often and also had a lower minimum headway compared to those in the light fog condition

The effect of electronic word of mouth communication on purchase intention moderate by trust: a case online consumer of Bahawalpur Pakistan

The aim of this study is concerned with improving the previous research finding complete filling the research gaps and introducing the e-WOM on purchase intention and brand trust as a moderator between the e-WOM, and purchase intention an online user in Bahawalpur city Pakistan, therefore this study was a focus at linking the research gap of previous literature of past study based on individual awareness from the real-life experience. we collected data from the online user of the Bahawalpur Pakistan. In this study convenience sampling has been used to collect data and instruments of this study adopted from the previous study. The quantitative research methodology used to collect data, survey method was used to assemble data for this study, 300 questionnaire were distributed in Bahawalpur City due to the ease, reliability, and simplicity, effective recovery rate of 67% as a result 202 valid response was obtained for the effect of e-WOM on purchase intention and moderator analysis has been performed. Hypotheses of this research are analyzed by using Structural Equation Modeling (SEM) based on Partial Least Square (PLS). The result of this research is e-WOM significantly positive effect on purchase intention and moderator role of trust significantly affects the relationship between e-WOM, and purchase intention. The addition of brand trust in the model has contributed to the explanatory power, some studied was conduct on brand trust as a moderator and this study has contributed to the literature in this favor. significantly this study focused on current marketing research. Unlike past studies focused on western context, this study has extended the regional literature on e-WOM, and purchase intention to be intergrading in Bahawalpur Pakistan context. Lastly, future studies are recommended to examine the effect of trust in other countries allow for the comparison of the findings

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

Unconstrained design: improving multitasking with in-vehicle information systems through enhanced situation awareness

In the age of information, in-vehicle multitasking is inevitable. The popularity of the automobile in combination with the demands of everyday life presents a demand to do more than simply focus on the road. Situation Awareness (SA) is a theory that allows designers to understand how operators interact in dynamic, complex environments. Unconstrained Design is proposed as a way of enhancing multitasking performance in-vehicle. This paper presents an experimental investigation into human-machine interface concepts that aim to support drivers to multitask in-vehicle when frequent task switching is required. Two SA-based approaches were investigated, one which focussed on supporting preparation for a Non-Driving Related Activity (NDRA), and one which focussed on supporting the Driving Related Activity (DRA) when an NDRA is active. While multitasking, Contextual Cueing, using a Head-up Display, produced significant reductions in NDRA response time while an auditory lane keeping aid increased the amount of time a driver spent in the central region of a lane. This provides evidence to suggest that using SA and Unconstrained Design as a philosophy for the design of IVIS that supports drivers’ ability to multitask in-vehicle, could lead to task performance improvements.Jaguar Land Rove

Use of theory to guide development and application of sensor technologies in Nursing

Sensor technologies for health care, research, and consumers have expanded and evolved rapidly. Many technologies developed in commercial or engineering spaces, lack theoretical grounding and scientific evidence to support their need, safety, and efficacy. Theory is a mechanism for synthesizing and guiding knowledge generation for the discipline of nursing, including the design, implementation, and evaluation of sensors and related technologies such as artificial intelligence and machine learning. In this paper, three nurse scientists summarize their presentations at the Council for the Advancement of Nursing Science 2019 Advanced Methods Conference on Expanding Science of Sensor Technology in Research discussing the theoretical underpinnings of sensor technologies development and use in nursing research and practice. Multiple theories with diverse epistemological roots guide decision-making about whether or not to apply sensors to a given use; development of, components of, and mechanisms by which sensor technologies are expected to work; and possible outcomes

Drivers’ response to attentional demand in automated driving

Vehicle automation can make driving safer; it can compensate for human impairments that are recognized as the leading cause of crashes. Vehicle automation has become a central topic in transportation and human factors research. This thesis addresses some unresolved challenges on how to guide attention for safe use of automation and on how to improve the design of automation to account for humans\u27 abilities and limitations. Specifically, this thesis investigated how driver attention changed with automation and the driving situation. The objective was to inform the design of vehicle systems and develop design knowledge to support safe driving. A novelty of this thesis was in the use of real-world driving data and Bayesian methods (improved statistical modeling techniques). The analysis of driver behavior was based on data collected in naturalistic driving studies (to study the effect of assistive automation) and in a simulator experiment (to study the effect of unsupervised automation). Driver behavior was examined with measures of visual and motor response, together with contextual information, on the driving situation. The results show that assistive automation affected driver attention in real-world driving. In general, drivers devoted less attention at the forward path with automation than without. However, driver attention was sensitive to the presence of other traffic and changes in illumination---variations in the surrounding environment that increased the uncertainty of the driving situation---and it was elicited by visual, audio, and vestibular-kinesthetic-somatosensory information (perceptual cues) that alerted to an impending conflict. Driver response to a critical situation with unsupervised automation had a reflexive component (glance on-path, hands on wheel, and feet on pedals) and a planned component (decision and execution of evasive maneuver). Warnings primarily alerted attention rather than triggering an intervention. Expectation, which changed over time depending on experience, affected driver response substantially. This thesis found that the safety implications of diverting attention away from the driving situation need to be interpreted in relation to the characteristics and criticality of the driving situation (driving context) and need to consider the reduction of risk exposure due to automation (e.g., headway maintenance and collision warnings). Drivers were, for example, successful at changing their behavior in the presence of other vehicles and in different light conditions independently of automation. If drivers are not attentive at critical points, warnings are effective for triggering a quick shift of attention to the driving task in preparation to an evasive action. The results improved on those of earlier studies by providing a comprehensive assessment of driver attentional response in routine driving and critical situations. The results can support evidence-based recommendations (inattention guidelines) and be used as a reference for driver modeling and vehicle systems development

Development of rear-end collision avoidance in automobiles

The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions. In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor

A framework for context-aware driver status assessment systems

The automotive industry is actively supporting research and innovation to meet manufacturers' requirements related to safety issues, performance and environment. The Green ITS project is among the efforts in that regard. Safety is a major customer and manufacturer concern. Therefore, much effort have been directed to developing cutting-edge technologies able to assess driver status in term of alertness and suitability. In that regard, we aim to create with this thesis a framework for a context-aware driver status assessment system. Context-aware means that the machine uses background information about the driver and environmental conditions to better ascertain and understand driver status. The system also relies on multiple sensors, mainly video and audio. Using context and multi-sensor data, we need to perform multi-modal analysis and data fusion in order to infer as much knowledge as possible about the driver. Last, the project is to be continued by other students, so the system should be modular and well-documented. With this in mind, a driving simulator integrating multiple sensors was built. This simulator is a starting point for experimentation related to driver status assessment, and a prototype of software for real-time driver status assessment is integrated to the platform. To make the system context-aware, we designed a driver identification module based on audio-visual data fusion. Thus, at the beginning of driving sessions, the users are identified and background knowledge about them is loaded to better understand and analyze their behavior. A driver status assessment system was then constructed based on two different modules. The first one is for driver fatigue detection, based on an infrared camera. Fatigue is inferred via percentage of eye closure, which is the best indicator of fatigue for vision systems. The second one is a driver distraction recognition system, based on a Kinect sensor. Using body, head, and facial expressions, a fusion strategy is employed to deduce the type of distraction a driver is subject to. Of course, fatigue and distraction are only a fraction of all possible drivers' states, but these two aspects have been studied here primarily because of their dramatic impact on traffic safety. Through experimental results, we show that our system is efficient for driver identification and driver inattention detection tasks. Nevertheless, it is also very modular and could be further complemented by driver status analysis, context or additional sensor acquisition