Hazard prediction discriminates between novice and experienced drivers

Typical hazard perception tests often confound multiple processes in their responses. The current study tested hazard prediction in isolation to assess whether this component can discriminate between novice and experienced drivers. A variant of the hazard perception test, based on the Situation Awareness Global Assessment Technique, found experienced drivers to outperform novices across three experiments suggesting that the act of predicting an imminent hazard is a crucial part of the hazard-perception process. Furthermore three additional hypotheses were tested in these experiments. First, performance was compared across clips of different length. There was marginal evidence that novice drivers' performance suffered with the longest clips, but experienced drivers’ performance did not, suggesting that experienced drivers find hazard prediction less effortful. Secondly, predictive accuracy was found to be dependent on the temporal proximity of visual precursors to the hazard. Thirdly the relationship between the hazard and its precursor was found to be important, with less obvious precursors improving the discrimination between novice and experience drivers. These findings demonstrate that a measure of hazard prediction, which is less confounded by the influence of risk appraisal than simple response time measures, can still discriminate between novice and experienced drivers. Application of this methodology under different conditions can produce insights into the underlying processes that may be at work, whilst also providing an alternative test of driver skill in relation to the detection of hazards

An object oriented Bayesian network approach for unsafe driving maneuvers prevention system

© 2017 IEEE. As the main contributor to the traffic accidents, unsafe driving maneuvers have taken attentions from automobile industries. Although driving feedback systems have been developed in effort of dangerous driving reduction, it lacks of drivers awareness development. Therefore, those systems are not preventive in nature. To cover this weakness, this paper presents an approach to develop drivers awareness to prevent dangerous driving maneuvers. The approach uses Object-Oriented Bayesian Network to model hazardous situations. The result of the model can truthfully reflect a driving environment based upon situation analysis, data generated from sensors, and maneuvers detectors. In addition, it also alerts drivers when a driving situation that has high probability to cause unsafe maneuver to be detected. This model then is used to design a system, which can raise drivers awareness and prevent unsafe driving maneuvers

Silent, Unsafe...and Underestimated?. Exploring the relationships between life stress and safety issues among spanish drivers

Almost all the recent studies addressing road safety from the approach of human factors agree that stress is one of the most considerable (but underestimated) threats for safe driving. However, evidence on the relationship between stressful life events and driver performance remains scarce. Therefore, this study aimed to assess life stress-related perceptions of Spanish drivers, as well as exploring their relationships with self-reported driving performance, decision-making and other road safety-related issues. Methods: This cross-sectional research analysed the information gathered from a nationwide sample of n=840 Spanish drivers responding to an electronic survey on psychosocial issues, stress-related factors and driving issues. The results show that a high percentage (75%) consider that stress may impair their driving performance, while 76.9% of drivers report having experienced at least one major stressful life event during the last year. Despite this relatively high awareness of the negative role of stress and its associated factors for driving safety, this study found that drivers tend to 'ignore the alarm signals', as they often reported keeping driving, even when noticing their driving fitness and performance might be impaired by stress-related factors. This study highlights the need to create and apply interventions aimed at informing and training drivers to identify, manage and cope with stress from different spheres, including stressful life events, as a means of potentially improving their driving safety habits and outcome

Security Implications of Fog Computing on the Internet of Things

Recently, the use of IoT devices and sensors has been rapidly increased which also caused data generation (information and logs), bandwidth usage, and related phenomena to be increased. To our best knowledge, a standard definition for the integration of fog computing with IoT is emerging now. This integration will bring many opportunities for the researchers, especially while building cyber-security related solutions. In this study, we surveyed about the integration of fog computing with IoT and its implications. Our goal was to find out and emphasize problems, specifically security related problems that arise with the employment of fog computing by IoT. According to our findings, although this integration seems to be non-trivial and complicated, it has more benefits than the implications.Comment: 5 pages, conference paper, to appear in Proceedings of the ICCE 2019, IEEE 37th International Conference on Consumer Electronics (ICCE), Jan 11- 13, 2019, Las Vegas, NV, US

Chitosan-zinc oxide composite for active food packaging Applications

Chitosan-zinc oxide (C-ZnO) films were prepared by a simple one pot procedure. In order to investigate the property of C-ZnO films, two composite films were prepared by varying the loading of ZnO and compared with pure chitosan film (C). The films were character-ized by various techniques such as FTIR, DSC, tensile, contact angle and water vapour permeability. FTIR analysis showed changes in hydrogen bonds band at 3351 cm-1 compared to pure chitosan film. The incorporation of ZnO in chitosan films increased the contact angle by 30.5% in C-ZnO1.0 film while water vapour transmission rate decreased by 7.8% compared to C film. From the tensile test, C-ZnO0.5 and C-ZnO1.0 films were found to be much superior by 1.5 times and 2.5 times respectively compared to bare chitosan film. Larger inhibition ring (by 47%) was exhibited by C-ZnO1.0 as compared to C-ZnO0.5 when tested against S.aureus. From the results, it is displayed that the incorporation of zinc oxide to chitosan improve their properties which also shown the potential to become a candi-date for food active packaging

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

'탑승자'의 관점의 시간, 위치 기반 차량 클러스터 UI 디자인 프레임 제안 연구

학위논문(석사)--서울대학교 대학원 :미술대학 디자인학부 디자인전공,2019. 8. 정의철.One important design issue is the examination of how the user interface (UI) supports the new user role in future mobility. However, there are few design studies on the passengers cognitive needs and behavior in Autonomous Vehicles (AVs) based on empirical data. There is no doubt that autonomous mobility technologies are growing. The technology is already aiding the driving experience, and it will change the mobility culture and the transition of driver into passenger. This study is based on the premise that future AV is capable of performing all driving tasks. It proposes a set of passenger-centered automotive cluster UI designs for future mobility employing two factors: time and path. A set of empirical data is provided to understand the passengers perspective. In this study, a solid set of empirical data on the cognitive needs of passengers is collected. Human cognitive characteristics and driving tasks are investigated from various viewpoints to understand the passengers iii perspective. The cognitive relationship in the driving environment is analyzed through a literature review on situation awareness (SA) and structuring of the data flow framework. The framework is further explored by connecting the technological role transformation to the passenger. To construct the empirical database on the passenger, three sets of user tests and in-depth interviews were undertaken. The user tests were designed employing the Wizard of Oz method, and the results were summarized using descriptive and exploratory analysis. Based on these insights, a set of UI designs from the perspective of the passenger was proposed, and usability tests were conducted to verify its effectiveness and usability. The results of the tests demonstrate that a major percentage of the information request was related to time (current time and duration) and path (vehicle location and surroundings). Based on the data, a UI framework was built. Two usage scenarios were designed, time-full and time-less, for better in-situation comprehension. Time- and path-based UI were proposed to flow with the scenarios. A usability test was conducted, and a passengers cognitive framework was defined. There are two aspects to this study: the data flow frameworks of the driver/passenger, and the UI design proposal. Situational precision from the perspective of the driver was analyzed to understand the relationship between the user, the vehicle and the road conditions. Further, the cognitive framework of the passenger was proposed based on the data. This study provides a solid understanding of drivers emerging needs when they are relieved of the cognitive burden of driving tasks. The UI features for AV are introduced based on the empirical data and research related to the provision of better situation awareness, focusing on time and location. This study contributes to the extant literature by observing the iv perspective of passengers in Autonomous vehicles based on a qualitative study. The proposed UI design will be further explored as a communication method between the system and the passive user in future mobility.사용자 인터페이스가 (UI) 미래 이동성에서 새로운 사용자 역할을 지지하는 디자인 도출은 미래 이동성 분야에서 중요한 디자인 이슈이다. 그러나 사용자 실험에 근거하여 자율주행차량 (AV) 의 탑승자인지 욕구와 행동에 대한 디자인 연구는 미미하다. 자율주행이 기술의 발전과 그 영역은 점차 넓어지고 있다. 해당 기술은 이미 운전 환경에 적용되고 있으며, 이로 인해 미래 이동문화에서 사용자의 역할은 '운전자'에서 '탑승자'로 변화한다. 본 연구는 미래 자율주행차량이 모든 운전 상황에 대처할 수 있다는 것을 전제로 한다. 사용자 실험을 통해 탑승자의 관점에 대한 분석을 진행하였고, 이를 기반으로 미래 모빌리티 환경에 적용될 사용자 인터페이스를 제안한다. 제안된 디자인은 운전자 중심의 상황인지에서 벗어나 탑승자 중심 인지 정보 요소를 분석하였고, 시간과 경로 두 가지 요소를 강조한 UI 를 제안한다. 본 연구에서 탑승자의 인지 정보 요구에 대한 실험적 데이터를 수집하였다. 탑승자의 관점을 이해하기 위해 다양한 관점에서 인간의 인지적 특성 및 운전 태스크를 관찰하였고, 상황인지 (SA) 에 관한 문헌 연구와 데이터 프레임워크 구조화를 통해 운전 환경에서 발생하는 인지적 요소 관계를 분석하였다. 제안된 프레임워크는 기술 변화에 따라 운전자가 탑승자로 변화되었을 때 운전 환경에서의 데이터 관계 변화를 시각적으로 구조화하여 심층적으로 탐구되었다. 탑승자의 인지 니즈 대한 실험적 데이터베이스를 수집하기 위해 총 3 세트의 유저 테스트와 심층 인터뷰가 수반되었다. 유저 테스트는 Wizard of Oz 방법을 사용하여 설계되었으며 실험 결과는 질적 연구방법론의 분석 방법을 통해 분석되었다. 실험을 통해 얻은 인사이트를 바탕으로 탑승자 관점에서 UI 디자인을 제안하고 사용성 테스트를 통해 효율성과 유용성을 5 점 리 커트 스케일로써 검증하였다. 실험 결과에 따르면 탑승자가 요청한 인지 정보는 시간 (현재 시각 및 기간)과 경로 (차량 위치 및 주변 환경)에 집중된 것을 관찰할 수 있었다. 이와 같은 데이터를 기반으로 UI 프레임워크를 구성하였다. 상황 속의 사용례를 제시하기 위하여도 가지 time-full 과 time-less 의 사용 시나리오를 구축하고, 제안된 시나리오에 따라 시간과 위치에 기반한 UI 를 제안하였다. 제안된 UI 에 대한 사용성 테스트를 진행하였고, 탑승자 관점에서의 운전상황 인지 워크 프레임을 완성하였다. 본 연구의 가치는 두 가지로 정리될 수 있다. 하나는 운전자 / 탑승자의 데이터 플로우 프레임워크를 제안하였다는 것과 두 번째는 탑승자의 관점을 지지하는 UI 디자인 제안에 있다. 운전자의 관점에서의 운전 상황을 분석하여 사용자, 차량, 그리고 도로 상태 간의 관계를 시각화하였고, 이는 탑승자인지 플로우 프레임워크를 제안하는데 기조적인 틀로써 사용되었다. 본 연구는 운전 태스크를 수행하는 데에 필요했던 인지 부담에서 벗어났을 때의 운전자가 필요로 하는 복합적인 니즈에 대해 관찰하고 미래 모빌리티 환경에 적합한 UI 의 디자인 요소에 대한 연구논문이다. 미래 자율주행차량 안의 사용자 인터페이스가 갖추어야 하는 요소를 실험적 데이터에 근거하여 제시하며, 시간과 루트를 강조하여 향상된 상황 인지를 제공하는 방법에 대한 심도있는 관찰을 기록한다. 본 연구는 질적 연구에 기초한 자율 차량의 탑승자 관점을 관찰함으로써 기존 자율주행이 디자인 연구에 기여할 것이다. 제안된 UI 디자인 미래 이동 성안에서 시스템과 탑승자 간의 커뮤니케이션 방법에 대한 연구로써 그 의의가 있다.ABSTRACT ...................................................................................................................... II CHAPTER 1. INTRODUCTION......................................................................................... １ 1.1. BACKGROUND ..............................................................................................................１ 1.2. PURPOSE .....................................................................................................................７ 1.3. RESEARCH QUESTION.....................................................................................................８ CHAPTER 2. LITERATURE REVIEW ..............................................................................１１ 2.1. SITATION AWARENESS (SA) ........................................................................................１１ 2.2. HUMAN INFORMATION PROCESSING MODEL..................................................................１５ 2.3. DRIVING SITUATION AWARENESS AND PERSPECTIVE.........................................................２０ 2.4. DRIVING TASK AND SENSORY INTERACTION ....................................................................２２ CHAPTER 3. COGNITIVE NEEDS IN AUTONOMOUS.....................................................２７ 3.1. DRIVING BEHAVIOR TRANSFORMATION AND CLUSTER UI..................................................２７ 3.2. COGNITIVE FRAMEWORK TRANSFORMATION ..................................................................３３ CHAPTER 4. USER TESTS ............................................................................................３６ 4.1. WIZARD OF OZ PROTOTYPING .....................................................................................３８ 4.2. PILOT TEST 1............................................................................................................４０ 4.2.1. Experiment Design & Laboratory Setting.................................................４０ 4.2.2. Persona Scenario & Task Design ..............................................................４２ 4.2.3. Preparation of Driving situation...............................................................４５ 4.2.4. Procedure.................................................................................................４７ 4.2.5. Data Analysis & Insight............................................................................４８ 4.3. PILOT TEST 2............................................................................................................５１ 4.3.1. Amendment: Experiment Design & Laboratory Setting ...........................５２ 4.3.2. Amendment: Task Scenario & Command Cue..........................................５４ 4.3.3. Amendment: Perform Role and preparation of driving situation ............５７ 4.3.4. Amendment: Procedure ...........................................................................５９ 4.3.5. Data Analysis & Insight............................................................................６２ 4.4. MAIN TEST ..............................................................................................................６５ 4.4.1. Experiment Design & Laboratory setting .................................................６６ 4.4.2. Task Design ..............................................................................................６９ 4.4.3. Procedure.................................................................................................７１ 4.4.4. Result Analysis & Insight..........................................................................７４ CHAPTER 5. UI CONCEPT DEVELOPMENT...................................................................８１ 5.1. UI DESIGN METHOD..................................................................................................８１ 5.2. DESIGN PROPOSAL ....................................................................................................８４ 5.3. USER SCENARIOS ......................................................................................................８６ 5.3.1 Scenario 1. Time-less: Late for a morning meeting..................................８６ 5.3.2 Scenario 2.Time-full: Leisure driving on weekends ..................................９３ CHAPTER 6. USABILITY TEST ......................................................................................９８ 6.1. USABILITY TEST GUIDE ...............................................................................................９８ 6.2. ASSESSMENT USABILITY TEST ..................................................................................１００ 6.2.1 Test planning........................................................................................１００ 6.2.2 Laboratory setting................................................................................１０２ 6.2.3 Test conduct and debriefing.................................................................１０６ 6.3. RESULT ANALYSIS ..................................................................................................１０６ CHAPTER 7. CONCLUSION......................................................................................１０７ APPENDIX 1...........................................................................................................１１０ APPENDIX 2...........................................................................................................１１１ APPENDIX 3...........................................................................................................１１３ APPENDIX 4...........................................................................................................１２１ APPENDIX 5...........................................................................................................１２４ APPENDIX 6...........................................................................................................１２８ APPENDIX 7...........................................................................................................１３３ BIBLIOGRAPHY ......................................................................................................１３６ 국문 초록 ............................................................................................................１４３Maste

The effect of a secondary task on drivers’ gap acceptance and situational awareness at junctions

The current studies explored the roles of the visuospatial and phonological working memory subsystems on drivers’ gap acceptance and memory for approaching vehicles at junctions. Drivers’ behaviour was measured in a high-fidelity driving simulator when at a junction, with, and without a visuospatial or phonological load. When asked to judge when to advance across the junction, gap acceptance thresholds, memory for vehicles and eye movements were not different when there was a secondary task compared to control. However, drivers’ secondary task performance was more impaired in the visuospatial than phonological domain. These findings suggest that drivers were able to accept impairment in the secondary task while maintaining appropriate safety margins and situational awareness. These findings can inform the development of in-car technologies, improving the safety of road users at junctions. Practitioner summary: Despite research indicating that concurrent performance on working memory tasks impairs driving, a matched visuospatial or phonological memory load did not change drivers’ gap acceptance or situational awareness at junctions. Drivers displayed appropriate compensatory behaviour by prioritising the driving task over the visuospatial secondary task. Abbreviations: ROW: right of way; RIG: random time interval generatio

How to keep drivers engaged while supervising driving automation? A literature survey and categorization of six solution areas

This work aimed to organise recommendations for keeping people engaged during human supervision of driving automation, encouraging a safe and acceptable introduction of automated driving systems. First, heuristic knowledge of human factors, ergonomics, and psychological theory was used to propose solution areas to human supervisory control problems of sustained attention. Driving and non-driving research examples were drawn to substantiate the solution areas. Automotive manufacturers might (1) avoid this supervisory role altogether, (2) reduce it in objective ways or (3) alter its subjective experiences, (4) utilize conditioning learning principles such as with gamification and/or selection/training techniques, (5) support internal driver cognitive processes and mental models and/or (6) leverage externally situated information regarding relations between the driver, the driving task, and the driving environment. Second, a cross-domain literature survey of influential human-automation interaction research was conducted for how to keep engagement/attention in supervisory control. The solution areas (via numeric theme codes) were found to be reliably applied from independent rater categorisations of research recommendations. Areas (5) and (6) were addressed by around 70% or more of the studies, areas (2) and (4) in around 50% of the studies, and areas (3) and (1) in less than around 20% and 5%, respectively. The present contribution offers a guiding organisational framework towards improving human attention while supervising driving automation.submittedVersio

The ‘frontal lobe’ project: A double-blind, randomized controlled study of the effectiveness of higher level driving skills training to improve frontal lobe (executive) function related driving performance in young drivers

The current study was undertaken in order to evaluate the effectiveness of higher level skills training on safe driving behaviour of 36 teenage drivers. The participants, who attended the Driver Training Research camp in Taupo (NZ) over a two week period, were 16 to 17 years old and had a valid restricted driver licence. The study focused on four main aims. Firstly, the behavioural characteristics of the sample and their attitudes to risk taking and driving were examined. Results showed that speeding was the most anticipated driving violation, and high levels of confidence were associated with a higher number of crashes and a greater propensity for risk taking. Many, often male participants, also rated their driving skills as superior to others and thought they would be less likely than others to be involved in an accident. Secondly, the relationship between driving performance and executive functioning, general ability and sustained attention was evaluated. Overall, better driving performance and more accurate self-evaluation of driving performance was related to higher levels of executive functions, in particular, working memory, and cognitive switching. In addition, higher general ability and greater ability to sustain attention were also linked to better performance on the driving related assessments. The third focus of this study was to compare the effects of both, higher level and vehicle handling skills training on driving performance, confidence levels and attitudes to risk. While both types of training improved direction control, speed choice and visual search, along with number of hazards detected and actions in relation to hazards, statistically significant improvement on visual search was seen only after higher level skills training. Vehicle handling skills training significantly improved direction control and speed choice. In addition, confidence levels in their driving skills were significantly lowered and attitudes to speeding, overtaking and close following had improved significantly in the participants after the higher level driving skills training. The final aspect to this study was to examine the effects of the training over the following 6 month period based on self-reported driving behaviour. The response rate of participants however, was not sufficient to reach any meaningful conclusion on any long-term training effects. A pilot study using GPSbased data trackers to assess post-training driving behaviour revealed some promising results for future driver training evaluation studies. The overall implications of the results are discussed in relation to improving the safety of young drivers in New Zealand