Human Factors in Intelligent Vehicles (River Publishers Series in Transport Technology)

Human Factors in Intelligent Vehicles addresses issues related to the analysis of human factors in the design and evaluation of intelligent vehicles for a wide spectrum of applications and over different dimensions. To commemorate the 8th anniversary of the IEEE ITS Workshop on Human Factors (http://hfiv.net) some recent works of authors active in the automotive human factors community have been collected in this book. Enclosed here are extended versions of papers and tutorials that were presented at the IEEE ITSS Workshop on "Human Factors in Intelligent Vehicles" and also included is additional deeper analysis along with detailed experimental and simulation results. The contributors cover autonomous vehicles as well as the frameworks for analyzing automation, modelling and methods for road users' interaction such as intelligent user interfaces, including brain-computer interfaces and simulation and analysis tools related to human factors. Front Cover Human Factors in Intelligent Vehicles Contents Preface List of Contributors List of Figures List of Tables List of Abbreviations 01 Continuous Game Theory Pedestrian Modelling Method for Autonomous Vehicles 1.1 Introduction 1.2 Related Work 1.2.1 Pedestrian Crossing Behaviour 1.2.2 Game Theory 1.2.3 Pedestrian Tracking 1.3 Methods 1.3.1 Human Experiment 1.3.2 Pedestrian Detection and Tracking 1.3.3 Sequential Chicken Model 1.3.4 Gaussian Process Parameter Posterior Analysis 1.4 Results 1.5 Discussion Acknowlegdment References 02 The Interface Challenge for Partially Automated Vehicles: How Driver Characteristics Affect Information Usage Over Time 2.1 Introduction 2.2 Method 2.2.1 Study Design 2.2.2 Participants 2.2.3 Interface Design 2.2.4 Driving Scenario 2.2.5 Procedure 2.2.6 Data Analysis 2.2.6.1 Trust (Parts 1 and 2) 2.2.6.2 DBQ (Part 1 only) 2.2.6.3 DALI (Part 2 only) 2.3 Results 2.3.1 Trust Results (Parts 1 and 2) 2.3.2 DBQ Results (Part 1 only) 2.3.3 DALI Results (Part 2 only) 2.3.4 Fixations 2.3.4.1 Fixations and Trust (Parts 1 and 2) 2.3.4.2 Fixations and DBQ (Part 1 only) 2.3.4.3 Fixations and DALI (Part 2 only) 2.3.5 Between trust, DBQ and DALI 2.3.5.1 Trust and DBQ (Part 1 only) 2.3.5.2 Trust and DALI (Part 2 only) 2.4 Discussion 2.4.1 Fixations 2.4.1.1 Fixations and Trust (Parts 1 and 2) 2.4.1.2 Fixations and DBQ (Part 1 only) 2.4.2 Fixations and DALI (Part 2 only) 2.4.3 Between Trust, DBQ and DALI 2.4.3.1 Trust and DBQ (Part 1 only) 2.4.3.2 Trust and DALI (Part 2 only) 2.5 Conclusion References 03 A CNN Approach for Bidirectional Brainwave Controller for Intelligent Vehicles 3.1 Introduction 3.1.1 Human Brain 3.1.2 Brainwaves Features 3.1.3 BCI Research 3.2 Setup Overview 3.2.1 Brainwave Sensor 3.2.2 Vehicle Platform 3.3 Methodology 3.3.1 Data Reading 3.3.2 Data Filtering 3.3.3 Input Processing 3.3.4 NN Classifier 3.3.5 CNN Classifier 3.3.5.1 MindNet_1 3.3.5.2 MindNet_2 3.4 Experimental Works and Results 3.4.1 General Classifier 3.4.2 Individual Classifier 3.4.3 Computational Time 3.5 Conclusion and Future Work References 04 A-RCRAFT Framework for Analysing Automation: Application to SAE J3016 Levels of Driving Automation 4.1 Introduction 4.2 A Framework for Automation Analysis: A-RCRAFT 4.2.1 Allocation of Functions and Tasks 4.2.2 Allocation of Resources 4.2.3 Allocation of Control Transitions 4.2.4 Allocation of Responsibility 4.2.5 Allocation of Authority 4.3 Qualitative Analysis of SAE J3016 Levels of Driving Automation with A-RCRAFT 4.3.1 Scope of the SAE J3016 for the Human Tasks and System Functions 4.3.2 Decomposition of Levels of Driving Automation Accordingto A-RCRAFT 4.3.3 Results of the Analysis and Benefits from Using A-RCRAFT 4.4 Conclusion References 05 Autonomous Vehicles: Vulnerable Road User Response to Visual Information Using an Analysis Framework for Shared Spaces 5.1 Introduction 5.2 Field Test Description 5.3 Analyzing Algorithm 5.3.1 Pedestrian Detection and Pose Estimation 5.3.2 Distance Estimation via Stereo Cameras 5.3.3 Pedestrian tracking with DeepSort 5.3.4 Face Detection 5.3.5 Velocity 5.3.6 Classification 5.3.7 Behavior Segmentation 5.4 Data Analysis 5.5 Results 5.5.1 Algorithm Result 5.5.2 Field Tests Results 5.6 Conclusion, Discussion, and Future Work Acknowledgment References 06 Intelligent Vehicles and Older Drivers 6.1 Introduction 6.2 Age-related Limitations in Driving 6.2.1 Vision and Audition 6.2.2 Cognitive Function 6.2.3 Physical Function 6.3 How Can Intelligent Vehicles Help Older Drivers? 6.4 Intelligent Vehicles and Older Driver 6.4.1 Research Methods 6.4.2 Age Differences in the Acceptance of Assistive Technologies 6.4.3 Age Differences in Effectiveness of FCW 6.4.4 Age Differences in Effectiveness of LDW 6.5 HMI Design for Older Drivers 6.5.1 Visual HMI Design 6.5.2 Audible HMI Design 6.5.3 Multiple-task Design 6.6 Conclusions References 07 Integration Model of Multi-Agent Architectures for Data Fusion-Based Active Driving System 7.1 Introduction 7.2 Related Work 7.3 Deployment Architecture 7.4 Materials and Methods 7.4.1 Materials 7.4.2 Deployment Details 7.4.3 Driving Trail Designing 7.5 Results 7.6 Discussion Acknowledgment References Index About the Editors Back Cover "Human Factors in Intelligent Vehicle saddresses issues related to theanalysis of human factors in the design and evaluation of intelligent vehiclesfor a wide spectrum of applications and over different dimensions. Thecontributors cover autonomous vehicles as well as the frameworks for analyzingautomation, modelling and methods for road users' interaction such asintelligent user interfaces, including brain-computer interfaces and simulationand analysis tools related to human factors."-- Provided by publisher