http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Human Factors in Intelligent Vehiclesaddresses 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.
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
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.
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).
Cristina Olaverri-Monreal, Fernando García-Fernǹdez, Rosaldo J. F. Rossetti
COMPUTERS / Database Management / Data Mining
River Publishers series in transport technology