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The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways. ; Doctor of Philosophy ; The deployment of Automated Vehicles (AV) is starting to become widespread throughout transportation, resulting in the recognition and awareness by legislative leaders of the potential impact on transportation operations. To assist transportation operators in making the needed preparations for these vehicles, an in-depth study regarding the impact of AV and Connected Automated Vehicles (CAV) is needed. In this research, the impact of AV and CAV on the highway setting is studied. This study addresses car-following models that are currently used for simulating AV and CAV. Diverse car-following models, such as the Intelligent Driver Model (IDM), the IDM with traffic adaptive driving Strategy (SIDM), the Improved IDM (IIDM), the IIDM with Constant-Acceleration Heuristic (CAH), and the MIcroscopic model for Simulation of Intelligent Cruise control (MIXIC) were examined with the state-of-the-art vehicle trajectory data. The Highway Drone dataset (HighD) were analyzed through the implementation of genetic algorithm to gain more insight about the trajectories of these vehicles. In 2020, there is no commercially available gully automated vehicle available to the public, although many companies are conducting in field testing. This research generated AV trajectories based on the actual vehicle trajectories from the High-D dataset and adjusts those trajectories to account for ideal AV operations. The analysis from the fitted trajectory data shows that the calibrated IIDM with CAH provides a best fit on AV behavior. Next, the AV and CAV were modeled in microscopic perspective to show the impact of these vehicles on a corridor. The traffic simulation software, VISSIM, modified by implementing an external driver model to govern the interactions between Legacy Vehicles (LV), AV, and CAV on a basic and merging highway segment as well as a model of the Interstate 95 corridor south of Richmond, Virginia. From the analysis, this research revealed that the AV and CAV could increase highway capacity significantly. Even with a small portion of AV or CAV, the roadway capacity increased. On I-95, CAV performed better than AV because of Cooperative Adaptive Cruise Control (CACC) and platooning due to CAV's ability to coordinate movement through communication; however, in weaving segments, CAV underperformed AV. This result indicates that the CAV algorithms would need to be flexible in order to maintain flow in areas with weaving sections. Lastly, diverse operational conditions, such as different heavy vehicle market penetration and different aggressiveness were examined to support traffic operators transition to the introduction of AV and CAV. Based on the analysis, the study concludes that the different aggressiveness could mitigate congestion in all cases if the proper aggressiveness level is selected considering the current traffic condition. Overall, the dissertation provides guidance to researchers, traffic operators, and lawmakers to model, simulate, and evaluate AV and CAV on highways.

This synthesis provides a summary and comparative analysis of actions states across the United States are taking inresponse to automated vehicles (AVs). The research focuses on state-level stakeholder forums (e.g., task forces, committees) and state-level strategic actions (e.g., studies, initiatives, programs) initiated by a state legislature, agovernor, or a state agency. The analysis found that AV stakeholder forums and strategic actions address a diverse set offocus areas, but they pay minimal attention to the implications of AVs on the environment, public health, social equity, land use, public transit, goods movement, and emergency response. Also, forums and strategic actions commonly include members from state transportation departments, the legislature, and academia; however, representatives from industry and non-governmental organizations (NGOs) are included less often. Academia and researchers participate in themajority of AV forums and actions, either in an advisory capacity (i.e., sharing expertise and experience) and/or through conducting research. Based on this analysis, the synthesis concludes with a recommendation for California to form a state-level working group representing leaders from the public sector, industry, NGOs, and academia to advise the Governor and the Legislature on AV policy across a range of focus areas.

Front Cover -- Autonomous Vehicles and Future Mobility -- Copyright -- Contents -- Contributors -- Introduction -- 1: Autonomous vehicles and future mobility solutions -- 1 Introduction -- 2 Background -- 2.1 Socio-economic trends -- 2.2 Vehicle technological innovations -- 3 Connected and automated vehicles deployment -- 3.1 Evolutionary and revolutionary paths -- 4 Future mobility scenarios -- 4.1 Mobility as a service -- 5 Conclusions and perspectives -- References -- 2: Where will self-driving vehicles take us? Scenarios for the development of automated vehicles with Sweden as a case study -- 1 Introduction -- 1.1 Background -- 1.2 Definitions -- 1.3 Contribution -- 2 Related work -- 2.1 Simulation studies -- 2.2 Future scenarios for SDVs -- 3 Method -- 3.1 The scenario planning method -- 3.2 The scenario development process -- 3.3 Consequence analysis -- 4 Results -- 4.1 The certain development: Where Sweden is going -- 4.2 Strategic uncertainties -- 4.3 The scenarios -- 4.3.1 Same, same, but different -- 4.3.2 Sharing is the new black -- 4.3.3 Follow the path -- 4.3.4 What you need is what you get -- 4.4 The development of SDVs -- 5 Conclusions and future work -- 6 Notes -- Acknowledgments -- References -- 3: Traffic flow with autonomous vehicles in real-life traffic situations -- 1 Introduction -- 1.1 Previous studies -- 1.2 Scope of study -- 2 Method -- 3 Results -- 3.1 The dataset -- 3.2 Headway settings influence saturation flow rates -- 4 Discussion -- 5 Conclusion -- References -- 4: Demand-oriented mobility solutions for rural areas using autonomous vehicles -- 1 Introduction -- 2 Background -- 2.1 Definition and characteristics of rural areas in Germany -- 2.2 Autonomous driving -- 2.3 Trends on transport demand and supply -- 3 Model scope and structure -- 4 Data utilized.

Unmanned and Autonomous Ground Vehicle (UAGV) is a smart vehicle that capable of doing tasks without the need of human operator. The automated vehicle can work during off and on road navigation and also used in military operation such as detecting bombs, border patrol, carrying cargos, search, rescue etc reducing soldier's exposure to danger, freeing them to perform other duties. This type of vehicle mainly uses sensors to observe the environment and automatically take decisions on its own in unpredictable situation and with unknown information or pass this information to the operator who control the UAGV through various communication when it requires support. This UAGV can send visual feedbacks to the operator at the ground station. An onboard sensor gives the complete environment of the vehicle as signals to the operator.

Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Author biographies -- Acknowledgments -- Table of cases -- Table of legislation -- CHAPTER 1 INTRODUCTION -- CHAPTER 2 TESTING AUTONOMOUS VEHICLES -- CHAPTER 3 INSURANCE -- CHAPTER 4 PRODUCT LIABILITY -- CHAPTER 5 CYBER SECURITY AND DATA PROTECTION -- CHAPTER 6 INTERNATIONAL COMPARISONS -- CHAPTER 7 FUTURE DEVELOPMENTS -- Appendix I: Automated and Electric Vehicles Act 2018 -- Appendix II: The key principles of vehicle cyber security for connected and automated vehicles -- Appendix III: Levels of vehicle automation -- Index

Intro -- Preface to the Second Edition -- Teaching and Learning from This Book -- Introduction to Autonomous Driving -- 1.1 Autonomous Driving Technologies Overview -- 1.2 Autonomous Driving Algorithms -- 1.2.1 Sensing -- 1.2.2 Perception -- 1.2.3 Object Recognition and Tracking -- 1.2.4 Action -- 1.3 Autonomous Driving Client System -- 1.3.1 Robot Operating System (ROS) -- 1.3.2 Hardware Platform -- 1.4 Autonomous Driving Cloud Platform -- 1.4.1 Simulation -- 1.4.2 HD Map Production -- 1.4.3 Deep Learning Model Training -- 1.5 It Is Just the Beginning -- Autonomous Vehicle Localization -- 2.1 Localization with GNSS -- 2.1.1 GNSS Overview -- 2.1.2 GNSS Error Analysis -- 2.1.3 Satellite-Based Augmentation Systems -- 2.1.4 Real-Time Kinematic and Differential GPS -- 2.1.5 Precise Point Positioning -- 2.1.6 GNSS INS Integration -- 2.2 Localization with LiDAR and High-Definition Maps -- 2.2.1 LiDAR Overview -- 2.2.2 High-Definition Maps Overview -- 2.2.3 Localization with LiDAR and HD Map -- 2.3 Visual Odometry -- 2.3.1 Stereo Visual Odometry -- 2.3.2 Monocular Visual Odometry -- 2.3.3 Visual Inertial Odometry -- 2.4 Dead Reckoning and Wheel Odometry -- 2.4.1 Wheel Encoders -- 2.4.2 Wheel Odometry Errors -- 2.4.3 Reduction of Wheel Odometry Errors -- 2.5 Sensor Fusion -- 2.5.1 CMU Boss for Urban Challenge -- 2.5.2 Stanford Junior for Urban Challenge -- 2.5.3 Bertha from Mercedes Benz -- 2.6 References -- Perception in Autonomous Driving -- 3.1 Introduction -- 3.2 Datasets -- 3.3 Detection -- 3.4 Segmentation -- 3.5 Stereo, Optical Flow, and Scene Flow -- 3.5.1 Stereo and Depth -- 3.5.2 Optical Flow -- 3.5.3 Scene Flow -- 3.6 Tracking -- 3.7 Conclusion -- 3.8 References -- Deep Learning in Autonomous Driving Perception -- 4.1 Convolutional Neural Networks -- 4.2 Detection -- 4.3 Semantic Segmentation -- 4.4 Stereo and Optical Flow -- 4.4.1 Stereo.

Part 1: Drones – aviation/aerial automated autonomous transportation systems -- Introduction -- "Drones: The New Delivery Men? A South African and UK perspective." -- Unmanned Air Transports: the use of drones and legal issues arising thereof -- The Civil Unmanned Aerial Vehicle (UAV) Law of China: A Comparative Study of the mainland, Hong Kong, and Macao of China -- Part 2: Ships – Maritime automated / autonomous transportation systems -- The Intersection Between Law and Technology in Maritime Law -- Salvage and Autonomous Maritime Navigation -- Uncharted Legal Waters: "The Applicability of the Law on Seaworthiness & Good seamanship to Autonomous vessels." -- Reforming the Law of the Sea for the Future of Automated Shipping -- Part 3: Road Vehicles – Road automated / autonomous transportation systems -- Automated Vehicles, Liability and Insurance -- Legal and Ethical Aspects of Autonomous Vehicles -- Cyber Risks: Social, Functional and Ethical Dimensions -- Road Traffic Law and Application to Automated Vehicles.

With the rapid urban development sustainable reliable and safe transportation solutions are required to satisfy the demand and minimize potentially negative impacts. The field of autonomous vehicles AVs has got lot of attention among media researchers politicians and industry since its introduction. It can fundamentally change road transportation and improve quality of life. Although on one hand the benefits of AVs can bring revolution in transportation field and can solve many engineering problems. On the other hand there are some serious concerns about social and ethical problems such as so-called trolley problem. In certain situations AVs forced to take some decision where harming someone cannot be avoided i.e. whether it protect its passenger or to protect other road users. Therefore it needs proper ethical guideline and crash-optimization algorithm to make decision in certain situations and so we believe applied software engineering ethical approach can be the solution to these problems. In this paper we will explore the regulative instruments standards legal and moral responsibilities of AVs to act and make decision in unavoidable situations.

Learn about the evolution of the automotive industry and how its recent connection with information technology created a path towards autonomous vehicles. In this course, discover the fundamentals of the vehicle communication environment, the communication layers that compose it, and cyber security basics. Upon completion, you will be able to interpret an automotive cyber security risk analysis of past attacks and known threats, know how to minimize risk, and develop possible solutions to automotive cyber security threats