00826nam0-22002651i-450-99000474942040332119990530000474942FED01000474942(Aleph)000474942FED0100047494219990530g18409999km-y0itay50------baitay-------001yyTentamina hierograpmica atque etymologicade hierographia et pantheo etruscorum, de vasis pictis...proposta a Cataldo JannellioNeapoliApud Mirandam1840.344 p.25 cmIannelli,Cataldo<1781-1841>392502ITUNINARICAUNIMARCBK990004749420403321X A 33481/1550FLFBCFLFBCTentamina hierograpmica atque etymologica557958UNINA01535nam 2200529 450 991046007430332120200520144314.01-60781-387-4(CKB)3710000000268244(EBL)3443907(SSID)ssj0001471897(PQKBManifestationID)11816585(PQKBTitleCode)TC0001471897(PQKBWorkID)11433214(PQKB)11462238(MiAaPQ)EBC3443907(OCoLC)1125377460(OCoLC)895116837(OCoLC)on1125377460(MdBmJHUP)muse48854(OCoLC)895116837(Au-PeEL)EBL3443907(CaPaEBR)ebr10962269(EXLCZ)99371000000026824420140812h20142014 uy| 0engur|n|---|||||txtccrRequiem for the living a memoir /Jeff MetcalfSalt Lake City, Utah :University of Utah Press,[2014]©20141 online resource (257 p.)"Published in cooperation with the Utah Division of Arts and Museums."1-60781-386-6 Authors, AmericanBiographyElectronic books.Authors, American362.1969Metcalf Jeff895775MiAaPQMiAaPQMiAaPQBOOK9910460074303321Requiem for the living2001144UNINA11236nam 2200529 450 991062728050332120231110214540.03-031-06780-0(MiAaPQ)EBC7081048(Au-PeEL)EBL7081048(CKB)24786660900041(PPN)264957903(EXLCZ)992478666090004120230201d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAI-enabled technologies for autonomous and connected vehicles /Yi Lu Murphey, Ilya Kolmanovsky, and Paul WattaCham, Switzerland :Springer International Publishing,[2022]©20221 online resource (563 pages)Lecture Notes in Intelligent Transportation and Infrastructure Print version: Murphey, Yi Lu AI-Enabled Technologies for Autonomous and Connected Vehicles Cham : Springer International Publishing AG,c2022 9783031067792 Intro -- Preface -- Contents -- Advances, Opportunities and Challenges in AI-enabled Technologies for Autonomous and Connected Vehicles -- 1 Introduction -- 2 Autonomous Vehicles: Current Technologies and Challenges -- 2.1 Levels of Autonomy -- 2.2 AI Technologies in Autonomous Vehicles -- 3 Connectivity and Mobility -- 3.1 Mobility Research -- 3.2 Prediction Model for V2X Communications -- 3.3 Big Data Research in Transportation -- 3.4 Automotive Cybersecurity -- References -- Sensors and Perception -- Semi-autonomous Truck Platooning with a Lean Sensor Package -- 1 Overview -- 2 Literature Survey -- 3 Auburn Platooning System -- 3.1 Dynamic Base Real-Time Kinematic Positioning (DRTK) -- 3.2 Delphi Electronically Scanning Radar (ESR) -- 3.3 Dedicated Short Range Communications -- 3.4 Sensor Fusion -- 4 Testing Campaign -- 5 Results of Sensor Impairment -- 5.1 Effect of a Faulty Radar on Platooning -- 5.2 Effect of a Degraded GPS on Platooning -- 5.3 Effect of Radio Interference -- 5.4 Summary -- 6 Conditional Effects -- 6.1 Occlusions -- 6.2 Rain -- 6.3 Antenna Position -- 6.4 RF Interference -- 6.5 GPS Outage -- 6.6 Road Curvature -- 6.7 Grade -- 7 Conclusion -- References -- Environmental Perception for Intelligent Vehicles -- 1 Sensors -- 1.1 Development of Sensors in Intelligent Autonomous Vehicles -- 1.2 Camera -- 1.3 LiDAR -- 1.4 Radar -- 1.5 Future of Sensors in Intelligent Vehicles -- 2 Data Restoration -- 2.1 RGB Image Restoration -- 2.2 LiDAR Point Cloud Restoration -- 3 Semantic Segmentation -- 3.1 Semantic Segmentation for RGB Images -- 3.2 Semantic Segmentation for RGB-D Images -- 3.3 Semantic Segmentation for LiDAR Point Cloud -- 4 Object Detection -- 4.1 2D Object Detection -- 4.2 2D Object Detection of Fisheye Camera -- 4.3 3D Object Detection -- 5 Object Tracking -- 5.1 Object Tracking for RGB Images.5.2 Object Tracking for Point Cloud -- 6 Simultaneous Localization and Mapping -- 6.1 SLAM Overview -- 6.2 2D Visual Location and Mapping -- 6.3 3D Visual Location and Mapping -- 7 Multi-sensor Fusion -- 7.1 Multi-sensor Fusion Overview -- 7.2 LiDAR and Camera Fusion -- 7.3 LiDAR and Radar Fusion -- 7.4 Radar and Camera Fusion -- References -- 3D Object Detection for Autonomous Driving -- 1 Introduction -- 2 Recent Advance in 3D Object Detection -- 2.1 Cloud Point-Based Method -- 2.2 Image-Based Method -- 3 Descriptor Enhanced Stereo R-CNN -- 3.1 Overview -- 3.2 Coarse 3D Box Estimation -- 3.3 Unsupervised Learning of the Local Descriptor -- 3.4 Descriptor-Enhanced 3D Box Alignment -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Comparison with State-of-the-Art -- 4.3 Multi-class Comparison with 3DBBX -- 4.4 Ablation Study -- 4.5 Runtime -- 5 Conclusion and Future Work -- References -- Comparative Study on Transfer Learning for Object Classification and Detection -- 1 Introduction -- 2 State-of-the-Art Review in DNNs and Transfer Learning -- 3 Architecture and Characteristics of CNN Models -- 3.1 CNN Models for Object Classification -- 3.2 NN Models for Object Detection -- 4 Transfer Learning for Object Classification and Detection -- 4.1 Transfer Learning for Object Classification -- 4.2 Transfer Learning for Object Detection -- 5 Conclusion -- References -- Future Technology and Research Trends in Automotive Sensing -- 1 Introduction -- 2 Advancements in Radar and Lidar Sensing -- 3 Toward Energy Efficient Edge Computing via Optical Advances -- 4 Conclusion -- References -- Automated Driving Decisions and Control -- Robust AI Driving Strategy for Autonomous Vehicles -- 1 Introduction -- 2 Decision Making: DRL Driving Strategy for Changing Lanes -- 2.1 Reinforcement Learning and Deep Reinforcement Learning an Introduction.2.2 DRL for Autonomous Driving -- 2.3 Vehicle Dynamics -- 2.4 Simulation Results -- 2.5 Summary -- 3 Executing DRL Decision with Motion Control Algorithm -- 3.1 Longitudinal Motion Control -- 3.2 Lateral Motion Control -- 3.3 Summary -- 4 Generic Safety Filter Design with Control Barrier Functions -- 4.1 Control Barrier Functions -- 4.2 Calculation of Barrier Constraints -- 4.3 Contextual Selection of Decoupled CBF -- 4.4 Examples -- 4.5 Summary -- 5 Integrated Driving Policy with DRL, Motion Control, and CBF Safety Filter -- 5.1 Training Architecture with CBF Safety Filter -- 5.2 Summary -- 6 Summary and Conclusion -- References -- Artificially Intelligent Active Safety Systems -- 1 Introduction -- 1.1 SAE Definitions -- 2 Active Safety Technology -- 2.1 Collision Warning -- 2.2 Collision Intervention -- 2.3 Driving Control Assistance -- 2.4 Parking Assistance -- 2.5 Other Driving Assistance -- 2.6 Beyond Assistance -- 3 Active Safety Potential -- 4 Systems on the Road Today -- 4.1 Methods -- 4.2 Results -- 4.3 Discussion -- 4.4 Conclusion -- 5 Promising AI Applications -- 5.1 Deep Learning -- 5.2 Reinforcement Learning -- 6 Final Thoughts -- References -- Model Predictive Control for Safe Autonomous Driving Applications -- 1 Introduction -- 1.1 Notation -- 2 Problem Description -- 3 Model Predictive Flexible Trajectory Tracking Control -- 4 Tracking an Infeasible Reference -- 5 Safety-Enforcing MPC -- 6 Simulations -- 6.1 ISS: Stability with Infeasible Reference -- 6.2 MPFTC: Ensuring Safety of the Controller -- 7 Conclusions -- References -- Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning -- 1 Introduction -- 2 Lane Changes for Energy-Efficient AV Driving -- 3 Powertrain Modeling for Battery Electric Vehicles -- 3.1 Model Description -- 3.2 Model Calibration and Validation.4 Controller Design -- 4.1 Game-Theoretic Traffic Environment -- 4.2 Observation and Action Spaces -- 4.3 Reward Function -- 4.4 Training Algorithm -- 4.5 Training Process -- 4.6 Autonomous Vehicle Control Policy for Benchmarking -- 5 Results -- 5.1 Training for RL-Based Policies -- 5.2 Control Performance -- 6 Conclusions -- References -- Self-learning Decision and Control for Highly Automated Vehicles -- 1 Introduction -- 2 Scalability -- 3 Performance -- 4 Interpretability -- 5 Mixed Model -- 6 Emergency Handling -- 7 Conclusion -- References -- Advanced Driver Assistant Systems -- MAGMA: Mobility Analytics Generated from Metrics on ADAS -- 1 Introduction -- 2 Constraints -- 2.1 Data Collection Costs -- 2.2 Data Generation and Collection Issues -- 3 Determination of Actual Feature Experience -- 4 Determination of Expected Feature Experience -- 5 ADAS Feature Customer Experience Metrics -- 5.1 Binary Feature Availability -- 5.2 Nuanced Feature Availability -- 5.3 Clustering for Outlier Discovery -- 6 Conclusion -- References -- Driver Assistance Systems and Safety-Assessment and Challenges -- 1 Introduction -- 2 The Scenario Approach -- 2.1 The Idea -- 2.2 Elements of a Scenario Based Evaluation -- 3 Scenario Generation and Selection -- 3.1 Scenario Generation -- 3.2 Scenarios from Crash Databases -- 3.3 Automated Scenario Catalogue Learning -- 4 Scenario Parametrization -- 4.1 Numerical Assessment Methods -- 5 Representation of the Surrounding Traffic -- 5.1 Trajectory Prediction -- 5.2 Threat Assessment -- 6 Metrics of Risk -- 7 Outlook -- 7.1 Induced Effects on Other Vehicles -- 7.2 Shields and Emergency Systems -- References -- Factors Influencing Driver Behavior and Advances in Monitoring Methods -- 1 Introduction -- 2 Factors Influencing Driver Behavior -- 2.1 Types of Human Factors -- 2.2 Types of Environmental Factors -- 2.3 Driver Profile.3 Driver Behavior Monitoring Methods -- 3.1 Intrusive Measuring Methods -- 3.2 Camera Based Measuring Methods -- 3.3 Ergonomic and Body Posture Based Measuring Methods -- 3.4 Vehicle Dynamics Based Measuring Methods -- 3.5 Hybrid Measuring Methods -- 4 Smart Detection Algorithms -- 4.1 Supervised Classification -- 4.2 Unsupervised Classification -- 5 Deep Learning Neural Network Classifiers -- 5.1 Supervised LSTM Classifier -- 5.2 Unsupervised LSTM Classifier -- 6 Summary -- References -- Connected Autonomous Vehicles, Mobility, and Security -- Towards Learning-Based Control of Connected and Automated Vehicles: Challenges and Perspectives -- 1 Introduction -- 2 Multi-Agent Coordination Problem -- 3 Modeling Agent Dynamics -- 3.1 Double Integrator Model -- 3.2 Bicycle Model -- 3.3 Further Extensions -- 3.4 Challenges and Perspectives -- 4 Optimal Agent Coordination -- 4.1 Local Objectives and Constraints -- 4.2 Collision Avoidance -- 4.3 Centralized Optimal Control Problem -- 4.4 Distributed Solution -- 5 Towards Learning-Based Control -- 5.1 Model Uncertainties -- 5.2 Mixed-Traffic Scenarios -- 6 Conclusion -- References -- Virtual Rings on Highways: Traffic Control by Connected Automated Vehicles -- 1 Introduction -- 2 Traffic Control by Connected Automated Vehicles -- 2.1 Simplified Models for Longitudinal Vehicle and Traffic Dynamics -- 2.2 Vehicle Control Influencing Traffic -- 2.3 Traffic Control -- 3 Benefits of Connectivity -- 3.1 Simulation Results -- 3.2 Energy Efficiency -- 4 Dynamics of Traffic Flow -- 4.1 Linearized Dynamics -- 4.2 Transfer Functions -- 5 Stability of Traffic Flow -- 5.1 Stability Conditions -- 5.2 Relationship of the Stability Conditions -- 6 Conclusions -- References -- Socioeconomic Impact of Emerging Mobility Markets and Implementation Strategies -- 1 Introduction -- 2 Theoretical Preliminaries.2.1 An Introduction to Mechanism Design.Lecture Notes in Intelligent Transportation and Infrastructure Artificial intelligenceEnvironmental applicationsVehicular ad hoc networks (Computer networks)Artificial intelligenceEnvironmental applications.Vehicular ad hoc networks (Computer networks)025.063637Murphey Yi Lu1266993Kolmanovsky Ilya V.Watta PaulMiAaPQMiAaPQMiAaPQBOOK9910627280503321AI-Enabled Technologies for Autonomous and Connected Vehicles2978858UNINA