| Autore |
Murphey Yi Lu
|
| Pubbl/distr/stampa |
Cham, Switzerland : , : Springer International Publishing, , [2022]
|
| Descrizione fisica |
1 online resource (563 pages)
|
| Disciplina |
025.063637
|
| Collana |
Lecture Notes in Intelligent Transportation and Infrastructure
|
| Soggetto topico |
Artificial intelligence - Environmental applications
Vehicular ad hoc networks (Computer networks)
|
| ISBN |
3-031-06780-0
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
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.
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| Record Nr. | UNINA-9910627280503321 |
Info
2010 IEEE Vehicular Networking Conference
