Deep Learning and Its Applications for Vehicle Networks

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
About the Editors
List of Contributors
Part I: Deep learning for vehicle safety and security
Chapter 1: Deep learning for vehicle safety
1.1 Introduction
1.2 Deep learning for internal vehicle monitoring
1.2.1 Camera-based system
1.2.2 Wearable sensor-based system
1.2.3 Driver behavior monitoring
1.3 Deep learning for surrounding environment perception
1.3.1 Road detection
1.3.2 Vehicle surrounding environment detection
1.3.3 Object detection in challenging environments
1.4 Deep learning for traffic management
1.4.1 Traffic flow modelling
1.4.2 Vehicle and infrastructure communications
1.5 Deep learning-based route planning and navigation
1.5.1 Route planning for travellers
1.5.2 Route planning for food transportation
1.5.3 Dynamic routing with unknown map
1.6 Conclusions
References
Chapter 2: Deep learning for driver drowsiness classification for a safe vehicle application
2.1 Introduction
2.1.1 Importance of drowsiness detection
2.1.2 Application in future automated vehicles
2.2 Driver drowsiness detection methods
2.2.1 Subjective measures
2.2.2 Objective measures
2.2.2.1 Input sources for driver drowsiness detection
2.2.3 Deep learning methods
2.2.3.1 Deep learning methods applied to the biosignals
2.3 Comparison of methods
2.4 Summary and outlook
Notes
References
Chapter 3: A deep learning perspective on Connected Automated Vehicle (CAV) cybersecurity and threat intelligence
3.1 Introduction
3.2 CAV technological enablers: automation and connectivity
3.3 CAV threat landscape and threat intelligence
3.3.1 In-vehicle (low-level sensor) cyber vulnerabilities
3.3.2 Vehicle control modules
3.3.3 Security analysis of CAV threats
3.3.4 Attack surfaces
3.3.5 Organizational risks to CAV ecosystem
3.4 CAV threat mitigation: anomaly detection and classification with deep learning
3.5 Frontiers in deep learning (advancement and future)
3.5.1 Meta-learning
3.5.2 Federated learning
3.6 End-to-end deep CNN-LSTM architecture for CAV cyberattack detection
3.6.1 Performance analysis
3.6.1.1 Dataset
3.6.1.2 Evaluation metrics
3.6.2 Results and discussions
3.7 Conclusion
References
Part II: Deep learning for vehicle communications
Chapter 4: Deep learning for UAV network optimization
4.1 Introduction
4.2 Key categories for UAV networking throughput enhancement
4.3 Routing enhancement for UAV networking throughput
4.3.1 Position-based routing
4.3.1.1 Single path-based
4.3.1.2 Multiple path-based
4.3.2 Topology-based routing
4.3.2.1 Proactive
4.3.2.2 Reactive
4.3.2.3 Hybrid
4.3.3 Swarm-based routing
4.3.4 DL-enabled routing for UAV networking
4.4 UAV networking construction
4.4.1 UAV swarm networking construction
4.4.2 DL-enabled UAV swarm networking enhancement
4.5 DL-enabled UAV networking throughput
4.5.1 DL-enabled allocation for throughput enhancement
4.5.2 DL-enabled scheduling for throughput enhancement
4.5.2.1 Scheduling for UAV networking
4.5.2.2 DL-enabled scheduling for UAV networking
4.6 Conclusions
References
Chapter 5: State-of-the-art in PHY layer deep learning for future wireless communication systems and networks
5.1 Introduction
5.1.1 Related survey papers
5.1.2 Summary of this chapter
5.2 Data-driven ML methods for transceiver optimization
5.2.1 Data-driven approach for end-to-end transceiver optimization
5.2.2 Model-aided data-driven methods for modular transceiver optimization
5.3 Deep learning for symbol detection
5.3.1 Incorporating expert knowledge into autoencoders
5.3.2 Implementing NNs at the receiver
5.3.3 Sequential detectors using ML
5.4 Channel estimation using ML
5.5 Channel prediction in frequency- and time-domain using ML
5.6 Channel coding using AI/ML
5.7 Intelligent link adaptation
5.8 Intelligent radios
5.8.1 Intelligent spectrum sensing
5.8.2 Automatic signal recognition using CNNs
5.8.3 Intelligent radio environment
5.9 ML for system-level performance evaluation of wireless networks
5.10 Conclusions
Notes
References
Chapter 6: Deep learning-based index modulation systems for vehicle communications
6.1 Introduction
6.2 V2V and V2I Communications
6.3 Deep learning-based index modulation systems
6.3.1 Multicarrier-based index modulation systems
6.3.1.1 Transmitter of system model with OFDM-IM
6.3.1.2 Traditional detection scheme
6.3.1.3 Deep learning-based detector
6.3.1.3.1 Deep learning model
6.3.1.3.2 Training procedure
6.3.1.3.3 Online deployment
6.3.2 Single-carrier based index modulation systems
6.3.2.1 Transmitter of system model with CIM-SS
6.3.2.2 Conventional detection scheme
6.3.2.3 Deep learning-based detection
6.3.3 Multi-input multi-output based index modulation systems
6.3.3.1 System model and related works
6.3.3.2 Deep learning-based SMTs
6.3.3.3 Performance analysis on energy efficiency of vehicle communications with IM
6.4 Conclusions
References
Chapter 7: Deep reinforcement learning applications in connected-automated transportation systems
7.1 Introduction
7.1.1 Chapter organization
7.2 Deep reinforcement learning: theory and background
7.2.1 (Deep) reinforcement learning: a brief history
7.2.2 Classical reinforcement learning
7.2.2.1 Value-based RL
7.2.2.2 Policy-based RL
7.2.3 Deep reinforcement learning
7.2.3.1 Deep Q-networks
7.2.3.2 Deep policy networks
7.2.3.3 Deep actor-critic networks
7.2.4 Formulating (deep) reinforcement learning for CAV application
7.3 Data environment in CAV networks
7.3.1 Benefits
7.3.2 Data generated by AVs
7.4 Deep reinforcement learning applications: connected vehicles
7.4.1 Lane changing and assistance
7.4.2 Traffic signal control
7.4.3 Traffic flow optimization
7.4.4 Rail and maritime transportation
7.4.5 Data communications, computing, and networking
7.4.6 DRL applications for cybersecurity
7.5 Deep reinforcement learning applications: automated driving systems
7.5.1 Motion planning
7.5.2 Lateral control
7.5.3 Safety
7.6 Challenges and future directions
7.6.1 Transferability to real-world applications
7.6.2 Representation of traffic environment
7.6.3 Formulating reward functions
7.6.4 Multi-agent DRL in CAV environment
7.6.5 Partial state observability
References
Part III: Deep learning for vehicle control
Chapter 8: Vehicle emission control on road with temporal traffic information using deep reinforcement learning
8.1 Introduction
8.2 Related work
8.3 Overview
8.3.1 Preliminary
8.3.2 Traffic data insight
8.3.3 Problem formulation
8.4 Methodology
8.4.1 Framework
8.4.2 EFRL model
8.5 Experiments
8.5.1 Data and setup
8.5.2 Baselines and metric
8.5.3 Results
8.6 Conclusion
References
Chapter 9: Load prediction of an electric vehicle charging pile
9.1 Introduction
9.2 Charging load characteristic analysis of electric vehicles
9.3 Quantile regression model of dilated causal convolutional
9.3.1 Dilated causal convolutional
9.3.2 Kernel density estimation
9.3.3 Dilated causal convolutional quantile regression
9.3.4 Model evaluation index
9.3.5 Example simulation based on python
9.4 Spatio-temporal dynamic load prediction of charging pile load based on deep learning
9.4.1 Spatio-temporal dynamic load prediction of the charging pile
9.4.2 Spatio-temporal dynamic load matrix construction
9.4.3 Spatio-temporal convolutional networks model
9.4.4 Spatio-temporal dynamic load forecasting based on dilated causal convolution-2D
9.4.5 Spatio-temporal dynamic load forecasting based on Spatio-temporal neural network
9.4.6 Example simulation based on python
9.5 Conclusions
References
Chapter 10: Deep learning for autonomous vehicles: A vision-based approach to self-adapted robust control
10.1 Introduction
10.2 References selection via deep learning image processing
10.2.1 CNN analytic outcomes as control references
10.2.2 Experimental data
10.2.3 Multi-objective evaluation
10.2.4 Control state variable
10.3 Robust control design
10.3.1 System identification
10.3.2 Robust Linear Quadratic Regulator (RLQR)
10.3.3 H∞ Controller
10.4 Case study for hybrid controller
10.4.1 Simulation environment and problem objective
10.4.2 Machine learning design
10.4.2.1 Input and output
10.4.2.2 Reward function
10.4.2.3 Neural network design
10.4.2.4 Simulation technical details
10.4.3 Hybrid control design
10.4.3.1 Control reference selection
10.4.3.2 System identification and nominal LQR
10.4.3.3 Uncertainty estimation via evolutionary search
10.4.4 Performance evaluation
10.5 Conclusions
Note
References
Part IV: DL for information management
Chapter 11: A natural language processing-based approach for automating IoT search
11.1 Introduction
11.2 IoT search engine
11.2.1 Architecture
11.2.2 Key components
11.2.3 Research challenges
11.3 NLP-based query processing
11.3.1 Design rationale
11.3.2 Basic components of NLP
11.3.3 NLP tools
11.3.4 Comparison of NLTK and spaCy
11.4 The ACQUISE approach
11.4.1 Baseline strategy
11.4.2 Enhanced static strategy
11.4.3 Enhanced dynamic strategy
11.5 Performance evaluation
11.5.1 Methodology
11.5.2 Results
11.6 Discussion
11.6.1 Machine learning
11.6.2 Protocols and algorithms
11.6.3 Security and privacy
11.7 Related work
11.8 Final remarks
Acknowledgement
References
Chapter 12: Toward incentive-compatible vehicular crowdsensing: A reinforcement learning-based approach
12.1 Introduction
12.2 Edge-assisted vehicular crowdsensing
12.2.1 Architecture design
12.2.2 Workflow
12.3 Incentive mechanism for vehicle recruitment
12.3.1 Stackelberg game
12.3.2 Strategy of the SSP
12.3.3 Strategies of vehicles
12.4 Case study
12.5 Conclusions
Appendix A
References
Chapter 13: Sub-signal detection from noisy complex signals using deep learning and mathematical morphology
13.1 Introduction
13.2 LSTM-RNN and mathematical morphology-based algorithm to detect sub-signals from noisy complex signals
13.2.1 Data preparation and pre-processing
13.2.2 LSTM-RNN local sub-signal learning
13.2.3 Mathematical morphological global sub-signal detection
13.3 Experimental results
13.4 Conclusion
Acknowledgment
Notes
References
Part V: Miscellaneous
Chapter 14: The basics of deep learning algorithms and their effect on driving behavior and vehicle communications
14.1 Introduction
14.2 Basics of deep learning algorithms and supervised learning
14.2.1 Linear regression and logistic regression
14.2.2 Artificial Neural Networks
14.2.3 Convolutional Neural Networks
14.2.4 Recurrent Neural Networks
14.2.5 Deep learning architectures
14.3 Deep unsupervised and semi-supervised learning
14.3.1 Restricted Boltzmann Machines and deep belief nets
14.3.2 Autoencoders & variational autoencoders
14.3.3 Generative adversarial networks
14.3.4 Transformers
14.4 Hyperparameters, pre-processing and optimization
14.4.1 Data augmentation and transfer learning
14.4.2 Weight initialization, activation functions and optimizers
14.4.3 Training time, pre-processing and architectural refinements
14.5 Applications of deep learning in driving behavior analysis and vehicle communication
14.6 Conclusions
References
Chapter 15: Integrated simulation of deep learning, computer vision and physical layer of UAV and ground vehicle networks
15.1 Introduction
15.2 Applications that can benefit from CAVIAR simulations
15.2.1 Simulation of UAV-enabled AI/ML
15.2.2 Beam-selection for V2I
15.3 Multi-domain integrated simulators
15.3.1 Wireless channel generation with Raymobtime
15.3.2 Caviar simulations
15.4 Simulations results
15.4.1 Beam selection for V2I with lidar as input
15.4.2 In-loop CAVIAR simulation of a computer vision application
15.4.3 Impact of 3D model accuracy on wireless channels
15.5 Conclusions
Acknowledgments
Notes
References