Advanced Driver Intention Inference: Theory and Design

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Advanced Driver Intention Inference: Theory and Design
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List of Abbreviations
Abstract
	Keywords
1. Introduction
	What Is Human Intention?
	Driver Intention Classification
	Studies Related to Driver Intention Inference
	Conclusion
	Chapter Outlines
	References
2. State of the Art of Driver Lane Change Intention Inference
	Driver Intention Inference Background
	Lane Change Maneuver Analysis—an Exemplary Scenario
	Lane Change Assistance Systems
		Lane Departure Warning
		Lane Keeping Assistance
		Lane Change Assistance
		Limitations and Emerging Requirement
	Human Intention Mechanisms
	Driver Intention Classification
		Timescale-Based Driver Intention Classification
		Direction-Based Driver Intention Classification
		Task-Based Driver Intention Classification
	Driver Intention Inference Methodologies
		The Architecture of Driver Intention Inference System
		Inputs for Driver Intention Inference System
			Traffic context
			Vehicle dynamics
			Driver behaviors
			Electroencephalography
	Algorithms for Driver Intention Inference
		Generative Model
		Discriminative Model
		Cognitive Model
		Deep Learning Methods
	Evaluation of Driver Intention Inference System
		Detection Accuracy
		Prediction Horizon
	Challenges and Future Works
		Design Next-Generation Advanced Driver Assistance Systems
			Integration of driver monitoring systems
			The need for a comprehensive environment model
			Design cognitive model for driver intention
		Situation Awareness and Interaction-Aware Modeling
			Situation awareness modeling
			Interaction-aware modeling
		Autonomous Driving
		Parallel Driver Intention Inference System
	Conclusions
	References
3. Road Perception in Driver Intention Inference System
	Introduction
	Vision-Based Lane Detection Algorithm
		General Lane Detection Procedure
	Conventional Image-Processing-Based Algorithms
	Machine Learning-Based Algorithms
	Integration Methodologies for Road Perception
		Integration Methods—Introduction
			Algorithm level integration
			System level integration
				Vehicle detection
				Vehicle detection
				System level fusion
				System level fusion
			Sensor level integration
	Evaluation Methodologies for Vision-Based Road Perception Systems
		Influential Factors for Lane Detection Systems
		Offline Evaluation
		Online Evaluation
		Evaluation Metrics
	Discussion
		Current Limitations and Challenges
		Applying the Parallel Theory to Road Perception Systems
	Conclusion
	References
4. Design of Integrated Road Perception and Lane Detection System for Driver Intention Inference
	Road Detection
		Introduction
		Related Works
		Data Processing
			KITTI road dataset
			Lidar camera calibration
		Model construction
			Fusion network architectures
			Model optimization
		Experimental Results
		Conclusion
	Lane Detection
		Introduction
		Popular lane detection techniques
		Lane Detection System Setup
		Algorithm-Level Integrated Lane Detection
			Lane detection using Sobel filter and Hough transform method
				Image processing
				Image processing
				Edge extraction
				Edge extraction
				Hough transform
				Hough transform
			Lane detection using Gaussian mixture model and RANSAC method
				GMM-based feature extraction
				GMM-based feature extraction
				RANSAC model fitting
				RANSAC model fitting
			Lane tracking with Kalman filter
			Lane sampling and voting for lane recognition
				Lane color detection
				Lane color detection
				Lane-type detection
				Lane-type detection
		Lane Algorithms Integration and Evaluation
			Integration of lane detection algorithms
			Integration of lane detection algorithms
		Experimental Results
			Experimental results
		Discussion
		Conclusions
	References
5. Driver Behavior Recognition in Driver Intention Inference Systems
	Introduction
		Machine-Learning Methods for Human Activities Recognition—A Case Study on Activity Recognition
			Initial data processing
			Data dimension reduction
			Support vector machine method
			Hidden Markov model method
	Feature Engineering in Driver Behavior Recognition
		Driver Behavior Overview
		Driver Head Pose Estimation
		Driver Head Pose Estimation Using Head Features
		Head Pose Estimation Using Random Forest
		Driver Body Detection
	Driver Behaviors Recognition Experimental Design and Data Analysis
		Overall System Architecture
		Inner-Vehicle Experiment Setup and Data Collection
	Data Processing
	Kinect Sensor-Based Head Rotation Data Calibration
		Noise Removal and Data Smoothing
	Tasks Identification Algorithms Design
		Feature Importance Evaluation Using Random Forest and Maximal Information Coefficient
			Random forests for feature importance estimation
			Maximal information coefficient for feature importance estimation
			Comparison of the feature importance prediction
		Feedforward Neural Network for Driver Behavior Classification
	Experiment Results and Analysis
		Behavior Recognition Results
		Feature Evaluation for Behavior Classification Performance
	Discussion and Future Work
	Conclusions
	References
6. Application of Deep Learning Methods in Driver Behavior Recognition
	Introduction
	Experiment and Data Collection
	End-to-End Recognition Based on Deep Learning Algorithm
		Image Preprocessing and Segmentation
		Model Preparation and Transfer Learning
	Experiment Results and Analysis
		The Impact of GMM Image Segmentation on Driving Tasks Recognition
		Visualization of Deep CNN Models
		Results Comparison Between Transfer Learning and Feature Extraction
		Driver Distraction Detection Using Binary Classifier
	Discussion
		Transfer Learning Performance
		Real-Time Application
	Conclusions
	References
7. Longitudinal Driver Intention Inference
	Braking Intention Recognition Based on Unsupervised Machine Learning Methods
		Unsupervised Learning Background
			K-means
			Gaussian mixture model
		Experiment Design
			Case study vehicle
			Driving cycle
			Parameter selection
			Unsupervised clustering training process
		Experiment Results
			K-means result
			Gaussian mixture model result
		Discussion
		Conclusions
	Levenberg-Marquardt Backpropagation for State Estimation of a Safety-Critical Cyber-Physical System
		Multilayer Artificial Neural Network Architecture
			System architecture
			Multilayer feedforward neural network
		Standard Backpropagation Algorithm
		Levenberg-Marquardt Backpropagation
	Hybrid-Learning-Based Classification and Quantitative Inference of Driver Braking Intensity
		Hybrid-Learning-Based Architecture and Algorithms
			High-Level architecture of the Proposed algorithms
			Classification of braking intention level using gaussian mixture model
			Braking intention classification using random forest
			Brake Pressure Estimation Based on Artificial Neural Network
		Experimental Testing and Data Preprocessing
			Experiment design
			Experimental vehicle with brake blending system
			Data collection and processing
			Feature selection and model training
		Experiment Results and Analysis
			Labeling result of braking intensity level using Gaussian mixture model
			Random forest-based classification results of braking intensity level
			Estimation result of braking pressure based on artificial neural network
		Discussions
			Fault classification of the intensive braking
		Performance With a Reduced Order Feature Vector
		Conclusions
	References
8. Driver Lane-Change Intention Inference
	Host Driver Intention Inference
		Introduction
		The Framework of Comprehensive Driver Intention Recognition
			Driver intention inference framework
			Lane-change intention formulation
		Methodologies in Driver Lane-Change Intention Inference
			Experimental setup and naturalistic highway data collection
			Traffic context and vehicle dynamic features
			Driver behavioral features
		Algorithms in Driver Lane-Change Intention Inference
			Support vector machine
				A case study of SVM in driver workload estimation
				A case study of SVM in driver workload estimation
			Hidden Markov model
			Recurrent neural network
			Long short-term memory
		Performance Evaluation
			Driver lane-change maneuver analysis
			Lane-change intention inference results
		Discussions and Perspectives
		Conclusions
	Leading Vehicle Intention Inference-Trajectory Prediction
		Introduction
		Driving Style Recognition Based on GMM
		Joint Feature Learning and Personalized Trajectory Prediction
			Recurrent neural network and LSTM
			Joint time-series model construction
		Experimental Results
			Evaluation metrics and baselines
			Performance evaluation
		Conclusions
	Mutual Understanding-Based Driver–Vehicle Collaboration
		Introduction
		Literature Review
	References
9. Conclusions, Discussions, and Directions for Future Work
	Integrated Road Detection Toward Robust Traffic Context Perception
		Algorithm Limitation
		Directions for Future Work
	Driving Activity Recognition and Secondary Task Detection
		Algorithm Limitation
		Directions for Future Work
	Driver Lane Change Intention Inference Based on Traffic Context and Driver Behavior Recognition
		Algorithm Limitation
		Directions for Future Work
	Driver Braking Intention Recognition and Braking Intensity Estimation Based on the Braking Style Classification
		Algorithm Limitation
		Directions for Future Work
	Conclusions and Final Discussions
Index
	A
	B
	C
	D
	E
	F
	G
	H
	I
	J
	K
	L
	M
	N
	O
	P
	R
	S
	T
	V
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