Chapter
1.3.1 Provision of Information to the User
1.3.3 Freight Transportation Operation
1.3.4 Public Transport Operation
2 Environmental Perception for Intelligent Vehicles
2.1 Vision-Based Road Information
2.1.1 Environmental Variability
2.1.3 Traffic Signs Recognition
2.1.3.2 Sign Classification
2.2 Vision-Based Perception
2.2.1 Vision-Based Object Detection and Semantic Segmentation
2.2.2 Onboard Vision-Based Object Detection
2.2.3 Onboard Vision-Based Semantic Segmentation
2.2.4 Onboard Vision Based on Deep Learning
2.3 Lidar-Based Perception
2.3.1 Surroundings Recognition
2.3.1.1 Obstacles Detection
2.3.1.2 Path Boundaries Detection
2.4 Sensing From the Infrastructure
2.4.1 Autonomous Traffic Sensors
2.4.1.1 Intrusive Sensors
2.4.1.1.2 Pneumatic Tubes
2.4.1.1.3 Piezoelectric Sensors
2.4.1.1.4 Fiber Optic Sensors
2.4.1.1.5 Geomagnetic Sensors
2.4.1.1.6 Wireless Sensor Networks (Motes)
2.4.1.2 Nonintrusive Sensors
2.4.1.2.1 Microwave Radars
2.4.1.2.2 Laser Sensors (Active Infrareds)
2.4.1.2.3 Ultrasonic Sensors
2.4.1.2.4 Passive Infrared Sensors
2.4.1.2.5 Acoustic Sensors
2.4.1.3 Summary of Strengths and Weaknesses of Autonomous Traffic Sensors
2.4.2 Dependant Traffic Sensors
2.4.2.1 Vehicle Identification by RFID (RFID Radio Frequency Identification)
2.4.2.1.1 Onboard Equipment (Tag)
2.4.2.1.2 Equipment in the Infrastructure (TRX)
2.4.2.2 Bluetooth Sensing
2.4.3 Conclusions and Recommendations
2.5.1.1 Data Fusion Definition
2.5.3 Data Fusion in Intelligent Transport Systems
3 Vehicular Communications
3.1 Standardization in Vehicular Communications
3.1.2 The ISO CALM Framework
3.1.2.1 The ISO CALM Communications Reference Architecture
3.1.2.2 The ISO CALM Access Media
3.1.2.2.7 IEEE 802.16 WiMAX
3.1.2.3 The ISO CALM Network Layer
3.1.2.3.2 IETF/ISO IPv6 Networking and Mobility
3.1.2.3.3 Mobility in IPv6 Networks
3.1.2.3.4 IEEE 1609.3 WAVE WSMP
3.1.3 Vehicular Communications in a Mobile Communications Scenario
3.2.2 Reference Architecture
3.2.3 Operative Technologies
3.2.3.1 Dedicated Short Range Communications
3.2.3.2 3/4G Mobile Telephony.
3.2.3.3 5G Mobile Telephony
3.2.4 Hybrid Communication Approach
3.2.6 Security and Privacy
4 Positioning and Digital Maps
4.1 Positioning Based Systems for Intelligent Vehicles
4.1.2 Location Based Services and Applications Based on Position
4.2 GNSS-Based Positioning
4.2.1 Motivation, Requirements and Working Principles
4.2.1.3 Working Principle
4.2.2 Performance Parameters
4.2.3 Satellite Positioning in ITS Domain and Applications
4.2.4 Future of GNSS in ITS
4.3 GNSS Aiding and Hybridized Positioning Systems
4.3.1 Technologies for GNSS-Aided Positioning and Navigation
4.3.2 GNSS/DR Positioning
4.3.2.4 Fusion Techniques
4.4.1 Importance and Utility
4.4.3 Digital Map Development
4.4.4 Map Quality Assessment
4.4.6 Map-Assisted GNSS Positioning
4.5 Alternatives to GNSS Positioning
4.5.1 Visual Odometry as Vehicle’s Movement Estimator
4.5.1.1 Visual Odometry Algorithms Using Computer Vision
4.5.1.2 Visual Odometry Algorithms Using LIDAR
5 Big Data in Road Transport and Mobility Research
5.1 Data and Information Sources
5.2.1 Feature Engineering
5.2.2 Dimensionality Reduction
5.3.2 Formats and Standards
5.4.1 Predictive Versus Descriptive
5.4.2 Classification Versus Regression
5.4.4 Real Time Application
5.4.5 Concept Drift Handling
5.5 Nonsupervised Learning
5.6 Processing Architectures
5.7.1 Transport Demand Modeling
5.7.2 Short-Term Traffic State Prediction
6 Driver Assistance Systems and Safety Systems
6.1 Integrated Safety Model
6.2 Systems for Improving Driving Task
6.2.1 Assistance Systems Aim
6.3 Electronic Aids for Reducing Accidents Consequences
6.3.1 Secondary Safety Systems
6.3.2 Interaction Between Primary and Secondary Safety Systems
6.3.3 Tertiary Safety Systems
6.4 Future Evolution of Assistance and Safety Systems
7.2.1 General Architecture
7.2.2 Support Technologies
7.2.3 Public Land Mobile Networks (Cellular Networks)
7.2.4 ITS G5 (Vehicular Wi-Fi)
7.2.5 Standardization Level
7.3.2 Systems Oriented to Information Provision
7.3.3 Systems Oriented to Improve Safety
7.3.4 Systems Oriented to Improve Efficiency
7.4 Challenges Toward Deployment
7.4.2 Implementation Issues
7.5 Main Related Initiatives at European Level
7.5.1 Interurban Mobility Pilots
7.5.1.1 DRIVE C2X—DRIVing Implementation and Evaluation of C2X Communication Technology in Europe
7.5.1.2 FOTsis—European Field Operational Test on Safe, Intelligent, and Sustainable Road Operation
7.5.2 Urban Mobility Pilots
7.5.2.1 COMPASS4D—Cooperative Mobility Pilot on Safety and Sustainability
7.5.2.2 CO-GISTICS—Cooperative Logistics for Sustainable Mobility of Goods
7.5.3 Collaborative Platforms and Supporting Initiatives
7.5.3.1 Car2Car Communication Consortium
7.5.3.3 The Amsterdam Group
7.5.3.4 CODECS—COoperative ITS DEployment Coordination Support
7.5.3.6 Cooperative ITS Corridor
7.5.3.7 Intercor—North Sea–Mediterranean Corridor
7.5.3.9 SISCOGA Corridor (Spain)
8.2.1 Control Architectures
8.2.2 Situation Awareness and Risk Assessment
8.2.3.1 Simulation and Software tools for IDMS
8.2.4 Driver–Vehicle Interaction
8.2.5.1.1 Costmap Generation
8.2.6.1 Longitudinal Motion Control
8.2.6.2 Lateral Motion Control
8.3 Cooperative Automated Driving
8.3.2 Urban Road Transport
8.4 Verification and Validation
8.5 Main Initiatives and Applications
8.5.1.1 Relevant Prototypes at International Level
8.5.1.2 Relevant Prototypes in Spain
8.5.3 Special Applications
8.6 Socioregulatory Aspects
8.6.1.1 General Framework: Vienna and Amsterdam
8.6.1.2 Legal Framework and Regulation About Autonomous Vehicles
Subchapter 9.1 Human Driver Behaviors
9.1.2 Driving Style: Definitions
9.1.3 Measures for Driving Style Modeling
9.1.3.1 Driver Biological Measures
9.1.3.2 Driver Physical Measures
9.1.3.3 Vehicle Dynamics Measures
9.1.3.4 Sociodemographic Measures
9.1.4 Driving Style Classification
9.1.4.2 Continuous Scoring
9.1.5 Algorithms for Driving Style Modeling
9.1.5.1 Unsupervised Learning Techniques
9.1.5.2 Supervised Learning Techniques
9.1.5.3 General Observations
9.1.6 Datasets for Driving Style Modeling
9.1.7 Applications for Intelligent Vehicles
9.1.7.1.1 Driver–Vehicle Interface (DVI)
9.1.7.1.2 ADAS Performance Enhancement
9.1.7.1.3 Trust and Use of the Technology
9.1.7.2.1 Driver–Vehicle Interface
9.1.7.2.2 Driver as Supervisor
9.1.7.2.3 Trust and Use of the Technology
9.1.7.3.1 Driver–Vehicle Interface
9.1.7.3.2 Trust and Use of the Technology
9.1.7.3.3 Driver Skill Over Time
9.1.7.5 Applications for Consumption Efficiency
Subchapter 9.2 User Interface
9.2.1 Introduction: Feedback Channels
9.2.1.3 Speech Recognition
9.2.2 Cognitive Load and Work Load
9.2.3 Information Classification and Prioritization
9.2.4 Implementation Issues
9.2.5 Guidelines and Standards
Subchapter 10.1 Driving Simulators
10.1.2 Architecture of Driving Simulators
Subchapter 10.2 Traffic Simulation
10.2.1 What is Traffic Simulation and Why is it Needed
10.2.2 Classic Traffic Simulation Paradigms
10.2.2.1 Macroscopic Simulation
10.2.2.2 Microscopic Simulation
10.2.2.3 Mesoscopic Simulation
10.2.3 Some (Traditional) Simulation Frameworks
10.2.4 Open Traffic Simulation: SUMO
10.2.5 Future Trends and Hopes
Subchapter 10.3 Data for Training Models, Domain Adaptation
10.3.1 Training Data and Ground Truth
10.3.2 Virtual Worlds and Domain Adaptation
11 The Socioeconomic Impact of the Intelligent Vehicles: Implementation Strategies
11.2 From Connected to Autonomous Vehicle
11.3.1 Acceptance of the Innovations
11.3.3 Effects on Employment
11.4.1 Liability/Insurance
11.4.2 Test and Validation
11.7.3 Infrastructure Costs
12 Future Perspectives and Research Areas
12.3 Current Research Areas
12.4 Main Expected Technological Leaps
12.5 Other Expected and/or Necessary Changes
Intelligent Vehicles
：Enabling Technologies and Future Developments
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