Intelligent Road Vehicles examines specific aspects of intelligent vehicles such as enabling technologies, human factors and an analysis of social and economic impacts. The book is an invaluable resource for those pursuing deeper knowledge in the intelligent vehicles field, providing readers with an idea of current and future technologies, current projects and developments and the future of intelligent vehicles. Intelligent road vehicles are becoming a challenging area of research worldwide. Apart from the final applications and systems in vehicles, there are many enabling technologies that should be introduced. Communications and automation are two key areas for future automobiles. This book benefits from collaboration on the Thematic Network on Intelligent Vehicles led by Felipe Jimenez. Provides a general overview of different aspects related to intelligent road vehicles (sensors, applications, communications, automation, human factors, etc.) Addresses the different components and building blocks of intelligent vehicles in a single, comprehensive reference Explains how sensors are interpreted, including how different sensor readings are fused Addresses issues involved with avoiding collisions and other factors such as pot holes, unclear road lines or markings, and unexpected weather conditions

lgrsnf/Intelligent Vehicles - Enabling Technologies and Future Developments.pdf

zlib/Engineering/Felipe Jimenez/Intelligent Vehicles: Enabling Technologies and Future Developments_13374783.pdf

Felipe Jiménez

Elsevier Science & Technology Books

Academic Press, Incorporated

Morgan Kaufmann Publishers

Brooks/Cole

Elsevier Ltd., Oxford, United Kingdom, 2018

United States, United States of America

1, 2017

lg3005442

{"isbns":["0128128003","9780128128008"],"last_page":504,"publisher":"Butterworth-Heinemann"}

Front-matter_2018_Intelligent-Vehicles
Intelligent Vehicles
Copyright_2018_Intelligent-Vehicles
Copyright
List-of-Contributors_2018_Intelligent-Vehicles
List of Contributors
Preface_2018_Intelligent-Vehicles
Preface
Chapter-1---Introduction_2018_Intelligent-Vehicles
1 Introduction
1.1 Intelligent Transport Systems (ITS)
1.2 Early Initiatives
1.2.1 Europe
1.2.2 United States
1.2.3 Japan
1.3 Services
1.3.1 Provision of Information to the User
1.3.2 Traffic Management
1.3.3 Freight Transportation Operation
1.3.4 Public Transport Operation
1.3.5 Electronic Payment
1.3.6 Emergencies
1.4 Intelligent Vehicles
1.5 Book Structure
References
Further Reading
Chapter-2---Environmental-Perception-for-Intelligent-_2018_Intelligent-Vehic
2 Environmental Perception for Intelligent Vehicles
2.1 Vision-Based Road Information
2.1.1 Environmental Variability
2.1.2 Lane Detection
2.1.2.1 Preprocessing
2.1.2.2 Postprocessing
2.1.3 Traffic Signs Recognition
2.1.3.1 Sign Detection
2.1.3.2 Sign Classification
2.1.4 Commercial Systems
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
2.4.1.1.1 Magnetic Loops
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
2.4.1.2.6 Video Cameras
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 Data Fusion
2.5.1 Data Fusion Levels
2.5.1.1 Data Fusion Definition
2.5.2 Architectures
2.5.3 Data Fusion in Intelligent Transport Systems
2.5.3.1 Other Approaches
References
Further Reading
Chapter-3---Vehicular-Communications_2018_Intelligent-Vehicles
3 Vehicular Communications
3.1 Standardization in Vehicular Communications
3.1.1 Introduction
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
3.1.2.2.1 IEEE WAVE
3.1.2.2.2 CEN DSRC
3.1.2.2.3 ETSI ITS G5
3.1.2.2.4 ISO CALM M5
3.1.2.2.5 IEEE 802.11
3.1.2.2.6 IEEE 802.11p
3.1.2.2.7 IEEE 802.16 WiMAX
3.1.2.3 The ISO CALM Network Layer
3.1.2.3.1 IETF IPv4
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
3.1.2.3.5 GeoNetworking
3.1.3 Vehicular Communications in a Mobile Communications Scenario
3.1.4 Conclusions
3.2 Technology
3.2.1 Introduction
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
3.2.3.4 RFID
3.2.3.5 Bluetooth
3.2.4 Hybrid Communication Approach
3.2.5 Services
3.2.6 Security and Privacy
3.2.7 Interoperability
References
Related standards
Chapter-4---Positioning-and-Digital-Maps_2018_Intelligent-Vehicles
4 Positioning and Digital Maps
4.1 Positioning Based Systems for Intelligent Vehicles
4.1.1 Definitions
4.1.2 Location Based Services and Applications Based on Position
4.2 GNSS-Based Positioning
4.2.1 Motivation, Requirements and Working Principles
4.2.1.1 Motivation
4.2.1.2 Requirements
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
4.3.2.1 Principle
4.3.2.2 Vehicle Models
4.3.2.3 Architecture
4.3.2.4 Fusion Techniques
4.4 Digital Maps
4.4.1 Importance and Utility
4.4.2 Specifications
4.4.3 Digital Map Development
4.4.4 Map Quality Assessment
4.4.5 Map-Matching
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
4.5.2 Wireless Networks
4.5.3 RFID
References
Chapter-5---Big-Data-in-Road-Transport-and-Mobility-R_2018_Intelligent-Vehic
5 Big Data in Road Transport and Mobility Research
5.1 Data and Information Sources
5.2 Data Preprocessing
5.2.1 Feature Engineering
5.2.2 Dimensionality Reduction
5.3 Data Normalization
5.3.1 Data Cleaning
5.3.2 Formats and Standards
5.3.3 Ontologies
5.4 Supervised Learning
5.4.1 Predictive Versus Descriptive
5.4.2 Classification Versus Regression
5.4.3 Learners
5.4.4 Real Time Application
5.4.5 Concept Drift Handling
5.5 Nonsupervised Learning
5.6 Processing Architectures
5.7 Applications
5.7.1 Transport Demand Modeling
5.7.2 Short-Term Traffic State Prediction
5.7.3 Planning/Routing
References
Further Reading
Chapter-6---Driver-Assistance-Systems-and-Safety-Sys_2018_Intelligent-Vehicl
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
6.2.2 Classification
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
References
Chapter-7---Cooperative-Systems_2018_Intelligent-Vehicles
7 Cooperative Systems
7.1 Introduction
7.2 C-ITS Framework
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 Services
7.3.1 Introduction
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
7.4.1 Technical Issues
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
7.5.3.2 C-ITS Platform
7.5.3.3 The Amsterdam Group
7.5.3.4 CODECS—COoperative ITS DEployment Coordination Support
7.5.3.5 C-Roads Platform
7.5.3.6 Cooperative ITS Corridor
7.5.3.7 Intercor—North Sea–Mediterranean Corridor
7.5.3.8 SCOOP@F (France)
7.5.3.9 SISCOGA Corridor (Spain)
7.6 Next Steps
References
Further Reading
Chapter-8---Automated-Driving_2018_Intelligent-Vehicles
8 Automated Driving
8.1 Fundamentals
8.2 Technology Bricks
8.2.1 Control Architectures
8.2.2 Situation Awareness and Risk Assessment
8.2.3 Decision Making
8.2.3.1 Simulation and Software tools for IDMS
8.2.4 Driver–Vehicle Interaction
8.2.5 Motion Planning
8.2.5.1 Path Planning
8.2.5.1.1 Costmap Generation
8.2.5.1.2 Global Planner
8.2.5.1.3 Local Planner
8.2.5.2 Speed Planning
8.2.6 Vehicle Control
8.2.6.1 Longitudinal Motion Control
8.2.6.2 Lateral Motion Control
8.3 Cooperative Automated Driving
8.3.1 Platooning
8.3.2 Urban Road Transport
8.4 Verification and Validation
8.5 Main Initiatives and Applications
8.5.1 Prototypes
8.5.1.1 Relevant Prototypes at International Level
8.5.1.2 Relevant Prototypes in Spain
8.5.2 Projects
8.5.3 Special Applications
8.6 Socioregulatory Aspects
8.6.1 Legal Pathways
8.6.1.1 General Framework: Vienna and Amsterdam
8.6.1.2 Legal Framework and Regulation About Autonomous Vehicles
8.6.2 Ethical Aspects
References
Chapter-9---Human-Factors_2018_Intelligent-Vehicles
9 Human Factors
Subchapter 9.1 Human Driver Behaviors
9.1.1 Introduction
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
9.1.3.5 Hybrid Measures
9.1.4 Driving Style Classification
9.1.4.1 Discrete Classes
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 Level 1
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 Level 2
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 Level 3
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
9.1.7.4 Level 4
9.1.7.5 Applications for Consumption Efficiency
References
Subchapter 9.2 User Interface
9.2.1 Introduction: Feedback Channels
9.2.1.1 Visual Channel
9.2.1.2 Acoustic Channel
9.2.1.3 Speech Recognition
9.2.1.4 Haptic Output
9.2.1.5 Multimodality
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
References
Chapter-10---Simulation-Tools_2018_Intelligent-Vehicles
10 Simulation Tools
Subchapter 10.1 Driving Simulators
10.1.1 Introduction
10.1.2 Architecture of Driving Simulators
10.1.3 Applications
References
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
10.2.3.1 CORSIM
10.2.3.2 MATSIM
10.2.3.3 AIMSUM2
10.2.4 Open Traffic Simulation: SUMO
10.2.5 Future Trends and Hopes
References
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
References
Chapter-11---The-Socioeconomic-Impact-of-the-Intelligent-V_2018_Intelligent-
11 The Socioeconomic Impact of the Intelligent Vehicles: Implementation Strategies
11.1 Introduction
11.2 From Connected to Autonomous Vehicle
11.3 Social Issues
11.3.1 Acceptance of the Innovations
11.3.2 Safety
11.3.3 Effects on Employment
11.4 Legal Issues
11.4.1 Liability/Insurance
11.4.2 Test and Validation
11.5 Privacy and Hacking
11.5.1 Privacy
11.6 Hacking
11.7 Economic Aspects
11.7.1 Congestion
11.7.2 Fuel
11.7.3 Infrastructure Costs
11.7.4 Vehicle Cost
11.7.5 Insurance Costs
11.8 Liveability
11.9 Conclusions
References
Further Reading
Chapter-12---Future-Perspectives-and-Research-Areas_2018_Intelligent-Vehicle
12 Future Perspectives and Research Areas
12.1 Introduction
12.2 Current Trends
12.3 Current Research Areas
12.4 Main Expected Technological Leaps
12.5 Other Expected and/or Necessary Changes
12.6 Conclusions
References
Index_2018_Intelligent-Vehicles
Index

Front Cover -- Intelligent Vehicles -- Copyright Page -- Contents -- List of Contributors -- Preface -- 1 Introduction -- 1.1 Intelligent Transport Systems (ITS) -- 1.2 Early Initiatives -- 1.2.1 Europe -- 1.2.2 United States -- 1.2.3 Japan -- 1.3 Services -- 1.3.1 Provision of Information to the User -- 1.3.2 Traffic Management -- 1.3.3 Freight Transportation Operation -- 1.3.4 Public Transport Operation -- 1.3.5 Electronic Payment -- 1.3.6 Emergencies -- 1.4 Intelligent Vehicles -- 1.5 Book Structure -- References -- Further Reading -- I. Enabling Technologies -- 2 Environmental Perception for Intelligent Vehicles -- 2.1 Vision-Based Road Information -- 2.1.1 Environmental Variability -- 2.1.2 Lane Detection -- 2.1.2.1 Preprocessing -- 2.1.2.2 Postprocessing -- 2.1.3 Traffic Signs Recognition -- 2.1.3.1 Sign Detection -- 2.1.3.2 Sign Classification -- 2.1.4 Commercial Systems -- 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 -- 2.4.1.1.1 Magnetic Loops -- 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 -- 2.4.1.2.6 Video Cameras

__Intelligent Road Vehicles__ examines specific aspects of intelligent vehicles such as enabling technologies, human factors and an analysis of social and economic impacts. The book is an invaluable resource for those pursuing deeper knowledge in the intelligent vehicles field, providing readers with an idea of current and future technologies, current projects and developments and the future of intelligent vehicles.
Intelligent road vehicles are becoming a challenging area of research worldwide. Apart from the final applications and systems in vehicles, there are many enabling technologies that should be introduced. Communications and automation are two key areas for future automobiles. This book benefits from collaboration on theThematic Network on Intelligent Vehicles led by Felipe Jimenez.

2021-05-10