The automotive industry is transforming to a greater degree than has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles.
What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to answer these questions, educating the reader with the information required to make informed predictions of how and when CAVs will impact their lives.
Topics covered include: History of Connected and Automated Vehicles, Localization, Connectivity, Sensor and Actuator Hardware, Computer Vision, Sensor Fusion, Path Planning and Motion Control, Verification and Validation, and Outlook for future of CAVs.
Author(s): Jeffrey Wishart, Yan Chen, Steven Como, Narayanan Kidambi, Duo Lu, Yezhou Yang
Publisher: SAE International
Year: 2022
Language: English
Pages: 272
City: Warrendale
Front Cover
Title Page
Copyright Page
Contents
Foreword
Preface
CHAPTER 1 Introduction and History of Connected and Automated Vehicles
CAV History and Origins
CAVs Today
Current Status
Positive Impacts
Negative Impacts
Societal Impacts
CAV Taxonomy and Definitions
Scope of the Book
References
CHAPTER 2 Localization
Localization Need
Mapping
Sensing
Localization Challenges
Localization Techniques
References
CHAPTER 3 Connectivity
Connectivity Defined
Connectivity Origins
Motivations: The Case for Connectivity
Motivations: Crash Avoidance
Motivations: Mobility Enhancement
Motivations: Environmental Impact
Connectivity Case Study: ACC versus CACC
Connectivity Technology
Connectivity Technology: DSRC
Connectivity Technology: C-V2X
Connectivity Technology: DSRC versus 5G
Connectivity Technology: CV Costs
Deployment Challenges versus Potential Benefits
References
CHAPTER 4 Sensor and Actuator Hardware
Principles and Characteristics of Sensor Hardware
Cameras
Definition and Description
Characteristics and Capabilities
RADAR
Definition and Description
Characteristics and Capabilities
LIDAR
Definition and Description
Working Principles
Types of LIDAR
Characteristics
Ultrasonic SONAR
Definition and Description
Characteristics
Other Important Sensors and Measurement Sources
HD Maps
High-Precision GPS
Sensor Suites
Overview
Sensor Suite: Functionality
Actuation and Propulsion Hardware
Steer-By-Wire
Rear-Wheel Steering
Electric Propulsion and In-Wheel Motors
References
CHAPTER 5 Computer Vision
Image and 3D Point Cloud
Image Formation
Image Processing
3D Point Cloud Formation
Deep Learning
Deep Neural Networks
Training Deep Neural Networks
Convolutional Neural Networks
Perception Tasks for CAV
Object Detection
Tracking
Segmentation
3D Depth Estimation
Perception System Development for CAV
Case Study: Google/Waymo CAV
Case Study: Tesla Autopilot
Case Study: CAROM
References
CHAPTER 6 Sensor Fusion
Sensor Fusion Definition and Requirements
Sensor Fusion Definition and CAV Data Sources
Sensor Fusion Requirements
Sensor Fusion Origins
JDL Model
Dasarathy Model
Boyd Control Loop
Intelligence Cycle
Omnibus Model
Object-Oriented Model
Frankel-Bedworth Architecture
Sensor Fusion Architecture
Centralized Fusion Architecture
Distributed Fusion Architecture
Hybrid Fusion Architecture
Sensor Interaction
Object and Situation Refinement Examples
Feature Extraction
Multi-Target Tracking
Evaluation Metrics
Sensor Fusion Applications: Active Safety Systems
Safe Speed and Distance
Lane-Keeping Assist
Intersection Navigation
Sensor Fusion Examples from Developmental CAVs
Waymo Sensor Fusion Applications
Lyft Self-Driving Platform with Level 5
Cruise Application of Late Fusion Techniques
Sensor Fusion Challenges
Lessons from Active Safety Systems and CAVs
Summary
References
CHAPTER 7 Path Planning and Motion Control
Definition and Hierarchy
Path Planning Objectives
Structured Environments
Deviations from Reference Paths
Unstructured Environments
Behavioral Decision-Making
Finite-State Machines
Probabilistic Methods
Learning-Based Methods
Behavioral Ethics
Moral Dilemmas
The Moral Machine Project
Regulatory Guidance
Trajectory Planning
Optimization-Based Methods
Graph Search and Sampling Methods
Motion Control
Kinematic Path Tracking
Trajectory Tracking
Model Predictive Control
Actuation and Actuator Delay
End-to-End Automated Driving
Summary and Outlook
References
CHAPTER 8 Verification and Validation
Definition
Design and Development Methods
Test and Validation Methods
Challenges
Test and Validation Methodology
Operational Safety Metrics
Test Methods
Simulation Testing
Closed Course Testing
Public Road Testing
Evaluation Methods
Evaluation Criteria
Safety Case
References
CHAPTER 9 Outlook
State of the Industry—Technology
State of the Industry—Deployments
State of the Industry—Regulation and Legislation
State Legislation and Regulation
Standards Activities
Public Perception
CAV-Related Research and Activities
Advanced Vehicle Tracking through Infrastructure—IAM and ASU
Deployment of Infrastructure-Based CAV Solutions—NAU and UofA
Scenario-Based Testing of CAVs—Mcity and UMTRI
CAV Interactions with Emergency Vehicles—VTTI
What’s Next?
References
Appendix B: Acronyms
About the Authors
Index
Back Cover
