On-Road Intelligent Vehicles

Motion Planning for Intelligent Transportation Systems
 
 
Butterworth-Heinemann (Verlag)
  • 1. Auflage
  • |
  • erschienen am 27. April 2016
  • |
  • 536 Seiten
 
E-Book | ePUB mit Adobe DRM | Systemvoraussetzungen
E-Book | PDF mit Adobe DRM | Systemvoraussetzungen
978-0-12-803756-0 (ISBN)
 

On-Road Intelligent Vehicles: Motion Planning for Intelligent Transportation Systems deals with the technology of autonomous vehicles, with a special focus on the navigation and planning aspects, presenting the information in three parts. Part One deals with the use of different sensors to perceive the environment, thereafter mapping the multi-domain senses to make a map of the operational scenario, including topics such as proximity sensors which give distances to obstacles, vision cameras, and computer vision techniques that may be used to pre-process the image, extract relevant features, and use classification techniques like neural networks and support vector machines for the identification of roads, lanes, vehicles, obstacles, traffic lights, signs, and pedestrians.

With a detailed insight into the technology behind the vehicle, Part Two of the book focuses on the problem of motion planning. Numerous planning techniques are discussed and adapted to work for multi-vehicle traffic scenarios, including the use of sampling based approaches comprised of Genetic Algorithm and Rapidly-exploring Random Trees and Graph search based approaches, including a hierarchical decomposition of the algorithm and heuristic selection of nodes for limited exploration, Reactive Planning based approaches, including Fuzzy based planning, Potential Field based planning, and Elastic Strip and logic based planning.

Part Three of the book covers the macroscopic concepts related to Intelligent Transportation Systems with a discussion of various topics and concepts related to transportation systems, including a description of traffic flow, the basic theory behind transportation systems, and generation of shock waves.


  • Provides an overall coverage of autonomous vehicles and Intelligent Transportation Systems
  • Presents a detailed overview, followed by the challenging problems of navigation and planning
  • Teaches how to compare, contrast, and differentiate navigation algorithms


The author was recently awarded with the First Prize in Best PhD Dissertation award by the IEEE Intelligent Transportation Systems Society at the 2014 IEEE Intelligent Transportation Systems Conference at Qingdao, China. The experience gained during the award conference was the chief motivation behind the decision of authoring a book in the domain. The author has already published in the IEEE Transactions on Intelligent Transportation Systems, the leading IEEE publication of the domain. The author has also been in close contacts with the people working in the same technology in India, from both the academic and the industry, and the increasing questions and concerns on navigation and planning clearly state the necessity of such a book. He is the author of three books and over 75 peer reviewed scientific papers, teaches a semester long course in the same topic, and he is an active reviewer of leading journals of the domain.
  • Englisch
  • Oxford
  • |
  • USA
Elsevier Science
  • 9,98 MB
978-0-12-803756-0 (9780128037560)
0128037563 (0128037563)
weitere Ausgaben werden ermittelt
  • Front Cover
  • On-Road Intelligent Vehicles
  • On-Road Intelligent Vehicles
  • Copyright
  • Contents
  • Acknowledgement
  • 1 - Introduction
  • 1.1 Introduction
  • 1.2 Why Autonomous Vehicles?
  • 1.2.1 Advantages
  • 1.2.2 Concerns
  • 1.3 A Mobile Robot on the Road
  • 1.4 Artificial Intelligence and Planning
  • 1.5 Fully Autonomous and Semi-Autonomous Vehicles
  • 1.6 A Network of Autonomous Vehicles
  • 1.7 Autonomous Vehicles in Action
  • 1.7.1 Entries From the DARPA Grand Challenge
  • 1.7.2 Autonomous Vehicles From Different Companies
  • 1.8 Other Types of Robots
  • 1.9 Into the Future
  • 1.10 Summary
  • References
  • 2 - Basics of Autonomous Vehicles
  • 2.1 Introduction
  • 2.2 Hardware
  • 2.2.1 External Sensors
  • 2.2.2 Stereovision and 3D Sensing
  • 2.2.3 Motion and Internal Sensors
  • 2.2.4 Actuators and Drive by Wire
  • 2.2.5 Processing and Networking
  • 2.2.6 Power
  • 2.3 Software
  • 2.3.1 Vision
  • 2.3.2 Mapping
  • 2.3.3 Localization
  • 2.3.4 Motion Planning
  • 2.3.5 Control
  • 2.3.6 Human-Machine Interface Issues
  • 2.4 Localization
  • 2.4.1 Kalman Filter
  • 2.4.2 Extended Kalman Filter
  • 2.4.3 Particle Filtering
  • 2.5 Control
  • 2.6 Summary
  • References
  • 3 - Perception in Autonomous Vehicles
  • 3.1 Introduction
  • 3.2 Perception
  • 3.2.1 Sensor Choices and Placement
  • 3.2.2 Sensor Calibration
  • 3.2.3 Stereovision and 3D Techniques
  • 3.2.4 Multisensors and Information Fusion
  • 3.3 Computer Vision
  • 3.3.1 Image Preprocessing
  • 3.3.2 Feature Extraction
  • 3.3.3 Segmentation and Localization
  • 3.4 Recognition
  • 3.4.1 Neural Networks
  • 3.4.2 Support Vector Machines
  • 3.4.3 Decision Trees
  • 3.4.4 Adaptive Boosting
  • 3.5 Tracking and Optical Flow
  • 3.6 Vision for General Navigation
  • 3.6.1 Vehicle and Obstacle Detection and Tracking
  • 3.6.2 Road and Lane Detection and Tracking
  • 3.6.3 Pedestrian Detection
  • 3.6.4 Understanding Traffic Signs
  • 3.7 Summary
  • References
  • 4 - Advanced Driver Assistance Systems
  • 4.1 Introduction
  • 4.2 Information-Based Assistance Systems
  • 4.2.1 Advanced Traveller Information Systems
  • 4.2.2 Inattention Alert Systems
  • 4.2.2.1 Indicators of Inattention
  • 4.2.2.2 General Procedure for Detecting Inattention
  • 4.2.2.3 Feature Detection
  • 4.2.2.4 Feature Extraction and Classification
  • 4.2.2.5 Hybrid Techniques for Inattention Detection
  • 4.2.3 Measuring Driver Performance
  • 4.3 Manipulation-Based Assistance Systems
  • 4.3.1 Safety Alert and Emergency Stopping
  • 4.3.2 Adaptive Cruise Control
  • 4.3.3 Overtaking Assessment and Assist
  • 4.3.4 Automated Parking Systems
  • 4.4 Feedback Modalities to Driver
  • 4.5 Multi-Vehicle Systems
  • 4.6 Communication
  • 4.7 Summary
  • References
  • 5 - Introduction to Planning
  • 5.1 Introduction
  • 5.2 Layers of Planning
  • 5.2.1 Strategic and Tactical Planning
  • 5.2.2 Route Planning
  • 5.2.3 Obstacle Avoidance
  • 5.2.4 Trajectory Generation
  • 5.2.5 Control
  • 5.3 Types of Traffic
  • 5.3.1 Organized and Structured Traffic
  • 5.3.2 Unorganized and Unstructured Traffic
  • 5.3.3 Off-road Vehicles
  • 5.4 Motion-Planning Primitives
  • 5.4.1 Configuration Space and Problem Description
  • 5.4.2 Metrics to Judge Planning Algorithms
  • 5.4.3 Deliberative and Reactive Planning
  • 5.4.4 Planning and Replanning
  • 5.4.5 Anytime Algorithms
  • 5.5 Multirobot Motion Planning
  • 5.5.1 Centralized Solutions
  • 5.5.2 Decentralized Solutions
  • 5.5.3 Prioritized Motion Planning
  • 5.5.4 Path-Velocity Decomposition
  • 5.6 Motion Planning for Autonomous Vehicles
  • 5.6.1 Road Coordinate Axis System
  • 5.6.2 Problem Formulation
  • 5.6.3 Objective Function
  • 5.6.4 Continual Planning and Per-segment Planning
  • 5.6.5 Planning With and Without Communication
  • 5.6.6 Planning and Coordination
  • 5.7 Planning for Special Scenarios
  • 5.8 Summary
  • References
  • 6 - Optimization-Based Planning
  • 6.1 Introduction
  • 6.2 A Brief Overview of Literature
  • 6.3 A Primer on Genetic Algorithm (GA)
  • 6.3.1 General Algorithm Framework
  • 6.3.2 Individual Representation
  • 6.3.3 Genetic Operators
  • 6.3.3.1 Scaling
  • 6.3.3.2 Selection
  • 6.3.3.3 Crossover
  • 6.3.3.4 Mutation
  • 6.3.3.5 Other Operators
  • 6.3.4 Stopping Criterion
  • 6.3.5 Exploration and Exploitation
  • 6.4 Motion Planning with Genetic Algorithm
  • 6.4.1 Individual Representation
  • 6.4.2 Genetic Operators
  • 6.4.3 Path Fitness Evaluation
  • 6.5 Coordination
  • 6.5.1 Determining Speed
  • 6.5.2 Overtaking
  • 6.5.3 Vehicle Following
  • 6.6 Results
  • 6.6.1 Higher-level Planning
  • 6.6.2 Overtaking
  • 6.6.3 Vehicle Following
  • 6.6.4 Vehicle Behaviour Analysis
  • 6.6.5 Genetic Algorithm Parameter Analysis
  • 6.7 Summary
  • References
  • 7 - Sampling-Based Planning
  • 7.1 Introduction
  • 7.2 A Brief Overview of Literature
  • 7.3 A Primer on Rapidly Exploring Random Trees (RRT)
  • 7.3.1 Rapidly Exploring Random Trees
  • Algorithm 7.1: RRT(source, goal)
  • 7.3.2 RRT-Connect
  • Algorithm 7.2: RRT-Connect (source, goal)
  • 7.3.3 Multitree Approaches
  • Algorithm 7.3: Bi-directional-RRT (source, goal)
  • 7.3.4 Role of Parameters
  • 7.4 Solution With RRT
  • 7.4.1 RRT Expansion
  • 7.4.2 Curve Generation
  • 7.4.3 Coordination
  • Algorithm 7.4: Plan (vehicles, map)
  • Algorithm 7.5: RRT (source, segment)
  • 7.5 Results
  • 7.5.1 Single-vehicle Simulations
  • 7.5.2 Multi-vehicle Simulations
  • 7.5.3 Analysis
  • 7.6 Solution With RRT-Connect
  • 7.6.1 RRT-Connect
  • Algorithm 7.6: RRT-Connect (source, time, vi)
  • Algorithm 7.7: CheckConnect (tree, node)
  • 7.6.2 Local Optimization
  • Algorithm 7.8: LocalOptimization(t)
  • 7.6.3 Coordination
  • Algorithm 7.9: Plan (road segment, time)
  • 7.7 Results
  • 7.7.1 Single-vehicle Scenarios
  • 7.7.2 Two-vehicle Overtaking and Vehicle-following Scenarios
  • 7.7.3 Vehicle-avoidance Scenarios
  • 7.7.4 RRT Analysis
  • 7.7.5 Local Optimization Analysis
  • 7.8 Summary
  • References
  • 8 - Graph Search-Based Hierarchical Planning
  • 8.1 Introduction
  • 8.2 A Brief Overview of Literature
  • 8.3 A Primer on Graph Search
  • 8.3.1 Graph Search
  • 8.3.2 States and Actions
  • 8.3.3 Uniform Cost Search
  • Algorithm 8.1: Uniform Cost Search (G
  • Algorithm 8.2: PrintPath(n)
  • 8.3.4 A* Algorithm
  • Algorithm 8.3: A* Search (G
  • 8.3.5 Heuristics in Search
  • 8.3.6 D* Algorithm
  • 8.3.7 Problems With Graph Search
  • 8.4 Multilayer Planning
  • 8.5 Hierarchy 1: Path Computation
  • 8.6 Hierarchy 2: Pathway Selection
  • 8.6.1 Pathway Segments Computation
  • Algorithm 8.4: getPathwaySegments
  • 8.6.2 Graph Conversion and Search
  • 8.6.3 Presence of Multiple Vehicles
  • 8.6.4 Heuristics in the Presence of Multiple Vehicles
  • Algorithm 8.5: getPathway
  • 8.7 Hierarchy 3: Pathway Distribution
  • 8.7.1 Order of (Re-) Planning
  • 8.7.2 Heuristic Placement of Vehicles
  • 8.7.3 Prepreparation and Postpreparation
  • 8.7.4 Feasibility Checks of Hierarchy
  • Algorithm 8.6: getDistributedPathway
  • 8.8 Hierarchy 4: Trajectory Generation
  • 8.8.1 Collision Checking and Replanning
  • 8.8.2 Curve Smoothing
  • Algorithm 8.7: getTrajectory
  • 8.8.3 Vehicular Movement
  • 8.9 Algorithm
  • Algorithm 8.8: RoadSegmentPlan
  • 8.10 Results
  • 8.10.1 Single- or Two-Vehicle Scenarios
  • 8.10.2 Multivehicle Scenarios
  • 8.10.3 Algorithmic Parameter Analysis
  • 8.10.4 Algorithmic Scalability Analysis
  • 8.11 Summary
  • References
  • 9 - Using Heuristics in Graph Search-Based Planning
  • 9.1 Introduction
  • 9.2 A Brief Overview of Literature
  • 9.3 Dynamic Distributed Lanes for a Single Vehicle
  • Algorithm 9.1: Uniform Cost Search for a Single Vehicle
  • 9.3.1 State Reduction
  • 9.3.2 State Selection
  • 9.3.3 Curve Generation
  • Algorithm 9.2: Expansion for a Single Vehicle
  • 9.3.4 Results
  • 9.4 Dynamic Distributed Lanes for Multiple Vehicles
  • 9.4.1 Free-State Expansion
  • 9.4.1.1 Computing the Number of Lanes Required
  • Algorithm 9.3: Getting Number of Vehicles Requiring Independent Lanes
  • Outline placeholder
  • 9.4.1.2 Lane Distribution
  • Algorithm 9.4: Division of the Road Into Lanes
  • Outline placeholder
  • 9.4.1.3 Vehicle Trajectory Generation
  • Algorithm 9.5: Trajectory Generation From the Current State to the Expanded State
  • Algorithm 9.5.1: GenerateTrajectorySelf()
  • Algorithm 9.5.2: GenerateTrajectoryElse()
  • Algorithm 9.6: Free-State Expansion Strategy
  • 9.4.2 Vehicle Following
  • Algorithm 9.7: Vehicle-Following Expansion Strategy
  • 9.4.3 Wait for Vehicle
  • 9.4.3.1 Calculating Preferred Positions
  • 9.4.3.2 Generating Trajectories
  • Algorithm 9.8: Wait for Vehicle Expansion Strategy
  • 9.4.4 Expansion Strategy
  • Algorithm 9.9: Selection of Expansion Strategy
  • 9.5 Results
  • 9.5.1 Simple Road Experiments
  • 9.5.2 Experiments of Multivehicles in an Obstacle Network
  • 9.5.3 Parameter Analysis
  • 9.6 Summary
  • References
  • 10 - Fuzzy-Based Planning
  • 10.1 Introduction
  • 10.2 A Brief Overview of Literature
  • 10.3 A Primer on Fuzzy Logic
  • 10.3.1 Fuzzy Sets
  • 10.3.2 Fuzzy Logical Operators
  • 10.3.3 Aggregation
  • 10.3.4 Defuzzification
  • 10.3.5 Fuzzy Inference Systems
  • 10.3.6 Role of Parameters
  • 10.4 Fuzzy Logic for Planning
  • 10.4.1 Fuzzy Inference System
  • 10.4.2 Overtaking
  • 10.4.3 Going Through a Crossing
  • 10.5 Evolution of the Fuzzy Inference System
  • 10.6 Results
  • 10.6.1 Single-Vehicle Scenarios
  • 10.6.2 Double-Vehicle Scenarios
  • 10.6.3 Overtaking
  • 10.6.4 Crossing
  • 10.6.5 Analysis
  • 10.7 Summary
  • References
  • 11 - Potential-Based Planning
  • 11.1 Introduction
  • 11.2 A Brief Overview of Literature
  • 11.3 A Primer on Artificial Potential Field
  • 11.3.1 Concept
  • 11.3.2 Attractive Potential
  • 11.3.3 Repulsive Potential
  • 11.3.4 Total Potential and Motion
  • 11.3.5 Working
  • 11.4 Lateral Potentials for Planning
  • 11.4.1 Forward Potential
  • 11.4.2 Side Potential
  • 11.4.3 Diagonal Potential
  • 11.4.4 Back Potential
  • 11.4.5 Lateral Planning
  • 11.4.6 Longitudinal Planning
  • 11.5 Results for Lateral Potentials
  • 11.5.1 Simulation Results
  • 11.5.2 Algorithmic Parameters
  • 11.6 A Primer on Elastic Strip
  • 11.6.1 Concept
  • 11.6.2 External Forces
  • 11.6.3 Internal Forces
  • 11.6.4 Working
  • 11.7 Problem Modelling With an Elastic Strip
  • 11.7.1 Objectives
  • 11.7.2 General Speed Bounds
  • 11.7.3 Plan Feasibility
  • 11.8 Solution With an Elastic Strip
  • 11.8.1 Plan Extender
  • Algorithm 11.1: Extend1(t, tstrat,vq)
  • Algorithm 11.2: Extend(t, tstrat, vq)
  • 11.8.2 Plan Optimizer
  • 11.8.3 Complete Framework
  • Algorithm 11.3: Plan(tobs, t, vq)
  • 11.9 Results With an Elastic Strip
  • 11.9.1 Simulation Results
  • 11.9.2 Parameters
  • 11.10 Summary
  • References
  • 12 - Logic-Based Planning
  • 12.1 Introduction
  • 12.2 A Brief Overview of Literature
  • 12.3 Problem and Solution Modelling
  • 12.3.1 Problem Modelling
  • 12.3.2 Single or Dual Carriageway
  • 12.3.3 Aggression Factor
  • 12.3.4 Algorithm Modelling
  • 12.4 Behaviours
  • 12.4.1 Obstacle Avoidance
  • Algorithm 12.1: ObstacleAvoidance(Ri, map)
  • 12.4.2 Centreing
  • 12.4.3 Lane Change
  • 12.4.4 Overtaking
  • 12.4.4.1 Direct Overtaking
  • 12.4.4.2 Assistive Overtaking
  • 12.4.5 Being Overtaken
  • 12.4.6 Maintain Separation Steer
  • 12.4.7 Slow Down
  • 12.4.8 Discover Conflicting Interests
  • 12.4.9 Travel Straight
  • 12.5 Single-Lane Overtaking
  • 12.5.1 Single-lane Overtaking Initiation
  • 12.5.2 General Travel
  • 12.5.3 Cancelling Single-Lane Overtaking
  • 12.5.4 Completing Single-Lane Overtaking
  • 12.6 Complete Algorithm
  • Algorithm 12.2: Plan(Vehicle Ri, Map, Previous Plan t)
  • 12.7 Results
  • 12.7.1 General Traversal
  • 12.7.2 Obstacle Avoidance
  • 12.7.3 Overtaking
  • 12.7.4 Complex Formulation
  • 12.7.5 Aggression
  • 12.7.6 Single-Lane Overtaking
  • 12.8 Summary
  • References
  • 13 - Basics of Intelligent Transportation Systems
  • 13.1 Introduction
  • 13.2 Traffic Systems and Traffic Flow
  • 13.2.1 Traffic Flow
  • 13.2.2 Fundamental Diagrams
  • 13.2.3 Interrupted Traffic Flow
  • 13.2.4 Congestion
  • 13.3 Traffic Simulation
  • 13.3.1 Basic Concepts
  • 13.3.2 Intelligent Driver Model
  • 13.3.3 Other Modules
  • 13.3.4 Empirical Studies
  • 13.3.5 Common Traffic Simulators
  • 13.4 Intelligent Constituents of the Transportation System
  • 13.4.1 Intelligent Traffic Lights
  • 13.4.2 Intelligent Intersection Management
  • 13.4.3 Traffic Merging Management
  • 13.5 Summary
  • References
  • 14 - Intelligent Transportation Systems With Diverse Vehicles
  • 14.1 Introduction
  • 14.2 A Brief Overview of Literature
  • 14.3 Semiautonomous Intelligent Transportation System for Diverse Vehicles
  • 14.3.1 Traffic Lights System
  • 14.3.1.1 Concept
  • 14.3.1.2 Simulations
  • 14.3.2 Speed Lanes
  • 14.3.3 Route Planning
  • 14.3.4 Reservations
  • 14.3.5 General Architecture
  • 14.3.6 Simulations
  • 14.4 Congestion Avoidance in City Traffic
  • 14.4.1 Problem Formulation and Scenario of Operation
  • 14.4.1.1 City Scenario
  • 14.4.1.2 Inferred Hypothesis
  • 14.4.1.3 Other Scenario Specifics
  • 14.4.1.4 Decentralized Anticipatory Routing
  • 14.4.2 System Working
  • 14.4.2.1 Traffic Simulation
  • 14.4.2.2 Single-Lane Overtaking
  • 14.4.2.3 Vehicle Routing
  • 14.4.3 Experimental Results
  • 14.4.3.1 Initialization
  • 14.4.3.2 Alternative Methods
  • 14.4.3.3 Comparisons
  • 14.4.3.4 Analysis of Single-Lane Overtaking
  • 14.5 Summary
  • References
  • 15 - Reaching Destination Before Deadline With Intelligent Transportation Systems
  • 15.1 Introduction
  • 15.2 A Brief Overview of Literature
  • 15.3 Computing Journey Start Times
  • 15.3.1 Problem Characteristics
  • 15.3.2 Problem Statement
  • 15.4 Algorithm for Computing Journey Start Times
  • 15.4.1 Learning Travel Speeds
  • 15.4.2 Routing
  • 15.4.3 Probability of Reaching the Destination on Time
  • 15.5 Cooperative Transportation Systems
  • 15.5.1 Vehicle Travel State
  • 15.5.2 Lateness Consciousness Cost
  • 15.5.3 Cooperative Traffic Lights
  • 15.5.4 Cooperative Lane Changes
  • 15.6 Results
  • 15.7 Summary
  • References
  • 16 - Conclusions
  • 16.1 Conclusions
  • 16.2 Autonomous Vehicles
  • 16.2.1 Algorithm Analysis
  • 16.2.2 Practical Scenarios
  • 16.2.3 Other Possibilities
  • 16.3 Intelligent Transportation Systems
  • 16.4 Limitations
  • 16.5 Closing Remarks
  • References
  • Index
  • A
  • B
  • C
  • D
  • E
  • F
  • G
  • H
  • I
  • J
  • K
  • L
  • M
  • N
  • O
  • P
  • Q
  • R
  • S
  • T
  • U
  • V
  • W
  • Y
  • Back Cover

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