On-Road Intelligent Vehicles - 1st Edition - ISBN: 9780128037294, 9780128037560

On-Road Intelligent Vehicles

1st Edition

Motion Planning for Intelligent Transportation Systems

Authors: Rahul Kala
eBook ISBN: 9780128037560
Paperback ISBN: 9780128037294
Imprint: Butterworth-Heinemann
Published Date: 27th April 2016
Page Count: 536
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Description

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.

Key Features

  • 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

Readership

Postgraduate students, researchers and practitioners, working in the areas of Intelligent Vehicles, Intelligent Transportation Systems, Autonomous Vehicles, Robot Motion Planning, Special Topics in Robotics, Cooperative Systems, Planning and Navigation.

Table of Contents

  • Acknowledgement
  • 1. Introduction
    • 1.1. Introduction
    • 1.2. Why Autonomous Vehicles?
    • 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
    • 1.8. Other Types of Robots
    • 1.9. Into the Future
    • 1.10. Summary
  • 2. Basics of Autonomous Vehicles
    • 2.1. Introduction
    • 2.2. Hardware
    • 2.3. Software
    • 2.4. Localization
    • 2.5. Control
    • 2.6. Summary
  • 3. Perception in Autonomous Vehicles
    • 3.1. Introduction
    • 3.2. Perception
    • 3.3. Computer Vision
    • 3.4. Recognition
    • 3.5. Tracking and Optical Flow
    • 3.6. Vision for General Navigation
    • 3.7. Summary
  • 4. Advanced Driver Assistance Systems
    • 4.1. Introduction
    • 4.2. Information-Based Assistance Systems
    • 4.3. Manipulation-Based Assistance Systems
    • 4.4. Feedback Modalities to Driver
    • 4.5. Multi-Vehicle Systems
    • 4.6. Communication
    • 4.7. Summary
  • 5. Introduction to Planning
    • 5.1. Introduction
    • 5.2. Layers of Planning
    • 5.3. Types of Traffic
    • 5.4. Motion-Planning Primitives
    • 5.5. Multirobot Motion Planning
    • 5.6. Motion Planning for Autonomous Vehicles
    • 5.7. Planning for Special Scenarios
    • 5.8. Summary
  • 6. Optimization-Based Planning
    • 6.1. Introduction
    • 6.2. A Brief Overview of Literature
    • 6.3. A Primer on Genetic Algorithm (GA)
    • 6.4. Motion Planning with Genetic Algorithm
    • 6.5. Coordination
    • 6.6. Results
    • 6.7. Summary
  • 7. Sampling-Based Planning
    • 7.1. Introduction
    • 7.2. A Brief Overview of Literature
    • 7.3. A Primer on Rapidly Exploring Random Trees (RRT)
    • Algorithm 7.1: RRT(source, goal)
    • Algorithm 7.2: RRT-Connect (source, goal)
    • Algorithm 7.3: Bi-directional-RRT (source, goal)
    • 7.4. Solution With RRT
    • Algorithm 7.4: Plan (vehicles, map)
    • Algorithm 7.5: RRT (source, segment)
    • 7.5. Results
    • 7.6. Solution With RRT-Connect
    • Algorithm 7.6: RRT-Connect (source, time, vi)
    • Algorithm 7.7: CheckConnect (tree, node)
    • Algorithm 7.8: LocalOptimization(τ)
    • Algorithm 7.9: Plan (road segment, time)
    • 7.7. Results
    • 7.8. Summary
  • 8. Graph Search-Based Hierarchical Planning
    • 8.1. Introduction
    • 8.2. A Brief Overview of Literature
    • 8.3. A Primer on Graph Search
    • Algorithm 8.1: Uniform Cost Search (G<V,E>, S, GoalTest)
    • Algorithm 8.2: PrintPath(n)
    • Algorithm 8.3: A∗ Search (G<V,E>, S, GoalTest)
    • 8.4. Multilayer Planning
    • 8.5. Hierarchy 1: Path Computation
    • 8.6. Hierarchy 2: Pathway Selection
    • Algorithm 8.4: getPathwaySegments
    • Algorithm 8.5: getPathway
    • 8.7. Hierarchy 3: Pathway Distribution
    • Algorithm 8.6: getDistributedPathway
    • 8.8. Hierarchy 4: Trajectory Generation
    • Algorithm 8.7: getTrajectory
    • 8.9. Algorithm
    • Algorithm 8.8: RoadSegmentPlan
    • 8.10. Results
    • 8.11. Summary
  • 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
    • Algorithm 9.2: Expansion for a Single Vehicle
    • 9.4. Dynamic Distributed Lanes for Multiple Vehicles
    • Algorithm 9.3: Getting Number of Vehicles Requiring Independent Lanes
    • Algorithm 9.4: Division of the Road Into Lanes
    • Algorithm 9.5: Trajectory Generation From the Current State to the Expanded State
    • Algorithm 9.6: Free-State Expansion Strategy
    • Algorithm 9.7: Vehicle-Following Expansion Strategy
    • Algorithm 9.8: Wait for Vehicle Expansion Strategy
    • Algorithm 9.9: Selection of Expansion Strategy
    • 9.5. Results
    • 9.6. Summary
  • 10. Fuzzy-Based Planning
    • 10.1. Introduction
    • 10.2. A Brief Overview of Literature
    • 10.3. A Primer on Fuzzy Logic
    • 10.4. Fuzzy Logic for Planning
    • 10.5. Evolution of the Fuzzy Inference System
    • 10.6. Results
    • 10.7. Summary
  • 11. Potential-Based Planning
    • 11.1. Introduction
    • 11.2. A Brief Overview of Literature
    • 11.3. A Primer on Artificial Potential Field
    • 11.4. Lateral Potentials for Planning
    • 11.5. Results for Lateral Potentials
    • 11.6. A Primer on Elastic Strip
    • 11.7. Problem Modelling With an Elastic Strip
    • 11.8. Solution With an Elastic Strip
    • Algorithm 11.1: Extend1(τ, τstrat,vq)
    • Algorithm 11.2: Extend(τ, τstrat, vq)
    • Algorithm 11.3: Plan(τobs, τ, vq)
    • 11.9. Results With an Elastic Strip
    • 11.10. Summary
  • 12. Logic-Based Planning
    • 12.1. Introduction
    • 12.2. A Brief Overview of Literature
    • 12.3. Problem and Solution Modelling
    • 12.4. Behaviours
    • Algorithm 12.1: ObstacleAvoidance(Ri, map)
    • 12.5. Single-Lane Overtaking
    • 12.6. Complete Algorithm
    • Algorithm 12.2: Plan(Vehicle Ri, Map, Previous Plan τ)
    • 12.7. Results
    • 12.8. Summary
  • 13. Basics of Intelligent Transportation Systems
    • 13.1. Introduction
    • 13.2. Traffic Systems and Traffic Flow
    • 13.3. Traffic Simulation
    • 13.4. Intelligent Constituents of the Transportation System
    • 13.5. Summary
  • 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
    • 14.4. Congestion Avoidance in City Traffic
    • 14.5. Summary
  • 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
    • 15.4. Algorithm for Computing Journey Start Times
    • 15.5. Cooperative Transportation Systems
    • 15.6. Results
    • 15.7. Summary
  • 16. Conclusions
    • 16.1. Conclusions
    • 16.2. Autonomous Vehicles
    • 16.3. Intelligent Transportation Systems
    • 16.4. Limitations
    • 16.5. Closing Remarks
  • Index

Details

No. of pages:
536
Language:
English
Copyright:
© Butterworth-Heinemann 2016
Published:
Imprint:
Butterworth-Heinemann
eBook ISBN:
9780128037560
Paperback ISBN:
9780128037294

About the Author

Rahul Kala

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.

Affiliations and Expertise

Assistant Professor, Robotics and Artificial Intelligence Laboratory, Indian Institute of Information Technology, Allahabad, India