On-Road Intelligent Vehicles

  • ID: 3612383
  • Book
  • 536 Pages
  • Elsevier Science and Technology
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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

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Part I: Autonomous Vehicles

1. Introduction

2. Basics of Autonomous Vehicles

3. Perception in Autonomous Vehicles

4. Advanced Driver Assistance Systems

Part II: Deliberative Motion Planning of Autonomous Vehicles

5. Introduction to Planning

6. Optimization Based Planning

7. Sampling Based Planning

8. Graph Search based Hierarchical Planning

9. Using Heuristics in Graph Search based Planning

Part III: (Near-)Reactive Motion Planning of Autonomous Vehicles

10. Fuzzy Based Planning

11. Potential Based Planning

12. Logic Based Planning

Part IV: Intelligent Transportation Systems

13. Basics of Intelligent Transportation System

14. Intelligent Transportation Systems with Diverse Vehicles

15. Reaching Destination before Deadline with Intelligent Transportation Systems

16. Conclusions

Appendix A: Resources from the Author

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Kala, Rahul
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.
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