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.
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