Autonomous Intelligent Vehicles : Theory, Algorithms, and Implementation

This important text/reference presents state-of-the-art research on intelligent vehicles, covering not only topics of object/obstacle detection and recognition, but also aspects of vehicle motion control. With an emphasis on both high-level concepts, and practical detail, the text links theory, algorithms, and issues of hardware and software implementation in intelligent vehicle research. Topics and features: presents a thorough introduction to the development and latest progress in intelligent vehicle research, and proposes a basic framework; provides detection and tracking algorithms for structured and unstructured roads, as well as on-road vehicle detection and tracking algorithms using boosted Gabor features; discusses an approach for multiple sensor-based multiple-object tracking, in addition to an integrated DGPS/IMU positioning approach; examines a vehicle navigation approach using global views; introduces algorithms for lateral and longitudinal vehicle motion control.

Autonomous intelligent vehicles pose unique challenges in robotics, that encompass issues of environment perception and modeling, localization and map building, path planning and decision-making, and motion control.

This important text/reference presents state-of-the-art research on intelligent vehicles, covering not only topics of object/obstacle detection and recognition, but also aspects of vehicle motion control. With an emphasis on both high-level concepts, and practical detail, the text links theory, algorithms, and issues of hardware and software implementation in intelligent vehicle research.

Topics and features:

• Presents a thorough introduction to the development and latest progress in intelligent vehicle research, and proposes a basic framework
• Provides detection and tracking algorithms for structured and unstructured roads, as well as on-road vehicle detection and tracking algorithms using boosted Gabor features
• Discusses an approach for multiple sensor-based multiple-object tracking, in addition to an integrated DGPS/IMU positioning approach
• Examines a vehicle navigation approach using global views
• Introduces algorithms for lateral and longitudinal vehicle motion control

An essential reference for researchers in the field, the broad coverage of all aspects of this research will also appeal to graduate students of computer science and robotics who are interested in intelligent vehicles.

Dr. Hong Cheng is Professor in the School of Automation Engineering, and Director of the Pattern Recognition and Machine Intelligence Institute at the University of Electronic Science and Technology of China.