Autonomous Intelligent Vehicles: Theory, Algorithms, and by Hong Cheng

By Hong Cheng

This crucial text/reference offers state of the art study on clever automobiles, overlaying not just subject matters of object/obstacle detection and popularity, but in addition elements of auto movement keep watch over. With an emphasis on either high-level strategies, and functional element, the textual content hyperlinks idea, algorithms, and problems with and software program implementation in clever automobile study. subject matters and contours: offers an intensive creation to the improvement and most up-to-date growth in clever car examine, and proposes a uncomplicated framework; presents detection and monitoring algorithms for established and unstructured roads, in addition to on-road automobile detection and monitoring algorithms utilizing boosted Gabor beneficial properties; discusses an process for a number of sensor-based multiple-object monitoring, as well as an built-in DGPS/IMU positioning technique; examines a motor vehicle navigation technique utilizing international perspectives; introduces algorithms for lateral and longitudinal car movement control.

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Extra resources for Autonomous Intelligent Vehicles: Theory, Algorithms, and Implementation (Advances in Computer Vision and Pattern Recognition)

Sample text

The RALPH first hypothesizes road models with different curvatures, then subtracts these curvatures from the low-resolution image, and finally validates which hypothesized curvature matches well the original image. 4 Neural Network Based Approaches In most prototypes of autonomous vehicles developed worldwide, road recognition and vehicle driving are two separate modules. However, some early systems were not based on the preliminary road detection, but obtained driving commands directly from road images.

3 Lane Detection Using Adaptive Random Hough Transform 39 Fig. 1 The ARTH algorithm flow parameter accuracy, larger scope of the parameter space, smaller storage requirements, and higher speed. However, the procedure is repeated to combine the discrete parameter values. Thereby, the wide range and high accuracy of the parameters could yield remarkably large computing burden and storage space requirements. Motivated by these problems, we present a new Adaptive RHT (ARHT) for lane detection, which combines the advantage of both the AHT and RHT.

N − 1); 4. Then proceed to the Importance Sampling step, when the next measurement arrives. 2 Lane Model In this approach, the lane tracking is formulated for the estimation of lane’s parameters and vehicle’s states. In this section, we introduce the lane model and the dynamic system model. 22) 46 4 Road Detection and Tracking Fig. 5 An illustration of a vehicle dynamic system model at different times where y is the lateral position of the road center relative to the vehicle, x is the longitudinal distance ahead, φ is the pitch of the camera relative to the road surface, a0 and a1 are the curvature and curvature rate of the lanes.

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