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

By Hong Cheng

This significant text/reference offers state of the art learn on clever autos, masking not just issues of object/obstacle detection and popularity, but additionally features of auto movement keep watch over. With an emphasis on either high-level strategies, and useful element, the textual content hyperlinks concept, algorithms, and problems with and software program implementation in clever motor vehicle study. subject matters and contours: offers a radical creation to the advance and most modern growth in clever automobile examine, and proposes a easy framework; offers detection and monitoring algorithms for established and unstructured roads, in addition to on-road automobile detection and monitoring algorithms utilizing boosted Gabor positive aspects; discusses an process for a number of sensor-based multiple-object monitoring, as well as an built-in DGPS/IMU positioning procedure; examines a motor vehicle navigation procedure utilizing worldwide perspectives; introduces algorithms for lateral and longitudinal automobile movement control.

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

Example text

To achieve these objectives, the driver and the on-board sensors must recognize the traffic signs. 2. Vehicle dynamics The motion of a vehicle is restricted by vehicle dynamics and includes two factors: the internal factor involving the tires, steering systems, and acceleration/deceleration systems and the other external factor involving driver 28 3 The Framework of Intelligent Vehicles instruction. In our framework, we consider it as a whole to affect the safe driving rather than look at each influencing sub-factor, and focus on vehicle stable-state and dynamic-state response properties.

This chapter proposes an integrated current and future safety situation analysis framework as general as possible, where we model not only the sensing phase, but also the control phase. 3 Interactive Safety Analysis Framework 25 Fig. 1 Interactive road situation analysis framework lidar data is used to distinguish two types of obstacles: static objects and moving objects. On the basis of the speed and type of obstacles, we form obstacle tracks only using a single sensor, and following that a track fusion approach is used to yield accurate and robust global tracks.

Similar to [12], we use L∗ u∗ v ∗ to represent pixel features, since this color space is good for perceiving uniform color spaces. 114 ⎦ ⎣ G ⎦ . 52) Regarding this space, we can use the following equation to measure color distance: E= ( L∗ )2 + ( u∗ )2 + ( v ∗ )2 . 53) We summarize the road recognition using mean shift segmentation as follows: • • • • • Down-sampled original images for reducing computational complexity. Extract pixel features using L∗ u∗ v ∗ color spaces. Segment image using mean shift segment algorithms.

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