New PDF release: Advances in Intelligent Vehicles

By Yaobin Chen and Lingxi Li (Auth.)

ISBN-10: 0123971993

ISBN-13: 9780123971999

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12: Mobile Robot with Laser Rangefinder. Ó 2011 IEEE. Reprinted with permission from “Virtual sensor system: Merging the real world with a simulation environment”, 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), 5e7 October 2011, pp. 1904e1909. A magnetometer has the ability to measure the strength and direction of a magnetic field. This sensor has been used to detect the field of magnets embedded into the lanes of a road [13]. Several magnetometers would be placed on the front of the robot to measure the magnetic field of the embedded magnets.

This example focuses on the testing stages of a “Driver Intention Estimator” developed at OSU CITR in collaboration with Honda R&D of Americas, Inc. The actual estimator software uses Vehicle-to-Vehicle (V2V) communications to detect, track, and predict the behavior of other vehicles as the ego vehicle approaches an intersection. The details of estimator implementation and underlying ideas can be found in Refs [14,15]. The input to the estimator suite is the measurement vector z containing information on the tracked vehicle and populated using V2V communications based on Dedicated Short Range Communication (DSRC) standards.

The total simulation time is 15 seconds. The simulation begins with all of the variables set to zero as the initial fà condition. During the simulation, the driver requests uh ¼ rw q_ w at time zero. In addition, the gain of state feedback K is calculated with an optimum regulator and takes the following values: ! 15: Block Diagram of Acceleration Control. 2. 16. One problem with this method was that each variable and desired variable did not match at the end of the simulation. After a certain period of time, the control inputs generated by the desired trajectory of acceleration became zero, and consequently the method considered in this simulation cannot resolve the error between the variables and the desired variables.

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Advances in Intelligent Vehicles by Yaobin Chen and Lingxi Li (Auth.)


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