Name
Abstract | ||
|---|---|---|
Vehicle following can be achieved by minimizing the relative information (Kullback-Leibler or K-L distance), between the estimated poses of leader and follower vehicles by formulating the vehicle following system as an optimization problem. The aim is to search for an optimal control action for the follower vehicle in the admissible control command space. Relative information is used as a metric in the search space and for evaluating the expected performance of vehicle following. With this metric, and based on the assumption that both vehicle pose (position and orientation) distributions are Gaussian functions, the K-L distance of the vehicle following system can be computed. With a series of admissible actions, such as steering and velocity commands, for the follower vehicle at each pose prediction step, and by minimizing the K-L distance, an optimized action for the follower vehicle can be obtained. The proposed vehicle following algorithm has been tested and the performance of the follower vehicle when the leader undergoes various kinds of maneuvers has been analyzed. Results using this new method, as compared to classical methods, have shown the advantages of this method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/IROS.2008.4650858 | IROS |
Keywords | Field | DocType |
steering commands,optimisation,admissible control command space,optimal control,kullback-leibler distance,mobile robots,vehicles,pose estimation,velocity control,optimization problem,autonomous vehicle,velocity commands,relative information metric,gaussian processes,vehicle following systems,optimal control action,gaussian functions,kullback leibler,search space | Mathematical optimization,Optimal control,Control theory,Computer science,Pose,Control engineering,Gaussian,Gaussian process,Optimization problem,Mobile robot | Conference |
ISBN | Citations | PageRank |
978-1-4244-2057-5 | 0 | 0.34 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Teck Chew Ng | 1 | 26 | 4.28 |
| Martin Adams | 2 | 150 | 21.18 |
| Javier Ibanez Guzman | 3 | 128 | 17.14 |