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On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle.

Martínez-Rey M, Espinosa F, Gardel A, Santos C - Sensors (Basel) (2015)

Bottom Line: This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient.An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory.By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronics, University of Alcalá. Polytechnic School, Campus Universitario, Alcalá de Henares 28871, Spain. miguel.martinez@depeca.uah.es.

ABSTRACT
For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver.

No MeSH data available.


Related in: MedlinePlus

General description of the system showing the most important elements: the vehicle (controller and estimator) and sensors linked by a wireless network.
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f1-sensors-15-14569: General description of the system showing the most important elements: the vehicle (controller and estimator) and sensors linked by a wireless network.

Mentions: This paper deals with the localization and guidance of an autonomous vehicle using a state-space model and an external sensorial system based on cameras. Figure 1 depicts the main elements of the system. In the center of the figure, there is the autonomous vehicle that executes an estimation algorithm, as well as a guidance control to follow a pre-configured reference path. Above it, camera sensors that detect the position are connected to the vehicle via a wireless network. The technology used for the external sensors is not relevant, since the proposed method would work with any other kind of localization sensors, such as lasers, ultrasound or infra-red local positioning systems.


On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle.

Martínez-Rey M, Espinosa F, Gardel A, Santos C - Sensors (Basel) (2015)

General description of the system showing the most important elements: the vehicle (controller and estimator) and sensors linked by a wireless network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507619&req=5

f1-sensors-15-14569: General description of the system showing the most important elements: the vehicle (controller and estimator) and sensors linked by a wireless network.
Mentions: This paper deals with the localization and guidance of an autonomous vehicle using a state-space model and an external sensorial system based on cameras. Figure 1 depicts the main elements of the system. In the center of the figure, there is the autonomous vehicle that executes an estimation algorithm, as well as a guidance control to follow a pre-configured reference path. Above it, camera sensors that detect the position are connected to the vehicle via a wireless network. The technology used for the external sensors is not relevant, since the proposed method would work with any other kind of localization sensors, such as lasers, ultrasound or infra-red local positioning systems.

Bottom Line: This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient.An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory.By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver.

View Article: PubMed Central - PubMed

Affiliation: Department of Electronics, University of Alcalá. Polytechnic School, Campus Universitario, Alcalá de Henares 28871, Spain. miguel.martinez@depeca.uah.es.

ABSTRACT
For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver.

No MeSH data available.


Related in: MedlinePlus