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

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.

Resource-Aware IoT Control: Saving Communication Through Predictive Triggering

“…Various design methods have been proposed in literature for event-based state estimation and, in particular, its core components, the prediction/estimation algorithms and event triggers. For the former, different types of Kalman filters [12], [13], [20], modified Luenberger-type observers [14], [15], and set-membership filters [21], [22] have been used, for example. Variants of event triggers include triggering based on the innovation [12], [23], estimation variance [13], [24], or entire probability density functions (PDFs) [25].…”

Event-Triggered Learning for Resource-Efficient Networked Control

“…The threshold parameter κ exact quantifies the error we are willing to tolerate. We denote (18) as the exact learning trigger because it involves the exact expected value E[τ ], as opposed to the trigger derived in the next subsection. Even though the trigger (18) is meant to detect inaccurate models, there is always a chance that the trigger fires not due to an inaccurate model, but instead due to the randomness of the process (and thus randomness of inter-communication times τ i ).…”

“…Even though the trigger (18) is meant to detect inaccurate models, there is always a chance that the trigger fires not due to an inaccurate model, but instead due to the randomness of the process (and thus randomness of inter-communication times τ i ). Even for a perfect model, (18) may trigger at some point. This is inevitable due to the stochastic nature of the problem.…”

“…This result can be used as follows: the user specifies the desired confidence level η, the number N of inter-communication times considered in the empirical average, and the maximum inter-communication time τ max . By choosing κ exact as in (20), the exact learning trigger (18) is guaranteed to make incorrect triggering decisions (false positives) with a probability of less than η.…”

“…In the experiments reported in the next sections, we use trigger (23), which is easier to compute than (18). In order to improve the trigger's robustness, we trigger learning experiments only when (23) holds for a certain duration rather than instantaneous.…”