Abstract
This paper proposes a framework for uncertainty prediction in complex fusion networks, where signals become available sporadically. Assuming there is no information of the sensor characteristics available, a surrogated model of the sensor uncertainty is yielded directly from data through artificial neural networks. The strategy developed is applied to autonomous vehicle localization through odometry sensors (speed and orientation), so as to determine the location uncertainty in the trajectory. The results obtained allow for fusion of autonomous vehicle location measurements, and effective correction of the accumulated odometry error in most scenarios. The neural networks applicability and generalization capacity are proven, evidencing the suitability of the presented methodology for uncertainty estimation in non-linear and intractable processes.
Highlights
Mobility has become a serious challenge in a society with a gradually aging population and a perpetually increasing traffic
Given the fact that the Neural Networks (NN) has seen 70% of the data used for testing, different version of the noisy trajectories are used, these still share similar characteristics which might prevent from yielding final conclusions
NNs can be considered as blackbox models whose robustness cannot be tested with conventional methods
Summary
Mobility has become a serious challenge in a society with a gradually aging population and a perpetually increasing traffic. This vehicle includes an advanced laser range finder, between other sensors, able to process the environment real time (Poczter and Jankovic, 2014) These advanced devices are generally associated with elevated cost, and are not feasible for serial production vehicles. The features perception becomes a complex problems where heterogeneous signals need to be registered, transformed into a common level and conveniently combined to guarantee safety (Jiang et al, 2011). This process is known as data fusion and usually involves noisy measurements and highly non-linear transformations
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