28,797 research outputs found
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
To assure that an autonomous car is driving safely on public roads, its
object detection module should not only work correctly, but show its prediction
confidence as well. Previous object detectors driven by deep learning do not
explicitly model uncertainties in the neural network. We tackle with this
problem by presenting practical methods to capture uncertainties in a 3D
vehicle detector for Lidar point clouds. The proposed probabilistic detector
represents reliable epistemic uncertainty and aleatoric uncertainty in
classification and localization tasks. Experimental results show that the
epistemic uncertainty is related to the detection accuracy, whereas the
aleatoric uncertainty is influenced by vehicle distance and occlusion. The
results also show that we can improve the detection performance by 1%-5% by
modeling the aleatoric uncertainty.Comment: Accepted to present in the 21st IEEE International Conference on
Intelligent Transportation Systems (ITSC 2018
Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs
The human reasoning process is seldom a one-way process from an input leading
to an output. Instead, it often involves a systematic deduction by ruling out
other possible outcomes as a self-checking mechanism. In this paper, we
describe the design of a hybrid neural network for logical learning that is
similar to the human reasoning through the introduction of an auxiliary input,
namely the indicators, that act as the hints to suggest logical outcomes. We
generate these indicators by digging into the hidden information buried
underneath the original training data for direct or indirect suggestions. We
used the MNIST data to demonstrate the design and use of these indicators in a
convolutional neural network. We trained a series of such hybrid neural
networks with variations of the indicators. Our results show that these hybrid
neural networks are very robust in generating logical outcomes with inherently
higher prediction accuracy than the direct use of the original input and output
in apparent models. Such improved predictability with reassured logical
confidence is obtained through the exhaustion of all possible indicators to
rule out all illogical outcomes, which is not available in the apparent models.
Our logical learning process can effectively cope with the unknown unknowns
using a full exploitation of all existing knowledge available for learning. The
design and implementation of the hints, namely the indicators, become an
essential part of artificial intelligence for logical learning. We also
introduce an ongoing application setup for this hybrid neural network in an
autonomous grasping robot, namely as_DeepClaw, aiming at learning an optimized
grasping pose through logical learning.Comment: 11 pages, 9 figures, 4 table
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