3,314 research outputs found
A Model-Predictive Motion Planner for the IARA Autonomous Car
We present the Model-Predictive Motion Planner (MPMP) of the Intelligent
Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses
a path planner to compute a path from its current position to the desired
destination. Using this path, the current position, a goal in the path and a
map, IARA's MPMP is able to compute smooth trajectories from its current
position to the goal in less than 50 ms. MPMP computes the poses of these
trajectories so that they follow the path closely and, at the same time, are at
a safe distance of eventual obstacles. Our experiments have shown that MPMP is
able to compute trajectories that precisely follow a path produced by a Human
driver (distance of 0.15 m in average) while smoothly driving IARA at speeds of
up to 32.4 km/h (9 m/s).Comment: This is a preprint. Accepted by 2017 IEEE International Conference on
Robotics and Automation (ICRA
Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data
In the past few years, Convolutional Neural Networks (CNNs) have been
achieving state-of-the-art performance on a variety of problems. Many companies
employ resources and money to generate these models and provide them as an API,
therefore it is in their best interest to protect them, i.e., to avoid that
someone else copies them. Recent studies revealed that state-of-the-art CNNs
are vulnerable to adversarial examples attacks, and this weakness indicates
that CNNs do not need to operate in the problem domain (PD). Therefore, we
hypothesize that they also do not need to be trained with examples of the PD in
order to operate in it.
Given these facts, in this paper, we investigate if a target black-box CNN
can be copied by persuading it to confess its knowledge through random
non-labeled data. The copy is two-fold: i) the target network is queried with
random data and its predictions are used to create a fake dataset with the
knowledge of the network; and ii) a copycat network is trained with the fake
dataset and should be able to achieve similar performance as the target
network.
This hypothesis was evaluated locally in three problems (facial expression,
object, and crosswalk classification) and against a cloud-based API. In the
copy attacks, images from both non-problem domain and PD were used. All copycat
networks achieved at least 93.7% of the performance of the original models with
non-problem domain data, and at least 98.6% using additional data from the PD.
Additionally, the copycat CNN successfully copied at least 97.3% of the
performance of the Microsoft Azure Emotion API. Our results show that it is
possible to create a copycat CNN by simply querying a target network as
black-box with random non-labeled data.Comment: 8 pages, 3 figures, accepted by IJCNN 201
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
Deep learning techniques have enabled the emergence of state-of-the-art
models to address object detection tasks. However, these techniques are
data-driven, delegating the accuracy to the training dataset which must
resemble the images in the target task. The acquisition of a dataset involves
annotating images, an arduous and expensive process, generally requiring time
and manual effort. Thus, a challenging scenario arises when the target domain
of application has no annotated dataset available, making tasks in such
situation to lean on a training dataset of a different domain. Sharing this
issue, object detection is a vital task for autonomous vehicles where the large
amount of driving scenarios yields several domains of application requiring
annotated data for the training process. In this work, a method for training a
car detection system with annotated data from a source domain (day images)
without requiring the image annotations of the target domain (night images) is
presented. For that, a model based on Generative Adversarial Networks (GANs) is
explored to enable the generation of an artificial dataset with its respective
annotations. The artificial dataset (fake dataset) is created translating
images from day-time domain to night-time domain. The fake dataset, which
comprises annotated images of only the target domain (night images), is then
used to train the car detector model. Experimental results showed that the
proposed method achieved significant and consistent improvements, including the
increasing by more than 10% of the detection performance when compared to the
training with only the available annotated data (i.e., day images).Comment: 8 pages, 8 figures,
https://github.com/viniciusarruda/cross-domain-car-detection and accepted at
IJCNN 201
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