6,844 research outputs found
Procedural Modeling and Physically Based Rendering for Synthetic Data Generation in Automotive Applications
We present an overview and evaluation of a new, systematic approach for
generation of highly realistic, annotated synthetic data for training of deep
neural networks in computer vision tasks. The main contribution is a procedural
world modeling approach enabling high variability coupled with physically
accurate image synthesis, and is a departure from the hand-modeled virtual
worlds and approximate image synthesis methods used in real-time applications.
The benefits of our approach include flexible, physically accurate and scalable
image synthesis, implicit wide coverage of classes and features, and complete
data introspection for annotations, which all contribute to quality and cost
efficiency. To evaluate our approach and the efficacy of the resulting data, we
use semantic segmentation for autonomous vehicles and robotic navigation as the
main application, and we train multiple deep learning architectures using
synthetic data with and without fine tuning on organic (i.e. real-world) data.
The evaluation shows that our approach improves the neural network's
performance and that even modest implementation efforts produce
state-of-the-art results.Comment: The project web page at
http://vcl.itn.liu.se/publications/2017/TKWU17/ contains a version of the
paper with high-resolution images as well as additional materia
Attack Resilience and Recovery using Physical Challenge Response Authentication for Active Sensors Under Integrity Attacks
Embedded sensing systems are pervasively used in life- and security-critical
systems such as those found in airplanes, automobiles, and healthcare.
Traditional security mechanisms for these sensors focus on data encryption and
other post-processing techniques, but the sensors themselves often remain
vulnerable to attacks in the physical/analog domain. If an adversary
manipulates a physical/analog signal prior to digitization, no amount of
digital security mechanisms after the fact can help. Fortunately, nature
imposes fundamental constraints on how these analog signals can behave. This
work presents PyCRA, a physical challenge-response authentication scheme
designed to protect active sensing systems against physical attacks occurring
in the analog domain. PyCRA provides security for active sensors by continually
challenging the surrounding environment via random but deliberate physical
probes. By analyzing the responses to these probes, and by using the fact that
the adversary cannot change the underlying laws of physics, we provide an
authentication mechanism that not only detects malicious attacks but provides
resilience against them. We demonstrate the effectiveness of PyCRA through
several case studies using two sensing systems: (1) magnetic sensors like those
found wheel speed sensors in robotics and automotive, and (2) commercial RFID
tags used in many security-critical applications. Finally, we outline methods
and theoretical proofs for further enhancing the resilience of PyCRA to active
attacks by means of a confusion phase---a period of low signal to noise ratio
that makes it more difficult for an attacker to correctly identify and respond
to PyCRA's physical challenges. In doing so, we evaluate both the robustness
and the limitations of PyCRA, concluding by outlining practical considerations
as well as further applications for the proposed authentication mechanism.Comment: Shorter version appeared in ACM ACM Conference on Computer and
Communications (CCS) 201
MScMS-II: an innovative IR-based indoor coordinate measuring system for large-scale metrology applications
According to the current great interest concerning large-scale metrology applications in many different fields of manufacturing industry, technologies and techniques for dimensional measurement have recently shown a substantial improvement. Ease-of-use, logistic and economic issues, as well as metrological performance are assuming a more and more important role among system requirements. This paper describes the architecture and the working principles of a novel infrared (IR) optical-based system, designed to perform low-cost and easy indoor coordinate measurements of large-size objects. The system consists of a distributed network-based layout, whose modularity allows fitting differently sized and shaped working volumes by adequately increasing the number of sensing units. Differently from existing spatially distributed metrological instruments, the remote sensor devices are intended to provide embedded data elaboration capabilities, in order to share the overall computational load. The overall system functionalities, including distributed layout configuration, network self-calibration, 3D point localization, and measurement data elaboration, are discussed. A preliminary metrological characterization of system performance, based on experimental testing, is also presente
White paper on Selected Environmental Parameters affecting Autonomous Vehicle (AV) Sensors
Autonomous Vehicles (AVs) being developed these days rely on various sensor
technologies to sense and perceive the world around them. The sensor outputs
are subsequently used by the Automated Driving System (ADS) onboard the vehicle
to make decisions that affect its trajectory and how it interacts with the
physical world. The main sensor technologies being utilized for sensing and
perception (S&P) are LiDAR (Light Detection and Ranging), camera, RADAR (Radio
Detection and Ranging), and ultrasound. Different environmental parameters
would have different effects on the performance of each sensor, thereby
affecting the S&P and decision-making (DM) of an AV. In this publication, we
explore the effects of different environmental parameters on LiDARs and
cameras, leading us to conduct a study to better understand the impact of
several of these parameters on LiDAR performance. From the experiments
undertaken, the goal is to identify some of the weaknesses and challenges that
a LiDAR may face when an AV is using it. This informs AV regulators in
Singapore of the effects of different environmental parameters on AV sensors so
that they can determine testing standards and specifications which will assess
the adequacy of LiDAR systems installed for local AV operations more robustly.
Our approach adopts the LiDAR test methodology first developed in the Urban
Mobility Grand Challenge (UMGC-L010) White Paper on LiDAR performance against
selected Automotive Paints.Comment: 25 pages, 20 figures. This white paper was developed with support
from the Urban Mobility Grand Challenge Fund by the Land Transport Authority
of Singapore (No. UMGC-L010). For associated dataset, see
https://researchdata.ntu.edu.sg/dataset.xhtml?persistentId=doi:10.21979/N9/NT8HIM.
arXiv admin note: substantial text overlap with arXiv:2309.0134
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