11,245 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
Autonomous Vehicles: Open-Source Technologies, Considerations, and Development
Autonomous vehicles are the culmination of advances in many areas such as
sensor technologies, artificial intelligence (AI), networking, and more. This
paper will introduce the reader to the technologies that build autonomous
vehicles. It will focus on open-source tools and libraries for autonomous
vehicle development, making it cheaper and easier for developers and
researchers to participate in the field. The topics covered are as follows.
First, we will discuss the sensors used in autonomous vehicles and summarize
their performance in different environments, costs, and unique features. Then
we will cover Simultaneous Localization and Mapping (SLAM) and algorithms for
each modality. Third, we will review popular open-source driving simulators, a
cost-effective way to train machine learning models and test vehicle software
performance. We will then highlight embedded operating systems and the security
and development considerations when choosing one. After that, we will discuss
Vehicle-to-Vehicle (V2V) and Internet-of-Vehicle (IoV) communication, which are
areas that fuse networking technologies with autonomous vehicles to extend
their functionality. We will then review the five levels of vehicle automation,
commercial and open-source Advanced Driving Assistance Systems, and their
features. Finally, we will touch on the major manufacturing and software
companies involved in the field, their investments, and their partnerships.
These topics will give the reader an understanding of the industry, its
technologies, active research, and the tools available for developers to build
autonomous vehicles.Comment: 13 pages, 7 figure
Procedural City Generation with Combined Architectures for Real-time Visualization
The work and research of this paper sought to build upon traditional city generation and simulation in creating a tool that both realistically simulates cities and their prominent features and also creates aesthetic and artistically rich cities using assets that combine several contemporary or near contemporary architectural styles. The major city features simulated are the surrounding terrain, road networks, individual buildings, and building placement. The tools used to both create and integrate these features were created in Houdini with Unreal Engine 5 as the intended final destination. This research was influenced by the city, town, and road networking of Ghost Recon:Wildlands. Both games exhibit successful creation and integration of cities in a real-time open world that creates a holistic and visually compelling experience. The software used in the development of this project were Houdini, Maya, Unreal Engine 5, and Zbrush, as well as Adobe Substance Designer, Substance Painter, and Photoshop. The city generation tool was built with the intent that it would be flexible. In this context flexibility refers to the capability to create many different kinds of city regions based on user specifications. Region size, road density and connectivity, and building types are examples of qualities of the city that can be directly controlled. The tool currently uses one set of city assets created with intent for use together and an overall design cohesion but is also built flexibly enough that new building assets could be included, only requiring the addition of building generators for the new set. Alternatively, assets developed with the current generation methods in mind could also be used to change the visual style of the city. Buildings were both generated and placed based on a district classification. Buildings were established as small residential, large residential, religious buildings, and government/commercial before being placed in appropriate locations in the city based on user district specifications
GIS Data Based Automatic High-Fidelity 3D Road Network Modeling
3D road models are widely used in many computer applications such as racing games and driving simulations_ However, almost all high-fidelity 3D road models were generated manually by professional artists at the expense of intensive labor. There are very few existing methods for automatically generating 3D high-fidelity road networks, especially those existing in the real world. This paper presents a novel approach thai can automatically produce 3D high-fidelity road network models from real 2D road GIS data that mainly contain road. centerline in formation. The proposed method first builds parametric representations of the road centerlines through segmentation and fitting . A basic set of civil engineering rules (e.g., cross slope, superelevation, grade) for road design are then selected in order to generate realistic road surfaces in compliance with these rules. While the proposed method applies to any types of roads, this paper mainly addresses automatic generation of complex traffic interchanges and intersections which are the most sophisticated elements in the road network
Autonomics: In Search of a Foundation for Next Generation Autonomous Systems
The potential benefits of autonomous systems have been driving intensive
development of such systems, and of supporting tools and methodologies.
However, there are still major issues to be dealt with before such development
becomes commonplace engineering practice, with accepted and trustworthy
deliverables. We argue that a solid, evolving, publicly available,
community-controlled foundation for developing next generation autonomous
systems is a must. We discuss what is needed for such a foundation, identify a
central aspect thereof, namely, decision-making, and focus on three main
challenges: (i) how to specify autonomous system behavior and the associated
decisions in the face of unpredictability of future events and conditions and
the inadequacy of current languages for describing these; (ii) how to carry out
faithful simulation and analysis of system behavior with respect to rich
environments that include humans, physical artifacts, and other systems,; and
(iii) how to engineer systems that combine executable model-driven techniques
and data-driven machine learning techniques. We argue that autonomics, i.e.,
the study of unique challenges presented by next generation autonomous systems,
and research towards resolving them, can introduce substantial contributions
and innovations in system engineering and computer science
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and
analysis of perception systems, especially those based on machine learning.
Specifically, we consider the problems of training a perception system to
handle rare events, testing its performance under different conditions, and
debugging failures. We show how a probabilistic programming language can help
address these problems by specifying distributions encoding interesting types
of inputs and sampling these to generate specialized training and test sets.
More generally, such languages can be used for cyber-physical systems and
robotics to write environment models, an essential prerequisite to any formal
analysis. In this paper, we focus on systems like autonomous cars and robots,
whose environment is a "scene", a configuration of physical objects and agents.
We design a domain-specific language, Scenic, for describing "scenarios" that
are distributions over scenes. As a probabilistic programming language, Scenic
allows assigning distributions to features of the scene, as well as
declaratively imposing hard and soft constraints over the scene. We develop
specialized techniques for sampling from the resulting distribution, taking
advantage of the structure provided by Scenic's domain-specific syntax.
Finally, we apply Scenic in a case study on a convolutional neural network
designed to detect cars in road images, improving its performance beyond that
achieved by state-of-the-art synthetic data generation methods.Comment: 41 pages, 36 figures. Full version of a PLDI 2019 paper (extending UC
Berkeley EECS Department Tech Report No. UCB/EECS-2018-8
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