3,337 research outputs found
Proceedings, MSVSCC 2019
Old Dominion University Department of Modeling, Simulation & Visualization Engineering (MSVE) and the Virginia Modeling, Analysis and Simulation Center (VMASC) held the 13th annual Modeling, Simulation & Visualization (MSV) Student Capstone Conference on April 18, 2019.
The Conference featured student research and student projects that are central to MSV. Also participating in the conference were faculty members who volunteered their time to impart direct support to their students’ research, facilitated the various conference tracks, served as judges for each of the tracks, and provided overall assistance to the conference.
Appreciating the purpose of the conference and working in a cohesive, collaborative effort, resulted in a successful symposium for everyone involved. These proceedings feature the works that were presented at the conference.
Capstone Conference Chair: Dr. Yuzhong Shen Capstone Conference Student Chair: Daniel Pere
Deep Learning Based Malware Classification Using Deep Residual Network
The traditional malware detection approaches rely heavily on feature extraction procedure, in this paper we proposed a deep learning-based malware classification model by using a 18-layers deep residual network. Our model uses the raw bytecodes data of malware samples, converting the bytecodes to 3-channel RGB images and then applying the deep learning techniques to classify the malwares. Our experiment results show that the deep residual network model achieved an average accuracy of 86.54% by 5-fold cross validation. Comparing to the traditional methods for malware classification, our deep residual network model greatly simplify the malware detection and classification procedures, it achieved a very good classification accuracy as well. The dataset we used in this paper for training and testing is Malimg dataset, one of the biggest malware datasets released by vision research lab of UCSB
Sim2real and Digital Twins in Autonomous Driving: A Survey
Safety and cost are two important concerns for the development of autonomous
driving technologies. From the academic research to commercial applications of
autonomous driving vehicles, sufficient simulation and real world testing are
required. In general, a large scale of testing in simulation environment is
conducted and then the learned driving knowledge is transferred to the real
world, so how to adapt driving knowledge learned in simulation to reality
becomes a critical issue. However, the virtual simulation world differs from
the real world in many aspects such as lighting, textures, vehicle dynamics,
and agents' behaviors, etc., which makes it difficult to bridge the gap between
the virtual and real worlds. This gap is commonly referred to as the reality
gap (RG). In recent years, researchers have explored various approaches to
address the reality gap issue, which can be broadly classified into two
categories: transferring knowledge from simulation to reality (sim2real) and
learning in digital twins (DTs). In this paper, we consider the solutions
through the sim2real and DTs technologies, and review important applications
and innovations in the field of autonomous driving. Meanwhile, we show the
state-of-the-arts from the views of algorithms, models, and simulators, and
elaborate the development process from sim2real to DTs. The presentation also
illustrates the far-reaching effects of the development of sim2real and DTs in
autonomous driving
Discrete event simulation and virtual reality use in industry: new opportunities and future trends
This paper reviews the area of combined discrete
event simulation (DES) and virtual reality (VR) use within industry.
While establishing a state of the art for progress in this
area, this paper makes the case for VR DES as the vehicle of choice
for complex data analysis through interactive simulation models,
highlighting both its advantages and current limitations. This paper
reviews active research topics such as VR and DES real-time
integration, communication protocols, system design considerations,
model validation, and applications of VR and DES. While
summarizing future research directions for this technology combination,
the case is made for smart factory adoption of VR DES as
a new platform for scenario testing and decision making. It is put
that in order for VR DES to fully meet the visualization requirements
of both Industry 4.0 and Industrial Internet visions of digital
manufacturing, further research is required in the areas of lower
latency image processing, DES delivery as a service, gesture recognition
for VR DES interaction, and linkage of DES to real-time data streams and Big Data sets
Trajectory generation for lane-change maneuver of autonomous vehicles
Lane-change maneuver is one of the most thoroughly investigated automatic driving operations that can be used by an autonomous self-driving vehicle as a primitive for performing more complex operations like merging, entering/exiting highways or overtaking another vehicle. This thesis focuses on two coherent problems that are associated with the trajectory generation for lane-change maneuvers of autonomous vehicles in a highway scenario: (i) an effective velocity estimation of neighboring vehicles under different road scenarios involving linear and curvilinear motion of the vehicles, and (ii) trajectory generation based on the estimated velocities of neighboring vehicles for safe operation of self-driving cars during lane-change maneuvers. ^ We first propose a two-stage, interactive-multiple-model-based estimator to perform multi-target tracking of neighboring vehicles in a lane-changing scenario. The first stage deals with an adaptive window based turn-rate estimation for tracking maneuvering target vehicles using Kalman filter. In the second stage, variable-structure models with updated estimated turn-rate are utilized to perform data association followed by velocity estimation. Based on the estimated velocities of neighboring vehicles, piecewise Bezier-curve-based methods that minimize the safety/collision risk involved and maximize the comfort ride have been developed for the generation of desired trajectory for lane-change maneuvers. The proposed velocity-estimation and trajectory-generation algorithms have been validated experimentally using Pioneer3- DX mobile robots in a simulated lane-change environment as well as validated by computer simulations
Winning the 3rd Japan Automotive AI Challenge -- Autonomous Racing with the Autoware.Auto Open Source Software Stack
The 3rd Japan Automotive AI Challenge was an international online autonomous
racing challenge where 164 teams competed in December 2021. This paper outlines
the winning strategy to this competition, and the advantages and challenges of
using the Autoware.Auto open source autonomous driving platform for multi-agent
racing. Our winning approach includes a lane-switching opponent overtaking
strategy, a global raceline optimization, and the integration of various tools
from Autoware.Auto including a Model-Predictive Controller. We describe the use
of perception, planning and control modules for high-speed racing applications
and provide experience-based insights on working with Autoware.Auto. While our
approach is a rule-based strategy that is suitable for non-interactive
opponents, it provides a good reference and benchmark for learning-enabled
approaches.Comment: Accepted at Autoware Workshop at IV 202
Survey on some key technologies of virtual tourism system based on Web3D
Some key technologies on how to build large-scale virtual tourism systssems comprehensively on Web browsers and mobiles were analyzed and the current R&D status on Web3D virtual tourism was surveyed insightfully. Then, some methods were summarized, including 3D trees or plants modeling, 3D architectural modeling, 3D Virtual Human behavior modeling, virtual agents path planning, collision detection and progressive transmission strategy suitable for developing large scale Web3D tourism scenarios. Also, some bottleneck problems of Web3D virtual tourism system were investigated. At the same time, the lightweight 3D engine, the lightweight 3D modeling, the lightweight 3D streaming and P2P based progressive transmission of huge Web3D tourism contents would become much helpful to breakthrough those bottlenecks of Web3D tourism systems were pointed out. In addition, all kinds of Web3D engines in terms of lightweight, realism and efficiency that would be a good reference for developers to choose during various applications were compared comprehensively. Finally, the prospect of future investigation of Web3D tourism system is presented, which will be going on in terms of four characteristics lightweight, high-speed, realism, beauty.
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