185 citations · 202 across the 7 of their papers we have counts for
7 papers
LEAF + AIO: Edge-Assisted Energy-Aware Object Detection for Mobile Augmented Reality
Haoxin Wang, BaekGyu Kim, Jiang Xie +1
Today very few deep learning-based mobile augmented reality (MAR) applications are applied in mobile devices because they are significantly energy-guzzling. In this paper, we desig…
LiveMap: Real-Time Dynamic Map in Automotive Edge Computing
Qiang Liu, Tao Han, Jiang +2
Autonomous driving needs various line-of-sight sensors to perceive surroundings that could be impaired under diverse environment uncertainties such as visual occlusion and extreme…
Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications
Haoxin Wang, BaekGyu Kim, Jiang Xie +1
Applying deep learning to object detection provides the capability to accurately detect and classify complex objects in the real world. However, currently, few mobile applications…
Architectural Design Alternatives based on Cloud/Edge/Fog Computing for Connected Vehicles
Haoxin Wang, Tingting Liu, BaekGyu Kim +4
As vehicles playing an increasingly important role in people's daily life, requirements on safer and more comfortable driving experience have arisen. Connected vehicles (CVs) can p…
Runtime-Safety-Guided Policy Repair
Weichao Zhou, Ruihan Gao, BaekGyu Kim +2
We study the problem of policy repair for learning-based control policies in safety-critical settings. We consider an architecture where a high-performance learning-based control p…
Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction
Alesia Chernikova, Alina Oprea, Cristina Nita-Rotaru +1
Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-driving cars. However, the security of DNN models in this context leads to major safety impli…