Výsledky vyhledávání - "Ren, Haotian"

Report

Autor: Li, Zhongnian, Ren, Haotian, Sun, Tongfeng, Li, Zhichen

Multi-abel Learning (MLL) often involves the assignment of multiple relevant labels to each instance, which can lead to the leakage of sensitive information (such as smoking, diseases, etc.) about the instances. However, existing MLL suffer from fail

Externí odkaz: http://arxiv.org/abs/2312.13312

Zobrazit plný text záznamu

Report

Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems

Autor: Zhou, Xugui, Chen, Anqi, Kouzel, Maxfield, Ren, Haotian, McCarty, Morgan, Nita-Rotaru, Cristina, Alemzadeh, Homa

Adaptive Cruise Control (ACC) is a widely used driver assistance technology for maintaining the desired speed and safe distance to the leading vehicle. This paper evaluates the security of the deep neural network (DNN) based ACC systems under runtime

Externí odkaz: http://arxiv.org/abs/2307.08939

Zobrazit plný text záznamu

Report

Design and Validation of an Open-Source Closed-Loop Testbed for Artificial Pancreas Systems

Autor: Zhou, Xugui, Kouzel, Maxfield, Ren, Haotian, Alemzadeh, Homa

The development of a fully autonomous artificial pancreas system (APS) to independently regulate the glucose levels of a patient with Type 1 diabetes has been a long-standing goal of diabetes research. A significant barrier to progress is the difficu

Externí odkaz: http://arxiv.org/abs/2208.06479

Zobrazit plný text záznamu

Report

Strategic Safety-Critical Attacks Against an Advanced Driver Assistance System

Autor: Zhou, Xugui, Schmedding, Anna, Ren, Haotian, Yang, Lishan, Schowitz, Philip, Smirni, Evgenia, Alemzadeh, Homa

A growing number of vehicles are being transformed into semi-autonomous vehicles (Level 2 autonomy) by relying on advanced driver assistance systems (ADAS) to improve the driving experience. However, the increasing complexity and connectivity of ADAS

Externí odkaz: http://arxiv.org/abs/2204.06768

Zobrazit plný text záznamu