End-to-end uncertainty-based mitigation of adversarial attacks to automated lane centering
In the development of advanced driver-assistance systems (ADAS) and autonomous
vehicles, machine learning techniques that are based on deep neural networks (DNNs) …
vehicles, machine learning techniques that are based on deep neural networks (DNNs) …
Wip: End-to-end analysis of adversarial attacks to automated lane centering systems
Machine learning techniques, particularly those based on deep neural networks (DNNs), are
widely adopted in the development of advanced driver-assistance systems (ADAS) and …
widely adopted in the development of advanced driver-assistance systems (ADAS) and …
[PDF][PDF] Hold tight and never let go: Security of deep learning based automated lane centering under physical-world attack
ABSTRACT Automated Lane Centering (ALC) systems are convenient and widely deployed
today, but also highly security and safety critical. In this work, we are the first to …
today, but also highly security and safety critical. In this work, we are the first to …
The vulnerability of semantic segmentation networks to adversarial attacks in autonomous driving: Enhancing extensive environment sensing
Enabling autonomous driving (AD) can be considered one of the biggest challenges in
today? s technology. AD is a complex task accomplished by several functionalities, with …
today? s technology. AD is a complex task accomplished by several functionalities, with …
Feasibility and suppression of adversarial patch attacks on end-to-end vehicle control
S Pavlitskaya, S Ünver… - 2020 IEEE 23rd …, 2020 - ieeexplore.ieee.org
In an end-to-end vehicle control scenario, where a deep neural network is trained on visual
input solely, adversarial vulnerability leaves a possibility to manipulate the steering …
input solely, adversarial vulnerability leaves a possibility to manipulate the steering …
Does physical adversarial example really matter to autonomous driving? towards system-level effect of adversarial object evasion attack
In autonomous driving (AD), accurate perception is indispensable to achieving safe and
secure driving. Due to its safety-criticality, the security of AD perception has been widely …
secure driving. Due to its safety-criticality, the security of AD perception has been widely …
A context-aware black-box adversarial attack for deep driving maneuver classification models
In a connected autonomous vehicle (CAV) scenario, each vehicle utilizes an onboard deep
neural network (DNN) model to understand its received time-series driving signals (eg …
neural network (DNN) model to understand its received time-series driving signals (eg …
Adversarial attacks on multi-task visual perception for autonomous driving
Deep neural networks (DNNs) have accomplished impressive success in various
applications, including autonomous driving perception tasks, in recent years. On the other …
applications, including autonomous driving perception tasks, in recent years. On the other …
Adversarial driving: Attacking end-to-end autonomous driving
As research in deep neural networks advances, deep convolutional networks become
promising for autonomous driving tasks. In particular, there is an emerging trend of …
promising for autonomous driving tasks. In particular, there is an emerging trend of …
Adaptive adversarial videos on roadside billboards: Dynamically modifying trajectories of autonomous vehicles
Deep neural networks (DNNs) are being incorporated into various autonomous systems like
self-driving cars and robots. However, there is a rising concern about the robustness of …
self-driving cars and robots. However, there is a rising concern about the robustness of …
相关搜索
- adversarial attacks automated lane
- mitigation of adversarial attacks
- adversarial attacks autonomous driving
- analysis of adversarial attacks
- adversarial attacks environment sensing
- adversarial attacks segmentation networks
- roadside billboards autonomous vehicles
- scene segmentation autonomous vehicles
- black box adversarial attack
- adversarial patch attacks
- vehicle control patch attacks
- adversarial attack autonomous vehicles
- adversarial attack scene segmentation
- autonomous driving environment sensing
- autonomous driving segmentation networks