Deep learning-based autonomous driving systems: A survey of attacks and defenses
The rapid development of artificial intelligence, especially deep learning technology, has
advanced autonomous driving systems (ADSs) by providing precise control decisions to …
advanced autonomous driving systems (ADSs) by providing precise control decisions to …
Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey
H Cao, W Zou, Y Wang, T Song, M Liu - arXiv preprint arXiv:2210.11237, 2022 - arxiv.org
Since the 2004 DARPA Grand Challenge, the autonomous driving technology has
witnessed nearly two decades of rapid development. Particularly, in recent years, with the …
witnessed nearly two decades of rapid development. Particularly, in recent years, with the …
Adversarial deep reinforcement learning for improving the robustness of multi-agent autonomous driving policies
Autonomous cars are well known for being vulnerable to adversarial attacks that can
compromise the safety of the car and pose danger to other road users. To effectively defend …
compromise the safety of the car and pose danger to other road users. To effectively defend …
Targeted attack on deep rl-based autonomous driving with learned visual patterns
Recent studies demonstrated the vulnerability of control policies learned through deep
reinforcement learning against adversarial attacks, raising concerns about the application of …
reinforcement learning against adversarial attacks, raising concerns about the application of …
Adversarially robust edge-based object detection for assuredly autonomous systems
Edge-based and autonomous, deep learning computer vision applications, such as those
used in surveillance or traffic management, must be assuredly correct and performant …
used in surveillance or traffic management, must be assuredly correct and performant …
ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events
Autonomous vehicles are advanced driving systems that are well known for being
vulnerable to various adversarial attacks, compromising the vehicle's safety, and posing …
vulnerable to various adversarial attacks, compromising the vehicle's safety, and posing …
Robust Few-Shot Learning Without Using Any Adversarial Samples
The high cost of acquiring and annotating samples has made the “few-shot” learning
problem of prime importance. Existing works mainly focus on improving performance on …
problem of prime importance. Existing works mainly focus on improving performance on …
Certified Robust Control under Adversarial Perturbations
Autonomous systems increasingly rely on machine learning techniques to transform high-
dimensional raw inputs into predictions that are then used for decision-making and control …
dimensional raw inputs into predictions that are then used for decision-making and control …
Securing Autonomous Driving: Addressing Adversarial Attacks and Defenses
J Yang - 2023 - search.proquest.com
Autonomous driving systems have gained significant attention in recent years,
revolutionizing the transportation industry. However, the increasing complexity and …
revolutionizing the transportation industry. However, the increasing complexity and …
Physically Realizable Targeted Adversarial Attacks on Autonomous Driving
P Buddareddygari - 2021 - search.proquest.com
Autonomous Driving (AD) systems are being researched and developed actively in recent
days to solve the task of controlling the vehicles safely without human intervention. One …
days to solve the task of controlling the vehicles safely without human intervention. One …