Connected and automated vehicles: Infrastructure, applications, security, critical challenges, and future aspects
Autonomous vehicles (AV) are game-changing innovations that promise a safer, more
convenient, and environmentally friendly mode of transportation than traditional vehicles …
convenient, and environmentally friendly mode of transportation than traditional vehicles …
Adversarial attacks and countermeasures on image classification-based deep learning models in autonomous driving systems: A systematic review
The rapid development of artificial intelligence (AI) and breakthroughs in Internet of Things
(IoT) technologies have driven the innovation of advanced autonomous driving systems …
(IoT) technologies have driven the innovation of advanced autonomous driving systems …
A survey on safety-critical driving scenario generation—A methodological perspective
Autonomous driving systems have witnessed significant development during the past years
thanks to the advance in machine learning-enabled sensing and decision-making …
thanks to the advance in machine learning-enabled sensing and decision-making …
A survey on automated driving system testing: Landscapes and trends
Automated Driving Systems (ADS) have made great achievements in recent years thanks to
the efforts from both academia and industry. A typical ADS is composed of multiple modules …
the efforts from both academia and industry. A typical ADS is composed of multiple modules …
Towards understanding and enhancing robustness of deep learning models against malicious unlearning attacks
Given the availability of abundant data, deep learning models have been advanced and
become ubiquitous in the past decade. In practice, due to many different reasons (eg …
become ubiquitous in the past decade. In practice, due to many different reasons (eg …
Common corruption robustness of point cloud detectors: Benchmark and enhancement
Object detection through LiDAR-based point cloud has recently been important in
autonomous driving. Although achieving high accuracy on public benchmarks, the state-of …
autonomous driving. Although achieving high accuracy on public benchmarks, the state-of …
[HTML][HTML] An autonomous decision-making framework for gait recognition systems against adversarial attack using reinforcement learning
Gait identification based on Deep Learning (DL) techniques has recently emerged as
biometric technology for surveillance. We leveraged the vulnerabilities and decision-making …
biometric technology for surveillance. We leveraged the vulnerabilities and decision-making …
{AE-Morpher}: Improve Physical Robustness of Adversarial Objects against {LiDAR-based} Detectors via Object Reconstruction
LiDAR-based perception is crucial to ensure the safety and reliability of autonomous driving
(AD) systems. Though some adversarial attack methods against LiDAR-based detectors …
(AD) systems. Though some adversarial attack methods against LiDAR-based detectors …
Sok: On the semantic ai security in autonomous driving
Autonomous Driving (AD) systems rely on AI components to make safety and correct driving
decisions. Unfortunately, today's AI algorithms are known to be generally vulnerable to …
decisions. Unfortunately, today's AI algorithms are known to be generally vulnerable to …
3d adversarial augmentations for robust out-of-domain predictions
Since real-world training datasets cannot properly sample the long tail of the underlying data
distribution, corner cases and rare out-of-domain samples can severely hinder the …
distribution, corner cases and rare out-of-domain samples can severely hinder the …