On adversarial robustness of trajectory prediction for autonomous vehicles
Trajectory prediction is a critical component for autonomous vehicles (AVs) to perform safe
planning and navigation. However, few studies have analyzed the adversarial robustness of …
planning and navigation. However, few studies have analyzed the adversarial robustness of …
Advdo: Realistic adversarial attacks for trajectory prediction
Trajectory prediction is essential for autonomous vehicles (AVs) to plan correct and safe
driving behaviors. While many prior works aim to achieve higher prediction accuracy, few …
driving behaviors. While many prior works aim to achieve higher prediction accuracy, few …
Robust trajectory prediction against adversarial attacks
Trajectory prediction using deep neural networks (DNNs) is an essential component of
autonomous driving (AD) systems. However, these methods are vulnerable to adversarial …
autonomous driving (AD) systems. However, these methods are vulnerable to adversarial …
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 …
Physgan: Generating physical-world-resilient adversarial examples for autonomous driving
Abstract Although Deep neural networks (DNNs) are being pervasively used in vision-based
autonomous driving systems, they are found vulnerable to adversarial attacks where small …
autonomous driving systems, they are found vulnerable to adversarial attacks where small …
Adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles
V Behzadan, A Munir - IEEE Intelligent Transportation Systems …, 2019 - ieeexplore.ieee.org
With the rapidly growing interest in autonomous navigation, the body of research on motion
planning and collision avoidance techniques has enjoyed an accelerating rate of novel …
planning and collision avoidance techniques has enjoyed an accelerating rate of novel …
Cat: Closed-loop adversarial training for safe end-to-end driving
Driving safety is a top priority for autonomous vehicles. Orthogonal to prior work handling
accident-prone traffic events by algorithm designs at the policy level, we investigate a …
accident-prone traffic events by algorithm designs at the policy level, we investigate a …
An analysis of adversarial attacks and defenses on autonomous driving models
Nowadays, autonomous driving has attracted much attention from both industry and
academia. Convolutional neural network (CNN) is a key component in autonomous driving …
academia. Convolutional neural network (CNN) is a key component in autonomous driving …
Attacks on machine learning: Adversarial examples in connected and autonomous vehicles
P Sharma, D Austin, H Liu - 2019 IEEE International …, 2019 - ieeexplore.ieee.org
Connected and autonomous vehicles (CAV aka driverless cars) offset human response for
transportation infrastructure, enhancing traffic efficiency, travel leisure, and road safety …
transportation infrastructure, enhancing traffic efficiency, travel leisure, and road safety …
Are self-driving cars secure? evasion attacks against deep neural networks for steering angle prediction
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 …
driving cars. However, the security of DNN models in this context leads to major safety …