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Perception sensors, particularly camera and Lidar, are key elements of Autonomous Driving Systems (ADS) that enable them to comprehend their surroundings to informed driving and control decisions. Therefore, developing realistic simulation…
Cameras capture images that are essential for many safety-critical tasks. To process these images, a complex pipeline with multiple layers is used. Security attacks on this pipeline can severely affect passenger safety and system…
Efficient key management for automotive networks (CAN) is a critical element, governing the adoption of security in the next generation of vehicles. A recent promising approach for dynamic key agreement between groups of nodes,…
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 studied. Among different attacks on AD perception, the…
Vision transformer (ViT) models, when coupled with interpretation models, are regarded as secure and challenging to deceive, making them well-suited for security-critical domains such as medical applications, autonomous vehicles, drones,…
Autonomous vehicles (AVs) are characterized by pervasive datafication and surveillance through sensors like in-cabin cameras, LIDAR, and GPS. Drawing on 16 semi-structured interviews with AV drivers analyzed using constructivist grounded…
Autonomous driving (AD) systems are often built and tested in a modular fashion, where the performance of different modules is measured using task-specific metrics. These metrics should be chosen so as to capture the downstream impact of…
We propose a new real-world attack against the computer vision based systems of autonomous vehicles (AVs). Our novel Sign Embedding attack exploits the concept of adversarial examples to modify innocuous signs and advertisements in the…
Cyberattacks are becoming more frequent, and attackers can use different mechanisms, such as denial of service (DoS) and false data injection (FDI). Furthermore, multiple attack types can be launched simultaneously, known as hybrid attacks,…
Full-stack autonomous driving perception modules usually consist of data-driven models based on multiple sensor modalities. However, these models might be biased to the sensor setup used for data acquisition. This bias can seriously impair…
Large-scale deployment of autonomous vehicles has been continually delayed due to safety concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of which would result in vulnerability to rare but complex…
The Internet of Vehicles (IoV) is transforming transportation by enhancing connectivity and enabling autonomous driving. However, this increased interconnectivity introduces new security vulnerabilities. Bot malware and cyberattacks pose…
Recent work has shown that the introduction of autonomous vehicles (AVs) in traffic could help reduce traffic jams. Deep reinforcement learning methods demonstrate good performance in complex control problems, including autonomous vehicle…
Transportation systems have long been shaped by complexity and heterogeneity, driven by the interdependency of agent actions and traffic outcomes. The deployment of automated vehicles (AVs) in such systems introduces a new challenge:…
Advanced Driver-Assistance Systems (ADAS) have been thriving and widely deployed in recent years. In general, these systems receive sensor data, compute driving decisions, and output control signals to the vehicles. To smooth out the…
Vehicle-to-everything (V2X) communication is expected to be a prominent component of the sixth generation (6G) to accomplish intelligent transportation systems (ITS). Autonomous vehicles relying only on onboard sensors cannot bypass the…
In recent advancements in connected and autonomous vehicles (CAVs), automotive ethernet has emerged as a critical technology for in-vehicle networks (IVNs), superseding traditional protocols like the CAN due to its superior bandwidth and…
As augmented reality (AR) becomes increasingly integrated into everyday life, ensuring the safety and trustworthiness of its virtual content is critical. Our research addresses the risks of task-detrimental AR content, particularly that…
In the autonomous driving domain, data collection and annotation from real vehicles are expensive and sometimes unsafe. Simulators are often used for data augmentation, which requires realistic sensor models that are hard to formulate and…
This article investigates the robustness of vision systems in Connected and Autonomous Vehicles (CAVs), which is critical for developing Level-5 autonomous driving capabilities. Safe and reliable CAV navigation undeniably depends on robust…