Related papers: HackCar: a test platform for attacks and defenses …
Electric Vehicle (EV) charging infrastructure is critical for the widespread adoption of EVs, ensuring efficient and secure charging processes. Evaluating the security and performance of EV charging systems in real-world infrastructure…
This paper considers the analysis and design of resilient/robust decentralized control systems. Specifically, we aim to assess how the pairing of sensors and actuators lead to architectures that are resilient to attacks/hacks for industrial…
In Autonomous Vehicles (AVs), one fundamental pillar is perception, which leverages sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving environment. Due to its direct impact on road safety, multiple prior…
Commercial Adaptive Cruise Control (ACC) systems are increasingly available as standard options in modern vehicles. At the same time, still little information is openly available on how these systems actually operate and how different is…
The emergence of intelligent, connected vehicles, containing complex functionality has potential to greatly benefit society by improving safety, security and efficiency of vehicular transportation. Much of this has been enabled by…
In the automotive industry there is a need to handle broad quality deficiencies, eg, performance, maintainability, cybersecurity, safety, and privacy, to mention a few. The idea is to prevent these issues from reaching end-users, ie, road…
Despite recent advances in autonomous driving systems, accidents such as the fatal Uber crash in 2018 show these systems are still susceptible to edge cases. Such systems must be thoroughly tested and validated before being deployed in the…
Cyber security is one of the most significant challenges in connected vehicular systems and connected vehicles are prone to different cybersecurity attacks that endanger passengers' safety. Cyber security in intelligent connected vehicles…
Techniques for verifying or invalidating the security of computer systems have come a long way in recent years. Extremely sophisticated tools are available to specify and formally verify the behavior of a system and, at the same time,…
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,…
Fully autonomous vehicles are emerging vehicular technologies that have gained significant attention in todays research endeavours. Even though it promises to optimize road safety, the proliferation of wireless and sensor technologies makes…
The safety-critical nature of vehicle steering is one of the main motivations for exploring the space of possible cyber-physical attacks against the steering systems of modern vehicles. This paper investigates the adversarial capabilities…
Unmanned vehicles able to conduct advanced operations without human intervention are being developed at a fast pace for many purposes. Not surprisingly, they are also expected to significantly change how military operations can be…
Self-driving technology is expected to revolutionize different sectors and is seen as the natural evolution of road vehicles. In the last years, real-world validation of designed and virtually tested solutions is growing in importance since…
We demonstrate that a supply-chain level compromise of the adaptive cruise control (ACC) capability on equipped vehicles can be used to significantly degrade system level performance of current day mixed-autonomy freeway networks. Via a…
Deep learning has shown impressive performance on challenging perceptual tasks and has been widely used in software to provide intelligent services. However, researchers found deep neural networks vulnerable to adversarial examples. Since…
Action anticipation, intent prediction, and proactive behavior are all desirable characteristics for autonomous driving policies in interactive scenarios. Paramount, however, is ensuring safety on the road -- a key challenge in doing so is…
In autonomous driving, behavior prediction is fundamental for safe motion planning, hence the security and robustness of prediction models against adversarial attacks are of paramount importance. We propose a novel adversarial backdoor…
Backdoor attacks on deep neural networks have emerged as significant security threats, especially as DNNs are increasingly deployed in security-critical applications. However, most existing works assume that the attacker has access to the…
To autonomously control vehicles, driving agents use outputs from a combination of machine-learning (ML) models, controller logic, and custom modules. Although numerous prior works have shown that adversarial examples can mislead ML models…