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1. A Trust Management Framework for Connected Autonomous Vehicles Using Interaction Provenance

   https://doi.org/10.1109/ICC45855.2022.9838476  

   Hoque, Mohammad Aminul; Hasan, Ragib (May 2022, ICC 2022 - IEEE International Conference on Communications)

   Connected autonomous vehicles (CAVs) have fostered the development of intelligent transportation systems that support critical safety information sharing with minimum latency and making driving decisions autonomously. However, the CAV environment is vulnerable to different external and internal attacks. Authorized but malicious entities which provide wrong information impose challenges in preventing internal attacks. An essential requirement for thwarting internal attacks is to identify the trustworthiness of the vehicles. This paper exploits interaction provenance to propose a trust management framework for CAVs that considers both in-vehicle and vehicular network security incidents, supports flexible security policies and ensures privacy. The framework contains an interaction provenance recording and trust management protocol that extracts events from interaction provenance and calculates trustworthiness using fuzzy policies based on the events. Simulation results show that the framework is effective and can be integrated with the CAV stack with minimal computation and communication overhead. 

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2. Evaluations of Cyberattacks on Cooperative Control of Connected and Autonomous Vehicles at Bottleneck Points

   Ahmad, HM; Sabouni, Ehsan; Xiao, Wei; Cassandras, Christos G; Li, Wenchao (February 2023, Symposium on Vehicles Security and Privacy (VehicleSec) 2023)

   In this paper we analyze the effect of cyberattacks on cooperative control of connected and autonomous vehicles (CAVs) at traffic bottleneck points. We focus on three types of such bottleneck points including merging roadways, intersections and roundabouts. The coordination amongst CAVs in the network is achieved in a decentralized manner whereby each CAV formulates its own optimal control problem and solves it onboard in real time. A roadside unit is introduced to act as the coordinator that communicates and exchanges relevant data with the CAVs through wireless V2X communication. We show that this CAV setup is vulnerable to various cyberattacks such as Sybil attack, jamming attack and false data injection attack. Results from our simulation experiments call attention to the extent to which such attacks may jeopardize the coordination performance and the safety of the CAVs. 

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3. Exploring Safety–Stability Tradeoffs in Cooperative CAV Platoon Controls with Bidirectional Impacts

   https://doi.org/10.3390/s24051614  

   Wei, Yu; He, Xiaozheng (March 2024, Sensors)

   Advanced sensing technologies and communication capabilities of Connected and Autonomous Vehicles (CAVs) empower them to capture the dynamics of surrounding vehicles, including speeds and positions of those behind, enabling judicious responsive maneuvers. The acquired dynamics information of vehicles spurred the development of various cooperative platoon controls, particularly designed to enhance platoon stability with reduced spacing for reliable roadway capacity increase. These controls leverage abundant information transmitted through various communication topologies. Despite these advancements, the impact of different vehicle dynamics information on platoon safety remains underexplored, as current research predominantly focuses on stability analysis. This knowledge gap highlights the critical need for further investigation into how diverse vehicle dynamics information influences platoon safety. To address this gap, this research introduces a novel framework based on the concept of phase shift, aiming to scrutinize the tradeoffs between the safety and stability of CAV platoons formed upon bidirectional information flow topology. Our investigation focuses on platoon controls built upon bidirectional information flow topologies using diverse dynamics information of vehicles. Our research findings emphasize that the integration of various types of information into CAV platoon controls does not universally yield benefits. Specifically, incorporating spacing information can enhance both platoon safety and string stability. In contrast, velocity difference information can improve either safety or string stability, but not both simultaneously. These findings offer valuable insights into the formulation of CAV platoon control principles built upon diverse communication topologies. This research contributes a nuanced understanding of the intricate interplay between safety and stability in CAV platoons, emphasizing the importance of information dynamics in shaping effective control strategies. 

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4. WIP: Infrared Laser Reflection Attack Against Traffic Sign Recognition Systems

   Sato, Takami; Bhupathiraju, Sri Hrushikesh; Clifford, Michael; Sugawara, Takeshi; Chen, Qi Alfred; Rampazzi, Sara (January 2023, ISOC Symposium on Vehicle Security and Privacy (VehicleSec))

   All vehicles must follow the rules that govern traffic behavior, regardless of whether the vehicles are human-driven or Connected, Autonomous Vehicles (CAVs). Road signs indicate locally active rules, such as speed limits and requirements to yield or stop. Recent research has demonstrated attacks, such as adding stickers or dark patches to signs, that cause CAV sign misinterpretation, resulting in potential safety issues. Humans can see and potentially defend against these attacks. But humans can not detect what they can not observe. We have developed the first physical-world attack against CAV traffic sign recognition systems that is invisible to humans. Utilizing Infrared Laser Reflection (ILR), we implement an attack that affects CAV cameras, but humans can not perceive. In this work, we formulate the threat model and requirements for an ILR-based sign perception attack. Next, we evaluate attack effectiveness against popular, CNNbased traffic sign recognition systems. We demonstrate a 100% success rate against stop and speed limit signs in our laboratory evaluation. Finally, we discuss the next steps in our research. 

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5. Cooperative Driving of Connected Autonomous Vehicles using Responsibility Sensitive Safety Rules: A Control Barrier Functions Approach

   MOHAMMAD KHAYATIAN, VECNA ROBOTICS MOHAMMADREZA (June 2024, ACM transactions on cyberphysical systems)

   Connected Autonomous Vehicles (CAVs) are expected to enable reliable, efficient, and intelligent transportation systems. Most motion planning algorithms for multi-agent systems implicitly assume that all vehicles/agents will execute the expected plan with a small error and evaluate their safety constraints based on this fact. This assumption, however, is hard to keep for CAVs since they may have to change their plan (e.g., to yield to another vehicle) or are forced to stop (e.g., A CAV may break down). While it is desired that a CAV never gets involved in an accident, it may be hit by other vehicles and sometimes, preventing the accident is impossible (e.g., getting hit from behind while waiting behind the red light). Responsibility-Sensitive Safety (RSS) is a set of safety rules that defines the objective of CAV to blame, instead of safety. Thus, instead of developing a CAV algorithm that will avoid any accident, it ensures that the ego vehicle will not be blamed for any accident it is a part of. Original RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road scenarios, plus RSS rules do not prevent deadlock situations among vehicles. In this paper, we propose a new formulation for RSS rules that can be applied to any driving scenario. We integrate the proposed RSS rules with the CAV’s motion planning algorithm to enable cooperative driving of CAVs. We use Control Barrier Functions to enforce safety constraints and compute the energy optimal trajectory for the ego CAV. Finally, to ensure liveness, our approach detects and resolves deadlocks in a decentralized manner. We have conducted different experiments to verify that the ego CAV does not cause an accident no matter when other CAVs slow down or stop. We also showcase our deadlock detection and resolution mechanism using our simulator. Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously with the case that they are autonomous and connected. 

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