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1. Exploring System-level Coordination of Vehicular Electronics: A Case Study for Traction Control

   Kabir, M. R.; Ray, S. (October 2021, 6th IEEE Automotive Reliability, Test, and Safety Workshop (ARTS 2021))

   In current practice, exploring the computation and software level of individual ECUs of an automotive system does not seem feasible enough for a system-level understanding of vehicular electronics. Exploring vehicular system-level use cases requires exercising the communication and coordination of the constituent ECUs. We are developing a prototype environment, VIVE, to enable early exploration of system-level coordination. VIVE enables extensible use case definition, as well as smooth and seamless addition of new, compute, sensor, or actuation functionality. This solution is flexible and configurable in such a way that enables the user to exercise inter-component and intersystem interactions. In this paper, we demonstrate the utility of such a prototyping environment in the exploration of a traction control use case. 

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2. A Virtual Prototyping Platform for Exploration of Vehicular Electronics

   https://doi.org/10.1109/JIOT.2023.3267339  

   Kabir, Md Rafiul; Yedla Ravi, Bhagawat Baanav; Ray, Sandip (September 2023, IEEE Internet of Things Journal)

   A critical requirement for robust, optimized, and secure design of vehicular systems is the ability to do system-level exploration, i.e., comprehend the interactions involved among ECUs, sensors, and communication interfaces in realizing systemlevel use cases and the impact of various design choices on these interactions. This must be done early in the system design to enable the designer to make optimal design choices without requiring a cost-prohibitive design overhaul. In this paper, we develop a virtual prototyping environment for the modeling and simulation of vehicular systems. Our solution, VIVE, is modular and configurable, allowing the user to conveniently introduce new system-level use cases. Unlike other related simulation environments, our platform emphasizes coordination and communication among various vehicular components and just the abstraction of the necessary computation of each electronic control unit. We discuss the ability of VIVE to explore the interactions between a number of realistic use cases in the automotive domain. We demonstrate the utility of the platform, in particular, to create real-time in-vehicle communication optimizers for various optimization targets. We also show how to use such a prototyping environment to explore vehicular security compromises. Furthermore, we showcase the experimental integration and validation of the platform with a hardware setup in a real-time scenario. 

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3. Virtualization for Automotive Safety and Security Exploration

   https://doi.org/10.1109/DCAS57389.2023.10130221  

   Kabir, Md Rafiul; Ray, Sandip (April 2023, IEEE)

   A modern automobile system is a safety-critical distributed embedded system that incorporates more than a hundred Electronic Control Units, a wide range of sensors, and actuators, all connected with several in-vehicle networks. Obviously, integration of these heterogeneous components can lead to subtle errors that can be possibly exploited by malicious entities in the field, resulting in catastrophic consequences. We develop a prototyping platform to enable the functional safety and security exploration of automotive systems. The platform realizes a unique, extensible virtualization environment for the exploration of vehicular systems. The platform includes a CAN simulator that mimics the vehicular CAN bus to interact with various ECUs, together with sensory and actuation capabilities. We show how to explore these capabilities in the safety and security exploration through the analysis of a representative vehicular use case interaction. 

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4. VIVE: Virtualization of Vehicular Electronics for System-level Exploration

   Kabir, R; Mishra, N; Ray, S. (January 2021, 24th IEEE International Conference on Intelligent Transportation (ITSC 2021))

   null (Ed.)

   We develop a virtual prototyping infrastructure for modeling and simulation of automotive systems. We focus on exercising and exploring use cases involving system-level coordination of vehicular electronics, sensors, and software. In current practice, such use cases can only be explored late in the design when all the relevant hardware components are available. Any design change, e.g., for optimization or security or even functional errors found during the exploration, incurs prohibitive cost at that stage. Our solution is a flexible, configurable prototyping platform that enables the user to seamlessly add new system-level use cases. Unlike other related prototyping environments, the focus of our platform is on communication and coordination among different components, not the computation of individual Electronic Control Units. We report on the use of the platform for implementing several realistic usage scenarios on automotive platforms and exploring the effects of their interaction. In particular, we show how to use the platform to develop real-time in-vehicle communication optimizers for different optimization targets. 

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5. Towards Reverse Engineering Controller Area Network Messages Using Machine Learning

   https://doi.org/10.1109/WF-IoT48130.2020.9221383  

   Young, Clinton; Svoboda, Jordan; Zambreno, Joseph (June 2020, Proceedings of the IEEE World Forum on Internet of Things (WF-IoT))

   The automotive Controller Area Network (CAN) allows Electronic Control Units (ECUs) to communicate with each other and control various vehicular functions such as engine and braking control. Consequently CAN and ECUs are high priority targets for hackers. As CAN implementation details are held as proprietary information by vehicle manufacturers, it can be challenging to decode and correlate CAN messages to specific vehicle operations. To understand the precise meanings of CAN messages, reverse engineering techniques that are time-consuming, manually intensive, and require a physical vehicle are typically used. This work aims to address the process of reverse engineering CAN messages for their functionality by creating a machine learning classifier that analyzes messages and determines their relationship to other messages and vehicular functions. Our work examines CAN traffic of different vehicles and standards to show that it can be applied to a wide arrangement of vehicles. The results show that the function of CAN messages can be determined without the need to manually reverse engineer a physical vehicle. 

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