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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. 

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. 

Connected Autonomous Vehicle (CAV) applications have shown the promise of transformative impact on road safety, transportation experience, and sustainability. However, they open large and complex attack surfaces: an adversary can corrupt sensory and communication inputs with catastrophic results. A key challenge in development of security solutions for CAV applications is the lack of effective infrastructure for evaluating such solutions. In this paper, we address the problem by designing an automated, flexible evaluation infrastructure for CAV security solutions. Our tool, CAVELIER, provides an extensible evaluation architecture for CAV security solutions against compromised communication and sensor channels. The tool can be customized for a variety of CAV applications and to target diverse usage models. We illustrate the framework with a number of case studies for security resiliency evaluation in Cooperative Adaptive Cruise Control (CACC). 

Vehicles can utilize their sensors or receive messages from other vehicles to acquire information about the surrounding environments. However, the information may be inaccurate, faulty, or maliciously compromised due to sensor failures, communication faults, or security attacks. The goal of this work is to detect if a lane-changing decision and the sensed or received information are anomalous. We develop three anomaly detection approaches based on deep learning: a classifier approach, a predictor approach, and a hybrid approach combining the classifier and the predictor. All of them do not need anomalous data nor lateral features so that they can generally consider lane-changing decisions before the vehicles start moving along the lateral axis. They achieve at least 82% and up to 93% F1 scores against anomaly on data from Simulation of Urban MObility (SUMO) and HighD. We also examine system properties and verify that the detected anomaly includes more dangerous scenarios. 

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. 

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. I 

Autonomous vehicle-following systems, including Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), improve safety, efficiency, and string stability for a vehicle (the ego vehicle) following its leading vehicle. The ego vehicle senses or receives information, such as the position, velocity, acceleration, or even intention, of the leading vehicle and controls its own behavior. However, it has been shown that sensors and wireless channels are vulnerable to security attacks, and attackers can modify data sensed from sensors or received from other vehicles. To address this problem, in this paper, we design three types of stealthy attacks on ACC or CACC inputs, where the stealthy attacks can deceive a rule-based detection approach and impede system properties (collision-freeness and vehicle-following distance). We then develop two deep-learning models, a predictor-based model and an encoder-decoder-based model to detect the attacks, where the two models do not need attacker models for training. The experimental results demonstrate the respective strengths of different models and lead to a methodology for the design of learning-based intrusion detection approaches.