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Consider a fleet of autonomous vehicles traversing an adversarial terrain that includes obstacles and mines. The goal of the fleet is to ensure that they can complete their mission safely (with minimal casualty) and efficiently (as quickly as possible). In Distributed Coordinated Fleet Management (DCFM), fleet members coordinate with one another while traversing the terrain, e.g., a vehicle encountering an obstacle at a location l can inform other agents so that they can recompute their route to avoid l. In this paper, we consider the problem of cyber-resilient DCFM, i.e., DCFM in an en- vironment where the adversary can additionally tamper with the cyber-communication performed by the fleet members. Our framework, DRIFT, enables fleet members to coordinate in the presence of such adversaries. Our extensive evaluations demonstrate that DRIFT can achieve a high degree of safety and efficiency against a large spectrum of communication adversaries. 

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.