An official website of the United States government
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).
more »
« less
REDEM: Real-Time Detection and Mitigation of Communication Attacks in Connected Autonomous Vehicle Applications
Emergent vehicles will support a variety of connected applications, where a vehicle communicates with other vehicles or with the infrastructure to make a variety of decisions. Cooperative connected applications provide a critical foundational pillar for autonomous driving, and hold the promise of improving road safety, efficiency and environmental sustainability. However, they also induce a large and easily exploitable attack surface: an adversary can manipulate vehicular communications to subvert functionality of participating individual vehicles, cause catastrophic accidents, or bring down the transportation infrastructure. In this paper we outline a potential direction to address this critical problem through a resiliency framework, REDEM, based on machine learning. REDEM has several interesting features, including (1) smooth integration with the architecture of the underlying application, (2) ability to handle diverse communication attacks within the same underlying foundation, and (3) real-time detection and mitigation capability. We present the vision of REDEM, identify some key challenges to be addressed in its realization, and discuss the kind of evaluation/analysis necessary for its viability. We also present initial results from one instantiation of REDEM introducing resiliency in Cooperative Adaptive Cruise Control (CACC).
more »
« less
- Award ID(s):
- 1908549
- PAR ID:
- 10191934
- Date Published:
- Journal Name:
- IFIP advances in information and communication technology
- Volume:
- 574
- ISSN:
- 1868-4238
- Page Range / eLocation ID:
- 105-122
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Given the aging infrastructure and the anticipated growing number of highway work zones in the U.S.A., it is important to investigate work zone merge control, which is critical for improving work zone safety and capacity. This paper proposes and evaluates a novel highway work zone merge control strategy based on cooperative driving behavior enabled by artificial intelligence. The proposed method assumes that all vehicles are fully automated, connected, and cooperative. It inserts two metering zones in the open lane to make space for merging vehicles in the closed lane. In addition, each vehicle in the closed lane learns how to adjust its longitudinal position optimally to find a safe gap in the open lane using an off-policy soft actor critic reinforcement learning (RL) algorithm, considering its surrounding traffic conditions. The learning results are captured in convolutional neural networks and used to control individual vehicles in the testing phase. By adding the metering zones and taking the locations, speeds, and accelerations of surrounding vehicles into account, cooperation among vehicles is implicitly considered. This RL-based model is trained and evaluated using a microscopic traffic simulator. The results show that this cooperative RL-based merge control significantly outperforms popular strategies such as late merge and early merge in terms of both mobility and safety measures. It also performs better than a strategy assuming all vehicles are equipped with cooperative adaptive cruise control.more » « less
-
Critical infrastructure is the backbone of modern societies. To meet increasing demand under resource-constrained and multihazard conditions, policy-makers are tapping into infrastructure resiliency: its capacity to withstand and recover from disruptions. Thus, resiliency-aware uncertainty quantification is key to identify tipping points, yet it remains computationally inaccessible. This paper maps resiliency measures to well understood time-dependent reliability computations, porting insights and methods from reliability theory to the service of critical infrastructure resiliency and upkeep efforts. For large-scale applications, we use particle integration methods (PIMs)—a family of sequential Monte Carlo methods with wide-ranging applications—and propose their optimal tuning in terms of their variance and number of limit-state function evaluations. We obtain consistent and unbiased probability estimates in applications to dynamical systems, network reliability, and resilience analysis, demonstrating PIMs as practical yet under-appreciated tools. For example, we obtain probability estimates of order 10−14 in networks with over 10,000 random variables.more » « less
-
Vehicles are becoming more intelligent and automated. To achieve higher automation levels, vehicles are being equipped with more and more sensors. High data rate connectivity seems critical to allow vehicles and road infrastructure exchanging all these sensor data to enlarge their sensing range and make better safety related decisions. Connectivity also enables other applications such as infotainment or high levels of traffic coordination. Current solutions for vehicular communications though do not support the gigabit-per-second data rates. This presentation makes the case that millimeter wave communication is the only viable approach for high bandwidth connected vehicles. The motivation and challenges associated with using mmWave for vehicle-to-vehicle and vehicle-to-infrastructure applications are highlighted. Examples from recent work are provided including new theoretical results that enable mmWave communication in high mobility scenarios and innovative architectural concepts like position and radar-aided communication.more » « less
-
The latest developments in vehicle-to-infrastructure (V2I) and vehicle-to-anything (V2X) technologies enable all the entities in the transportation system to communicate and collaborate to optimize transportation safety, mobility, and equity at the system level. On the other hand, the community of researchers and developers is becoming aware of the critical role of roadway infrastructure in realizing automated driving. In particular, intelligent infrastructure systems, which leverage modern sensors, artificial intelligence, and communication capabilities, can provide critical information and control support to connected and/or automated vehicles to fulfill functions that are infeasible for automated vehicles alone due to technical or cost considerations. However, there is limited research on formulating and standardizing the intelligence levels of road infrastructure to facilitate the development, as the SAE automated driving levels have done for automated vehicles. This article proposes a five-level intelligence definition for intelligent roadway infrastructure, namely, connected and automated highway (CAH). The CAH is a subsystem of the more extensive collaborative automated driving system (CADS), along with the connected automated vehicle (CAV) subsystem. Leveraging the intelligence definition of CAH, the intelligence definition for the CADS is also defined. Examples of how the CAH at different levels operates with the CAV in the CADS are also introduced to demonstrate the dynamic allocation of various automated driving tasks between different entities in the CADS.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1007/978-3-030-43605-6_7
