3,972 research outputs found
Analyzing Attacks on Cooperative Adaptive Cruise Control (CACC)
Cooperative Adaptive Cruise Control (CACC) is one of the driving applications
of vehicular ad-hoc networks (VANETs) and promises to bring more efficient and
faster transportation through cooperative behavior between vehicles. In CACC,
vehicles exchange information, which is relied on to partially automate
driving; however, this reliance on cooperation requires resilience against
attacks and other forms of misbehavior. In this paper, we propose a rigorous
attacker model and an evaluation framework for this resilience by quantifying
the attack impact, providing the necessary tools to compare controller
resilience and attack effectiveness simultaneously. Although there are
significant differences between the resilience of the three analyzed
controllers, we show that each can be attacked effectively and easily through
either jamming or data injection. Our results suggest a combination of
misbehavior detection and resilient control algorithms with graceful
degradation are necessary ingredients for secure and safe platoons.Comment: 8 pages (author version), 5 Figures, Accepted at 2017 IEEE Vehicular
Networking Conference (VNC
A comprehensive survey on cooperative intersection management for heterogeneous connected vehicles
Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy
Federated Robust Embedded Systems: Concepts and Challenges
The development within the area of embedded systems (ESs) is moving rapidly, not least due to falling costs of computation and communication equipment. It is believed that increased communication opportunities will lead to the future ESs no longer being parts of isolated products, but rather parts of larger communities or federations of ESs, within which information is exchanged for the benefit of all participants. This vision is asserted by a number of interrelated research topics, such as the internet of things, cyber-physical systems, systems of systems, and multi-agent systems. In this work, the focus is primarily on ESs, with their specific real-time and safety requirements.
While the vision of interconnected ESs is quite promising, it also brings great challenges to the development of future systems in an efficient, safe, and reliable way. In this work, a pre-study has been carried out in order to gain a better understanding about common concepts and challenges that naturally arise in federations of ESs. The work was organized around a series of workshops, with contributions from both academic participants and industrial partners with a strong experience in ES development.
During the workshops, a portfolio of possible ES federation scenarios was collected, and a number of application examples were discussed more thoroughly on different abstraction levels, starting from screening the nature of interactions on the federation level and proceeding down to the implementation details within each ES. These discussions led to a better understanding of what can be expected in the future federated ESs. In this report, the discussed applications are summarized, together with their characteristics, challenges, and necessary solution elements, providing a ground for the future research within the area of communicating ESs
Communication-Efficient Decentralized Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control
Connected and autonomous vehicles (CAVs) promise next-gen transportation
systems with enhanced safety, energy efficiency, and sustainability. One
typical control strategy for CAVs is the so-called cooperative adaptive cruise
control (CACC) where vehicles drive in platoons and cooperate to achieve safe
and efficient transportation. In this study, we formulate CACC as a multi-agent
reinforcement learning (MARL) problem. Diverging from existing MARL methods
that use centralized training and decentralized execution which require not
only a centralized communication mechanism but also dense inter-agent
communication, we propose a fully-decentralized MARL framework for enhanced
efficiency and scalability. In addition, a quantization-based communication
scheme is proposed to reduce the communication overhead without significantly
degrading the control performance. This is achieved by employing randomized
rounding numbers to quantize each piece of communicated information and only
communicating non-zero components after quantization. Extensive experimentation
in two distinct CACC settings reveals that the proposed MARL framework
consistently achieves superior performance over several contemporary benchmarks
in terms of both communication efficiency and control efficacy.Comment: 11 pages, 7 figure
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