1,003 research outputs found
Diversity Maximized Scheduling in RoadSide Units for Traffic Monitoring Applications
This paper develops an optimal data aggregation policy for learning-based
traffic control systems based on imagery collected from Road Side Units (RSUs)
under imperfect communications. Our focus is optimizing semantic information
flow from RSUs to a nearby edge server or cloud-based processing units by
maximizing data diversity based on the target machine learning application
while taking into account heterogeneous channel conditions (e.g., delay, error
rate) and constrained total transmission rate. As a proof-of-concept, we
enforce fairness among class labels to increase data diversity for
classification problems. The developed constrained optimization problem is
non-convex. Hence it does not admit a closed-form solution, and the exhaustive
search is NP-hard in the number of RSUs. To this end, we propose an approximate
algorithm that applies a greedy interval-by-interval scheduling policy by
selecting RSUs to transmit. We use coalition game formulation to maximize the
overall added fairness by the selected RSUs in each transmission interval.
Once, RSUs are selected, we employ a maximum uncertainty method to handpick
data samples that contribute the most to the learning performance. Our method
outperforms random selection, uniform selection, and pure network-based
optimization methods (e.g., FedCS) in terms of the ultimate accuracy of the
target learning application
Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network
Accurate lane localization and lane change detection are crucial in advanced
driver assistance systems and autonomous driving systems for safer and more
efficient trajectory planning. Conventional localization devices such as Global
Positioning System only provide road-level resolution for car navigation, which
is incompetent to assist in lane-level decision making. The state of art
technique for lane localization is to use Light Detection and Ranging sensors
to correct the global localization error and achieve centimeter-level accuracy,
but the real-time implementation and popularization for LiDAR is still limited
by its computational burden and current cost. As a cost-effective alternative,
vision-based lane change detection has been highly regarded for affordable
autonomous vehicles to support lane-level localization. A deep learning-based
computer vision system is developed to detect the lane change behavior using
the images captured by a front-view camera mounted on the vehicle and data from
the inertial measurement unit for highway driving. Testing results on
real-world driving data have shown that the proposed method is robust with
real-time working ability and could achieve around 87% lane change detection
accuracy. Compared to the average human reaction to visual stimuli, the
proposed computer vision system works 9 times faster, which makes it capable of
helping make life-saving decisions in time
The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions
Ramp metering, a traditional traffic control strategy for conventional
vehicles, has been widely deployed around the world since the 1960s. On the
other hand, the last decade has witnessed significant advances in connected and
automated vehicle (CAV) technology and its great potential for improving
safety, mobility and environmental sustainability. Therefore, a large amount of
research has been conducted on cooperative ramp merging for CAVs only. However,
it is expected that the phase of mixed traffic, namely the coexistence of both
human-driven vehicles and CAVs, would last for a long time. Since there is
little research on the system-wide ramp control with mixed traffic conditions,
the paper aims to close this gap by proposing an innovative system architecture
and reviewing the state-of-the-art studies on the key components of the
proposed system. These components include traffic state estimation, ramp
metering, driving behavior modeling, and coordination of CAVs. All reviewed
literature plot an extensive landscape for the proposed system-wide coordinated
ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE
- ITSC 201
Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics
Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version
Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles
By offloading computation-intensive tasks of vehicles to roadside units
(RSUs), mobile edge computing (MEC) in the Internet of Vehicles (IoV) can
relieve the onboard computation burden. However, existing model-based task
offloading methods suffer from heavy computational complexity with the increase
of vehicles and data-driven methods lack interpretability. To address these
challenges, in this paper, we propose a knowledge-driven multi-agent
reinforcement learning (KMARL) approach to reduce the latency of task
offloading in cybertwin-enabled IoV. Specifically, in the considered scenario,
the cybertwin serves as a communication agent for each vehicle to exchange
information and make offloading decisions in the virtual space. To reduce the
latency of task offloading, a KMARL approach is proposed to select the optimal
offloading option for each vehicle, where graph neural networks are employed by
leveraging domain knowledge concerning graph-structure communication topology
and permutation invariance into neural networks. Numerical results show that
our proposed KMARL yields higher rewards and demonstrates improved scalability
compared with other methods, benefitting from the integration of domain
knowledge
Mobile Edge Computing
This is an open access book. It offers comprehensive, self-contained knowledge on Mobile Edge Computing (MEC), which is a very promising technology for achieving intelligence in the next-generation wireless communications and computing networks. The book starts with the basic concepts, key techniques and network architectures of MEC. Then, we present the wide applications of MEC, including edge caching, 6G networks, Internet of Vehicles, and UAVs. In the last part, we present new opportunities when MEC meets blockchain, Artificial Intelligence, and distributed machine learning (e.g., federated learning). We also identify the emerging applications of MEC in pandemic, industrial Internet of Things and disaster management. The book allows an easy cross-reference owing to the broad coverage on both the principle and applications of MEC. The book is written for people interested in communications and computer networks at all levels. The primary audience includes senior undergraduates, postgraduates, educators, scientists, researchers, developers, engineers, innovators and research strategists
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