14,703 research outputs found
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
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
SymbioCity: Smart Cities for Smarter Networks
The "Smart City" (SC) concept revolves around the idea of embodying
cutting-edge ICT solutions in the very fabric of future cities, in order to
offer new and better services to citizens while lowering the city management
costs, both in monetary, social, and environmental terms. In this framework,
communication technologies are perceived as subservient to the SC services,
providing the means to collect and process the data needed to make the services
function. In this paper, we propose a new vision in which technology and SC
services are designed to take advantage of each other in a symbiotic manner.
According to this new paradigm, which we call "SymbioCity", SC services can
indeed be exploited to improve the performance of the same communication
systems that provide them with data. Suggestive examples of this symbiotic
ecosystem are discussed in the paper. The dissertation is then substantiated in
a proof-of-concept case study, where we show how the traffic monitoring service
provided by the London Smart City initiative can be used to predict the density
of users in a certain zone and optimize the cellular service in that area.Comment: 14 pages, submitted for publication to ETT Transactions on Emerging
Telecommunications Technologie
Development and Performance Evaluation of a Connected Vehicle Application Development Platform (CVDeP)
Connected vehicle (CV) application developers need a development platform to build,
test and debug real-world CV applications, such as safety, mobility, and environmental
applications, in edge-centric cyber-physical systems. Our study objective is to develop
and evaluate a scalable and secure CV application development platform (CVDeP)
that enables application developers to build, test and debug CV applications in realtime.
CVDeP ensures that the functional requirements of the CV applications meet the
corresponding requirements imposed by the specific applications. We evaluated the
efficacy of CVDeP using two CV applications (one safety and one mobility application)
and validated them through a field experiment at the Clemson University Connected
Vehicle Testbed (CU-CVT). Analyses prove the efficacy of CVDeP, which satisfies the
functional requirements (i.e., latency and throughput) of a CV application while
maintaining scalability and security of the platform and applications
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