15,191 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
2nd Symposium on Management of Future motorway and urban Traffic Systems (MFTS 2018): Booklet of abstracts: Ispra, 11-12 June 2018
The Symposium focuses on future traffic management systems, covering the subjects of traffic control, estimation, and modelling of motorway and urban networks, with particular emphasis on the presence of advanced vehicle communication and automation technologies.
As connectivity and automation are being progressively introduced in our transport and mobility systems, there is indeed a growing need to understand the implications and opportunities for an enhanced traffic management as well as to identify innovative ways and tools to optimise traffic efficiency.
In particular the debate on centralised versus decentralised traffic management in the presence of connected and automated vehicles has started attracting the attention of the research community.
In this context, the Symposium provides a remarkable opportunity to share novel ideas and discuss future research directions.JRC.C.4-Sustainable Transpor
Verification and Validation for Flight-Critical Systems (VVFCS)
On March 31, 2009 a Request for Information (RFI) was issued by NASA s Aviation Safety Program to gather input on the subject of Verification and Validation (V & V) of Flight-Critical Systems. The responses were provided to NASA on or before April 24, 2009. The RFI asked for comments in three topic areas: Modeling and Validation of New Concepts for Vehicles and Operations; Verification of Complex Integrated and Distributed Systems; and Software Safety Assurance. There were a total of 34 responses to the RFI, representing a cross-section of academic (26%), small & large industry (47%) and government agency (27%)
The Rockstar Phase-Space Temporal Halo Finder and the Velocity Offsets of Cluster Cores
We present a new algorithm for identifying dark matter halos, substructure,
and tidal features. The approach is based on adaptive hierarchical refinement
of friends-of-friends groups in six phase-space dimensions and one time
dimension, which allows for robust (grid-independent, shape-independent, and
noise-resilient) tracking of substructure; as such, it is named Rockstar
(Robust Overdensity Calculation using K-Space Topologically Adaptive
Refinement). Our method is massively parallel (up to 10^5 CPUs) and runs on the
largest current simulations (>10^10 particles) with high efficiency (10 CPU
hours and 60 gigabytes of memory required per billion particles analyzed). A
previous paper (Knebe et al 2011) has shown Rockstar to have class-leading
recovery of halo properties; we expand on these comparisons with more tests and
higher-resolution simulations. We show a significant improvement in
substructure recovery as compared to several other halo finders and discuss the
theoretical and practical limits of simulations in this regard. Finally, we
present results which demonstrate conclusively that dark matter halo cores are
not at rest relative to the halo bulk or satellite average velocities and have
coherent velocity offsets across a wide range of halo masses and redshifts. For
massive clusters, these offsets can be up to 350 km/s at z=0 and even higher at
high redshifts. Our implementation is publicly available at
http://code.google.com/p/rockstar .Comment: 20 pages, 14 figures. Minor revisions to match accepted versio
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