64,985 research outputs found
A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks
One of the key ideas to make Intelligent Transportation Systems (ITS) work
effectively is to deploy advanced communication and cooperative control
technologies among the vehicles and road infrastructures. In this spirit, we
propose a consensus-based distributed speed advisory system that optimally
determines a recommended common speed for a given area in order that the group
emissions, or group battery consumptions, are minimised. Our algorithms achieve
this in a privacy-aware manner; namely, individual vehicles do not reveal
in-vehicle information to other vehicles or to infrastructure. A mobility
simulator is used to illustrate the efficacy of the algorithm, and
hardware-in-the-loop tests involving a real vehicle are given to illustrate
user acceptability and ease of the deployment.Comment: This is a journal paper based on the conference paper "Highway speed
limits, optimised consensus, and intelligent speed advisory systems"
presented at the 3rd International Conference on Connected Vehicles and Expo
(ICCVE 2014) in November 2014. This is the revised version of the paper
recently submitted to the IEEE Transactions on Intelligent Transportation
Systems for publicatio
Time- and Frequency-Varying -Factor of Non-Stationary Vehicular Channels for Safety Relevant Scenarios
Vehicular communication channels are characterized by a non-stationary time-
and frequency-selective fading process due to fast changes in the environment.
We characterize the distribution of the envelope of the first delay bin in
vehicle-to-vehicle channels by means of its Rician -factor. We analyze the
time-frequency variability of this channel parameter using vehicular channel
measurements at 5.6 GHz with a bandwidth of 240 MHz for safety-relevant
scenarios in intelligent transportation systems (ITS). This data enables a
frequency-variability analysis from an IEEE 802.11p system point of view, which
uses 10 MHz channels. We show that the small-scale fading of the envelope of
the first delay bin is Ricean distributed with a varying -factor. The later
delay bins are Rayleigh distributed. We demonstrate that the -factor cannot
be assumed to be constant in time and frequency. The causes of these variations
are the frequency-varying antenna radiation patterns as well as the
time-varying number of active scatterers, and the effects of vegetation. We
also present a simple but accurate bi-modal Gaussian mixture model, that allows
to capture the -factor variability in time for safety-relevant ITS
scenarios.Comment: 26 pages, 12 figures, submitted to IEEE Transactions on Intelligent
Transportation Systems for possible publicatio
Improving Automated Driving through Planning with Human Internal States
This work examines the hypothesis that partially observable Markov decision
process (POMDP) planning with human driver internal states can significantly
improve both safety and efficiency in autonomous freeway driving. We evaluate
this hypothesis in a simulated scenario where an autonomous car must safely
perform three lane changes in rapid succession. Approximate POMDP solutions are
obtained through the partially observable Monte Carlo planning with observation
widening (POMCPOW) algorithm. This approach outperforms over-confident and
conservative MDP baselines and matches or outperforms QMDP. Relative to the MDP
baselines, POMCPOW typically cuts the rate of unsafe situations in half or
increases the success rate by 50%.Comment: Preprint before submission to IEEE Transactions on Intelligent
Transportation Systems. arXiv admin note: text overlap with arXiv:1702.0085
Relational Fusion Networks: Graph Convolutional Networks for Road Networks
The application of machine learning techniques in the setting of road
networks holds the potential to facilitate many important intelligent
transportation applications. Graph Convolutional Networks (GCNs) are neural
networks that are capable of leveraging the structure of a network. However,
many implicit assumptions of GCNs do not apply to road networks. We introduce
the Relational Fusion Network (RFN), a novel type of GCN designed specifically
for road networks. In particular, we propose methods that outperform
state-of-the-art GCNs by 21%-40% on two machine learning tasks in road
networks. Furthermore, we show that state-of-the-art GCNs may fail to
effectively leverage road network structure and may not generalize well to
other road networks.Comment: IEEE Transactions on Intelligent Transportation Systems (2020). arXiv
admin note: substantial text overlap with arXiv:1908.1156
Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning
Heterogeneous trajectory forecasting is critical for intelligent
transportation systems, while it is challenging because of the difficulty for
modeling the complex interaction relations among the heterogeneous road agents
as well as their agent-environment constraint. In this work, we propose a risk
and scene graph learning method for trajectory forecasting of heterogeneous
road agents, which consists of a Heterogeneous Risk Graph (HRG) and a
Hierarchical Scene Graph (HSG) from the aspects of agent category and their
movable semantic regions. HRG groups each kind of road agents and calculates
their interaction adjacency matrix based on an effective collision risk metric.
HSG of driving scene is modeled by inferring the relationship between road
agents and road semantic layout aligned by the road scene grammar. Based on
this formulation, we can obtain an effective trajectory forecasting in driving
situations, and superior performance to other state-of-the-art approaches is
demonstrated by exhaustive experiments on the nuScenes, ApolloScape, and
Argoverse datasets.Comment: Submitted to IEEE Transactions on Intelligent Transportation Systems,
202
Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation
Traffic speed data imputation is a fundamental challenge for data-driven
transport analysis. In recent years, with the ubiquity of GPS-enabled devices
and the widespread use of crowdsourcing alternatives for the collection of
traffic data, transportation professionals increasingly look to such
user-generated data for many analysis, planning, and decision support
applications. However, due to the mechanics of the data collection process,
crowdsourced traffic data such as probe-vehicle data is highly prone to missing
observations, making accurate imputation crucial for the success of any
application that makes use of that type of data. In this article, we propose
the use of multi-output Gaussian processes (GPs) to model the complex spatial
and temporal patterns in crowdsourced traffic data. While the Bayesian
nonparametric formalism of GPs allows us to model observation uncertainty, the
multi-output extension based on convolution processes effectively enables us to
capture complex spatial dependencies between nearby road segments. Using 6
months of crowdsourced traffic speed data or "probe vehicle data" for several
locations in Copenhagen, the proposed approach is empirically shown to
significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems,
201
SECMACE: Scalable and Robust Identity and Credential Management Infrastructure in Vehicular Communication Systems
Several years of academic and industrial research efforts have converged to a
common understanding on fundamental security building blocks for the upcoming
Vehicular Communication (VC) systems. There is a growing consensus towards
deploying a special-purpose identity and credential management infrastructure,
i.e., a Vehicular Public-Key Infrastructure (VPKI), enabling pseudonymous
authentication, with standardization efforts towards that direction. In spite
of the progress made by standardization bodies (IEEE 1609.2 and ETSI) and
harmonization efforts (Car2Car Communication Consortium (C2C-CC)), significant
questions remain unanswered towards deploying a VPKI. Deep understanding of the
VPKI, a central building block of secure and privacy-preserving VC systems, is
still lacking. This paper contributes to the closing of this gap. We present
SECMACE, a VPKI system, which is compatible with the IEEE 1609.2 and ETSI
standards specifications. We provide a detailed description of our
state-of-the-art VPKI that improves upon existing proposals in terms of
security and privacy protection, and efficiency. SECMACE facilitates
multi-domain operations in the VC systems and enhances user privacy, notably
preventing linking pseudonyms based on timing information and offering
increased protection even against honest-but-curious VPKI entities. We propose
multiple policies for the vehicle-VPKI interactions, based on which and two
large-scale mobility trace datasets, we evaluate the full-blown implementation
of SECMACE. With very little attention on the VPKI performance thus far, our
results reveal that modest computing resources can support a large area of
vehicles with very low delays and the most promising policy in terms of privacy
protection can be supported with moderate overhead.Comment: 14 pages, 9 figures, 10 tables, IEEE Transactions on Intelligent
Transportation System
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