2,759 research outputs found
Parameter estimation for stochastic hybrid model applied to urban traffic flow estimation
This study proposes a novel data-based approach for estimating the parameters of a stochastic hybrid model describing the traffic flow in an urban traffic network with signalized intersections. The model represents the evolution of the traffic flow rate, measuring the number of vehicles passing a given location per time unit. This traffic flow rate is described using a mode-dependent first-order autoregressive (AR) stochastic process. The parameters of the AR process take different values depending on the mode of traffic operation – free flowing, congested or faulty – making this a hybrid stochastic process. Mode switching occurs according to a first-order Markov chain. This study proposes an expectation-maximization (EM) technique for estimating the transition matrix of this Markovian mode process and the parameters of the AR models for each mode. The technique is applied to actual traffic flow data from the city of Jakarta, Indonesia. The model thus obtained is validated by using the smoothed inference algorithms and an online particle filter. The authors also develop an EM parameter estimation that, in combination with a time-window shift technique, can be useful and practical for periodically updating the parameters of hybrid model leading to an adaptive traffic flow state estimator
Human Motion Trajectory Prediction: A Survey
With growing numbers of intelligent autonomous systems in human environments,
the ability of such systems to perceive, understand and anticipate human
behavior becomes increasingly important. Specifically, predicting future
positions of dynamic agents and planning considering such predictions are key
tasks for self-driving vehicles, service robots and advanced surveillance
systems. This paper provides a survey of human motion trajectory prediction. We
review, analyze and structure a large selection of work from different
communities and propose a taxonomy that categorizes existing methods based on
the motion modeling approach and level of contextual information used. We
provide an overview of the existing datasets and performance metrics. We
discuss limitations of the state of the art and outline directions for further
research.Comment: Submitted to the International Journal of Robotics Research (IJRR),
37 page
aUToLights: A Robust Multi-Camera Traffic Light Detection and Tracking System
Following four successful years in the SAE AutoDrive Challenge Series I, the
University of Toronto is participating in the Series II competition to develop
a Level 4 autonomous passenger vehicle capable of handling various urban
driving scenarios by 2025. Accurate detection of traffic lights and correct
identification of their states is essential for safe autonomous operation in
cities. Herein, we describe our recently-redesigned traffic light perception
system for autonomous vehicles like the University of Toronto's self-driving
car, Artemis. Similar to most traffic light perception systems, we rely
primarily on camera-based object detectors. We deploy the YOLOv5 detector for
bounding box regression and traffic light classification across multiple
cameras and fuse the observations. To improve robustness, we incorporate priors
from high-definition semantic maps and perform state filtering using hidden
Markov models. We demonstrate a multi-camera, real time-capable traffic light
perception pipeline that handles complex situations including multiple visible
intersections, traffic light variations, temporary occlusion, and flashing
light states. To validate our system, we collected and annotated a varied
dataset incorporating flashing states and a range of occlusion types. Our
results show superior performance in challenging real-world scenarios compared
to single-frame, single-camera object detection.Comment: In Proceedings of the Conference on Robots and Vision (CRV'23),
Montreal, Canada, Jun. 6-8, 202
Quality-Aware Broadcasting Strategies for Position Estimation in VANETs
The dissemination of vehicle position data all over the network is a
fundamental task in Vehicular Ad Hoc Network (VANET) operations, as
applications often need to know the position of other vehicles over a large
area. In such cases, inter-vehicular communications should be exploited to
satisfy application requirements, although congestion control mechanisms are
required to minimize the packet collision probability. In this work, we face
the issue of achieving accurate vehicle position estimation and prediction in a
VANET scenario. State of the art solutions to the problem try to broadcast the
positioning information periodically, so that vehicles can ensure that the
information their neighbors have about them is never older than the
inter-transmission period. However, the rate of decay of the information is not
deterministic in complex urban scenarios: the movements and maneuvers of
vehicles can often be erratic and unpredictable, making old positioning
information inaccurate or downright misleading. To address this problem, we
propose to use the Quality of Information (QoI) as the decision factor for
broadcasting. We implement a threshold-based strategy to distribute position
information whenever the positioning error passes a reference value, thereby
shifting the objective of the network to limiting the actual positioning error
and guaranteeing quality across the VANET. The threshold-based strategy can
reduce the network load by avoiding the transmission of redundant messages, as
well as improving the overall positioning accuracy by more than 20% in
realistic urban scenarios.Comment: 8 pages, 7 figures, 2 tables, accepted for presentation at European
Wireless 201
Cleaning sky survey databases using Hough Transform and Renewal String approaches
Large astronomical databases obtained from sky surveys such as the
SuperCOSMOS Sky Survey (SSS) invariably suffer from spurious records coming
from artefactual effects of the telescope, satellites and junk objects in orbit
around earth and physical defects on the photographic plate or CCD. Though
relatively small in number these spurious records present a significant problem
in many situations where they can become a large proportion of the records
potentially of interest to a given astronomer. Accurate and robust techniques
are needed for locating and flagging such spurious objects, and we are
undertaking a programme investigating the use of machine learning techniques in
this context. In this paper we focus on the four most common causes of unwanted
records in the SSS: satellite or aeroplane tracks, scratches, fibres and other
linear phenomena introduced to the plate, circular halos around bright stars
due to internal reflections within the telescope and diffraction spikes near to
bright stars. Appropriate techniques are developed for the detection of each of
these. The methods are applied to the SSS data to develop a dataset of spurious
object detections, along with confidence measures, which can allow these
unwanted data to be removed from consideration. These methods are general and
can be adapted to other astronomical survey data.Comment: Accepted for MNRAS. 17 pages, latex2e, uses mn2e.bst, mn2e.cls,
md706.bbl, shortbold.sty (all included). All figures included here as low
resolution jpegs. A version of this paper including the figures can be
downloaded from http://www.anc.ed.ac.uk/~amos/publications.html and more
details on this project can be found at
http://www.anc.ed.ac.uk/~amos/sattrackres.htm
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