1,342 research outputs found
Improving performance of pedestrian positioning by using vehicular communication signals
Pedestrian-to-vehicle communications, where pedestrian devices transmit their position information to nearby vehicles to indicate their presence, help to reduce pedestrian accidents. Satellite-based systems are widely used for pedestrian positioning, but have much degraded performance in urban canyon, where satellite signals are often obstructed by roadside buildings. In this paper, we propose a pedestrian positioning method, which leverages vehicular communication signals and uses vehicles as anchors. The performance of pedestrian positioning is improved from three aspects: (i) Channel state information instead of RSSI is used to estimate pedestrian-vehicle distance with higher precision. (ii) Only signals with line-of-sight path are used, and the property of distance error is considered. (iii) Fast mobility of vehicles is used to get diverse measurements, and Kalman filter is applied to smooth positioning results. Extensive evaluations, via trace-based simulation, confirm that (i) Fixing rate of positions can be much improved. (ii) Horizontal positioning error can be greatly reduced, nearly by one order compared with off-the-shelf receivers, by almost half compared with RSSI-based method, and can be reduced further to about 80cm when vehicle transmission period is 100ms and Kalman filter is applied. Generally, positioning performance increases with the number of available vehicles and their transmission frequency
Unmanned Aircraft System Navigation in the Urban Environment: A Systems Analysis
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140665/1/1.I010280.pd
Detection of variance changes and mean value jumps in measurement noise for multipath mitigation in urban navigation
This paper studies an urban navigation filter for land vehicles. Typical urban-canyon phenomena as multipath and GPS outages seriously degrade positioning performance. To deal with these scenarios a hybrid navigation system using GPS and dead-reckoning sensors is presented. This navigation system is complemented by a two-step detection procedure that classifies outliers according to their associated source of error. Two different situations will be considered in the presence of multipath. These situations correspond to the presence or absence of line of sight for the different GPS satellites. Therefore, two kinds of errors are potentially “corrupting” the pseudo-ranges, modeled as variance changes or mean value jumps in noise measurements. An original multiple model approach is proposed to detect, identify and correct these errors and provide a final consistent solution
Towards Full Automated Drive in Urban Environments: A Demonstration in GoMentum Station, California
Each year, millions of motor vehicle traffic accidents all over the world
cause a large number of fatalities, injuries and significant material loss.
Automated Driving (AD) has potential to drastically reduce such accidents. In
this work, we focus on the technical challenges that arise from AD in urban
environments. We present the overall architecture of an AD system and describe
in detail the perception and planning modules. The AD system, built on a
modified Acura RLX, was demonstrated in a course in GoMentum Station in
California. We demonstrated autonomous handling of 4 scenarios: traffic lights,
cross-traffic at intersections, construction zones and pedestrians. The AD
vehicle displayed safe behavior and performed consistently in repeated
demonstrations with slight variations in conditions. Overall, we completed 44
runs, encompassing 110km of automated driving with only 3 cases where the
driver intervened the control of the vehicle, mostly due to error in GPS
positioning. Our demonstration showed that robust and consistent behavior in
urban scenarios is possible, yet more investigation is necessary for full scale
roll-out on public roads.Comment: Accepted to Intelligent Vehicles Conference (IV 2017
Implicit Cooperative Positioning in Vehicular Networks
Absolute positioning of vehicles is based on Global Navigation Satellite
Systems (GNSS) combined with on-board sensors and high-resolution maps. In
Cooperative Intelligent Transportation Systems (C-ITS), the positioning
performance can be augmented by means of vehicular networks that enable
vehicles to share location-related information. This paper presents an Implicit
Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle
(V2V) connectivity in an innovative manner, avoiding the use of explicit V2V
measurements such as ranging. In the ICP approach, vehicles jointly localize
non-cooperative physical features (such as people, traffic lights or inactive
cars) in the surrounding areas, and use them as common noisy reference points
to refine their location estimates. Information on sensed features are fused
through V2V links by a consensus procedure, nested within a message passing
algorithm, to enhance the vehicle localization accuracy. As positioning does
not rely on explicit ranging information between vehicles, the proposed ICP
method is amenable to implementation with off-the-shelf vehicular communication
hardware. The localization algorithm is validated in different traffic
scenarios, including a crossroad area with heterogeneous conditions in terms of
feature density and V2V connectivity, as well as a real urban area by using
Simulation of Urban MObility (SUMO) for traffic data generation. Performance
results show that the proposed ICP method can significantly improve the vehicle
location accuracy compared to the stand-alone GNSS, especially in harsh
environments, such as in urban canyons, where the GNSS signal is highly
degraded or denied.Comment: 15 pages, 10 figures, in review, 201
Beam Alignment for Millimetre Wave Links with Motion Prediction of Autonomous Vehicles
Intelligent Transportation Systems (ITSs) require ultra-low end-to-end delays
and multi-gigabit-per-second data transmission. Millimetre Waves (mmWaves)
communications can fulfil these requirements. However, the increased mobility
of Connected and Autonomous Vehicles (CAVs), requires frequent beamforming -
thus introducing increased overhead. In this paper, a new beamforming algorithm
is proposed able to achieve overhead-free beamforming training. Leveraging from
the CAVs sensory data, broadcast with Dedicated Short Range Communications
(DSRC) beacons, the position and the motion of a CAV can be estimated and
beamform accordingly. To minimise the position errors, an analysis of the
distinct error components was presented. The network performance is further
enhanced by adapting the antenna beamwidth with respect to the position error.
Our algorithm outperforms the legacy IEEE 802.11ad approach proving it a viable
solution for the future ITS applications and services.Comment: Proc. of IET Colloquium on Antennas, Propagation & RF Technology for
Transport and Autonomous Platforms, to appea
Satellite Navigation for the Age of Autonomy
Global Navigation Satellite Systems (GNSS) brought navigation to the masses.
Coupled with smartphones, the blue dot in the palm of our hands has forever
changed the way we interact with the world. Looking forward, cyber-physical
systems such as self-driving cars and aerial mobility are pushing the limits of
what localization technologies including GNSS can provide. This autonomous
revolution requires a solution that supports safety-critical operation,
centimeter positioning, and cyber-security for millions of users. To meet these
demands, we propose a navigation service from Low Earth Orbiting (LEO)
satellites which deliver precision in-part through faster motion, higher power
signals for added robustness to interference, constellation autonomous
integrity monitoring for integrity, and encryption / authentication for
resistance to spoofing attacks. This paradigm is enabled by the 'New Space'
movement, where highly capable satellites and components are now built on
assembly lines and launch costs have decreased by more than tenfold. Such a
ubiquitous positioning service enables a consistent and secure standard where
trustworthy information can be validated and shared, extending the electronic
horizon from sensor line of sight to an entire city. This enables the
situational awareness needed for true safe operation to support autonomy at
scale.Comment: 11 pages, 8 figures, 2020 IEEE/ION Position, Location and Navigation
Symposium (PLANS
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