Constrained unscented Kalman filter based fusion of GPS/INS/digital map for vehicle localization

Li, W.; Leung, Henry

doi:10.1109/itsc.2003.1252706

Cited by 40 publications

(29 citation statements)

References 16 publications

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“…In addition to these hardware technologies other software technologies can be used to improve localisation estimates. These include map matching software, which constrains the vehicle's position to the road network [16] and Bayesian recursive filtering techniques, such as the Kalman filter [17]. The latter allow data from more than one sensor and data from other sources such as dynamic data and vehicle control data to be fused to provide a probabilistic estimate of position.…”

Section: Sources Of Localisation Datamentioning

confidence: 99%

Quantifying the impact of probe vehicle localisation data errors on signalised junction control

Waterson

Box

2012

IET Intell. Transp. Syst.

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This study investigates theoretical signal control algorithms based solely on probe vehicle data. Through development of a simulation system that can model urban signalised junction control using localisation probe data from all vehicles in the local area, improvements in junction operational efficiency that result from the improved input data are demonstrated for both isolated and coordinated junctions. Results from the isolated junction scenario show that the richness of the information contained within probe vehicle data means that control algorithms based just on positions and velocities of vehicles can produce 25% reductions in average delay compared to the current standard control algorithm MOVA. Results from the twin junction scenario confirm the importance of using high-level synchronisation to coordinate closely connected junctions, achieving reductions in average delays (compared to independent control approaches) of up to 40% through a process of weighting the probe vehicle data to reflect prior stage decisions of other parts of the junction. Critical to achieving these benefits, however, is the availability of high localisation accuracy probe data, with results indicating that the levels of accuracy necessary are representative of the typical performance of current in-vehicle global positioning system units, except when those vehicles are operating in urban canyon environments.

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Section: Sources Of Localisation Datamentioning

confidence: 99%

Quantifying the impact of probe vehicle localisation data errors on signalised junction control

Waterson

Box

2012

IET Intell. Transp. Syst.

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“…It is a way to avoid limitations of GPS and depend on on-board sensors as Inertial Navigation Systems (INS) or radio-altimeters. Analogously, in [75], the DGPS and INS data are fused considering also map geometry stored in a digital map database.…”

Section: Context In Filteringmentioning

confidence: 99%

“…UKF also allows non-linear processes with a more efficient transformation. In order to exploit context with PF or UKF methods, hard constraints externally known are naturally integrated on the state vector or the measurement process during the estimation process [75,89]. For instance, in [75], a constrained unscented Kalman filter is used in GPS/INS fusion integrating state constraints from the surface geometry.…”

Section: Context In Filteringmentioning

confidence: 99%

Context-based Information Fusion: A survey and discussion

2015

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This survey aims to provide a comprehensive status of recent and current research on context-based Information Fusion (IF) systems, tracing back the roots of the original thinking behind the development of the concept of ''context''. It shows how its fortune in the distributed computing world eventually per-meated in the world of IF, discussing the current strategies and techniques, and hinting possible future trends. IF processes can represent context at different levels (structural and physical constraints of the scenario, a priori known operational rules between entities and environment, dynamic relationships modelled to interpret the system output, etc.). In addition to the survey, several novel context exploita-tion dynamics and architectural aspects peculiar to the fusion domain are presented and discussed.

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“…In addition to these hardware technologies other software technologies can be employed to improve localization estimates. These include map matching software, which constrains the vehicle's position to the road network [9] and Bayesian recursive filtering techniques, such as the Kalman filter [3]. The latter allow data from more than one sensor and data from other sources such as dynamic data and vehicle control data to be fused to provide a probabilistic estimate of position.…”

Section: Localization Data Modulementioning

confidence: 99%

Comparison of signalized junction control strategies that use localization probe data

Waterson¹

2010

IET Road Transport Information and Control Conference and the ITS United Kingdom Members' Conference (RTIC 2010). Better Transp

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This paper presents a simulation test bed and a methodology for evaluating urban signalized junction control algorithms that use localization probe data from all vehicles in the local area. The simulator is based on SIAS Paramics microsimulation software with bespoke software modules built on top for automatic network generation, localization data processing and signal control. Also presented are results from tests carried out using the simulation test bed to evaluate localization strategies. The tested strategies use a hierarchical structure of auctioning agents. Results from tests on an isolated signalized junction indicate that the performance of the auctioning agent algorithms compare favourably with the MOVA algorithm using inductive loop data. Results are also presented for tests on a twin junction where strategies are synchronized. These show a significant improvement in performance through synchronization.

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Constrained unscented Kalman filter based fusion of GPS/INS/digital map for vehicle localization

Cited by 40 publications

References 16 publications

Quantifying the impact of probe vehicle localisation data errors on signalised junction control

Quantifying the impact of probe vehicle localisation data errors on signalised junction control

Context-based Information Fusion: A survey and discussion

Comparison of signalized junction control strategies that use localization probe data

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