Joint Vehicle Trajectory and Model Parameter Estimation Using Road Side Sensors

Hostettler, Roland; Birk, Wolfgang; Nordenvaad, Magnus Lundberg

doi:10.1109/jsen.2015.2432748

Cited by 11 publications

(5 citation statements)

References 33 publications

(52 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some research exists for vehicle trajectory estimation again mostly based on video cameras [24], [25], combining inside vehicle sensor data [26], or based on statistical methods [27]. Authors at [28] passing vehicles. A hypothesis exists that it is possible to perform re-identification for specific passing vehicles based only on a magnetic signature.…”

Section: Related Workmentioning

confidence: 99%

Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array

et al. 2023

View full text Add to dashboard Cite

The increasing presence of vehicles on roads necessitates intelligent traffic management solutions in areas where video cameras cannot be utilized. Currently, there are limited choices for depersonalized vehicle reidentification systems. This paper introduces a system that later will be used for vehicle reidentification. The system uses anisotropic magnetoresistive sensors and is based on the hypothesis that each vehicle leaves unique magnetic signatures which can be used for comparison and matching. Vehicle location on the road perpendicular to sensor array detection methodology is presented in this work. An array of magnetic sensors is installed in asphalt across the vehicle's driving direction. The system continuously measures Earth's natural magnetic field and detects distortions when vehicles pass an array of a sensors and then logs magnetic signatures. Useful parameters from raw sensor axes are calculated -modules and derivatives. Applying signal-to-noise ratio calculation for module derivatives between ambient noise and signal gives important features for neural network input. Different types of neural network architectures and output result interpretation techniques are investigated. Further, after evaluating network output it is possible to label sensor nodes that are directly beneath the vehicle. Experiment results show that implemented algorithm is highly sufficient for valid sensors under the vehicle selection. Correct sensor selection is important for further re-identification algorithms.INDEX TERMS Magnetic field measurement, magnetic signature, vehicle re-identification, intelligent transportation systems.

show abstract

Section: Related Workmentioning

confidence: 99%

Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The authors of [183] formulated a multi-phase optimal control problem to simultaneously optimize the reference speed and steering angle within the detection range. Except for vehicle sensors, smartphones and roadside sensors also bring broader prospects of application [40,266,267]. These developments have brought new opportunities for the development of autonomous driving/assisted driving systems.…”

Section: Autonomous Driving/driver-assistance Systemmentioning

confidence: 99%

Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey

Cui

et al. 2022

Sci. China Inf. Sci.

View full text Add to dashboard Cite

With the growing popularity of the Internet-of-Vehicles (IoV), it is of pressing necessity to understand transportation traffic patterns and their impact on wireless network designs and operations. Vehicular mobility patterns and traffic models are the keys to assisting a wide range of analyses and simulations in these applications. This study surveys the status quo of vehicular mobility models, with a focus on recent advances in the last decade. To provide a comprehensive and systematic review, the study first puts forth a requirement-model-application framework in the IoV or general communication and transportation networks. Existing vehicular mobility models are categorized into vehicular distribution, vehicular traffic, and driving behavior models. Such categorization has a particular emphasis on the random patterns of vehicles in space, traffic flow models aligned to road maps, and individuals’ driving behaviors (e.g., lane-changing and car-following). The different categories of the models are applied to various application scenarios, including underlying network connectivity analysis, off-line network optimization, online network functionality, and real-time autonomous driving. Finally, several important research opportunities arise and deserve continuing research efforts, such as holistic designs of deep learning platforms which take the model parameters of vehicular mobility as input features, qualification of vehicular mobility models in terms of representativeness and completeness, and new hybrid models incorporating different categories of vehicular mobility models to improve the representativeness and completeness.

show abstract

“…, which yields an analytically tractable substructure. The first model, Model 1, is a commonly encountered (see, e.g., [27]- [29]) mixed linear/nonlinear Gaussian state-space model defined as follows.…”

Section: Problem Formulationmentioning

confidence: 99%

Rao–Blackwellized Gaussian Smoothing

Hostettler

Särkkä

2019

IEEE Trans. Automat. Contr.

Self Cite

View full text Add to dashboard Cite

In this paper, we consider Rao-Blackwellization of linear substructures in sigma-point-based Gaussian assumed density smoothers. We derive marginalized prediction, smoothing, and update steps for the mixed linear/nonlinear Gaussian state-space model as well as for a hierarchical model for both conventional and iterated posterior linearization Gaussian smoothers. The proposed method is evaluated in a numerical example and it is shown that the computational complexity is reduced considerably compared to non-Rao-Blackwellized Gaussian smoothers for systems with high-dimensional linear subspaces.

show abstract

Joint Vehicle Trajectory and Model Parameter Estimation Using Road Side Sensors

Cited by 11 publications

References 33 publications

Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array

Passing Vehicle Road Occupancy Detection Using the Magnetic Sensor Array

Vehicular mobility patterns and their applications to Internet-of-Vehicles: a comprehensive survey

Rao–Blackwellized Gaussian Smoothing

Contact Info

Product

Resources

About