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Journal ArticleDOI

On the assessment of vehicle trajectory data accuracy and application to the Next Generation SIMulation (NGSIM) program data

TLDR
This paper intends to design quantitative methods to inspect trajectory data, involving jerk analysis, consistency analysis and spectral analysis, and is applied to the complete set of NGSIM databases.
Abstract
Trajectories drawn in a common reference system by all the vehicles on a road are the ultimate empirical data to investigate traffic dynamics. The vast amount of such data made freely available by the Next Generation SIMulation (NGSIM) program is therefore opening up new horizons in studying traffic flow theory. Yet the quality of trajectory data and its impact on the reliability of related studies was a vastly underestimated problem in the traffic literature even before the availability of NGSIM data. The absence of established methods to assess data accuracy and even of a common understanding of the problem makes it hard to speak of reproducibility of experiments and objective comparison of results, in particular in a research field where the complexity of human behaviour is an intrinsic challenge to the scientific method. Therefore this paper intends to design quantitative methods to inspect trajectory data. To this aim first the structure of the error on point measurements and its propagation on the space travelled are investigated. Analytical evidence of the bias propagated in the vehicle trajectory functions and a related consistency requirement are given. Literature on estimation/filtering techniques is then reviewed in light of this requirement and a number of error statistics suitable to inspect trajectory data are proposed. The designed methodology, involving jerk analysis, consistency analysis and spectral analysis, is then applied to the complete set of NGSIM databases.

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Citations
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Proceedings ArticleDOI

CommonRoad: Composable benchmarks for motion planning on roads

TL;DR: CommonRoad as discussed by the authors is a composable benchmark for motion planning on roads, which is composed by a vehicle model, a cost function, and a scenario (including goals and constraints).
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Analysis of Recurrent Neural Networks for Probabilistic Modeling of Driver Behavior

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Book

Millimeter Wave Vehicular Communications: A Survey

TL;DR: An overview of mmWave vehicular communication with an emphasis on results on channel measurements, the physical PHY layer, and the medium access control MAC layer is provided, with special attention paid to identifying important topics of future research.
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Trajectory data reconstruction and simulation-based validation against macroscopic traffic patterns

TL;DR: This paper shows that the behavior of driver models, either individually or entangled in stochastic traffic simulation, is affected by the accuracy of empirical vehicle trajectories, and a “traffic-informed” methodology is proposed to restore physical and platoon integrity of trajectories in a finite time–space domain.
Journal ArticleDOI

A critical evaluation of the Next Generation Simulation (NGSIM) vehicle trajectory dataset

TL;DR: This paper shows that the NGSIM errors are beyond anything that could be corrected strictly through cleaning or interpolation of the reported N GSIM data.
References
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Journal ArticleDOI

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TL;DR: In this paper, the acceleration at time t of a car attempting to follow a leader is proportional to the difference in velocity of the two cars at a time t-Δ, Δ being about 1.5 sec and the proportionality constant being about 0.37 sec-1.
Journal ArticleDOI

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Journal ArticleDOI

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TL;DR: In this article, the authors assess the range of options available in the choice of car-following model, and assess just how far work has proceeded in our understanding of what, at times, would appear to be a simple process.
Book

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Journal ArticleDOI

A real-time computer vision system for vehicle tracking and traffic surveillance

TL;DR: The issues associated with feature based tracking are described, the real-time implementation of a prototype system, and the performance of the system on a large data set are presented.
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