Extracting Lane Geometry and Topology Information From Vehicle Fleet Trajectories in Complex Urban Scenarios Using a Reversible Jump McMc Method

Roeth, Oliver; Zaum, D.; Brenner, Claus

doi:10.5194/isprs-annals-iv-1-w1-51-2017

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…Furthermore, a fuzzy-set-based algorithm is proposed to construct the lane geometry near road intersections using trafc rules [16]. Roeth et al [17] proposed a method based on elementary building blocks that guarantees applicable lane models and used a reversible jump Markov chain Monte Carlo method to explore the model parameters. Tird, these lane models of road segments are connected into lanes.…”

Section: Related Workmentioning

confidence: 99%

Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior

Fan

Zhang

Wan

et al. 2022

Journal of Advanced Transportation

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In recent years, the construction of lane-level road maps has received extensive attention from industry and academia. It has been widely studied because this kind of map provides the foundation for much research, such as high-precision navigation, driving behavior analysis, and traffic analysis. Trajectory-based crowd-mapping is an emerging approach to lane-level map construction. However, the major problem is that existing methods neglect modeling the trajectory distribution in the longitudinal direction of the road, which significantly impacts precision. Thus, this article proposes a two-stage method based on vehicle lane-changing behavior to model the road’s lateral and longitudinal trajectory distributions simultaneously. In the first stage, lane-changing behaviors are extracted from vehicle trajectories. In the second stage, the lane extraction model is established using the weighted constrained Gaussian mixture model and hidden Markov model to estimate lane parameters (e.g., lane counts and lane centerline) on each road cross section. Then accurate and continuous lane centerlines can be constructed accordingly. The proposed method is verified using vehicle trajectory data collected from the crowdsourced platform named Mapillary. The results show that the proposed method can construct lane-level road information satisfactorily.

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Section: Related Workmentioning

confidence: 99%

Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior

Fan

Zhang

Wan

et al. 2022

Journal of Advanced Transportation

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show abstract

“…As an extension, they presented an approach that is able to estimate the exact lanes inside of an intersection [4]. Since they aim at offline map generation, they do not regard any execution time limits and thus rely on complex and time consuming models.…”

Section: Related Workmentioning

confidence: 99%

Fast Lane-Level Intersection Estimation using Markov Chain Monte Carlo Sampling and B-Spline Refinement

Meyer¹,

Walter²,

Lauer³

2020

Preprint

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Estimating the current scene and understanding the potential maneuvers are essential capabilities of automated vehicles. Most approaches rely heavily on the correctness of maps, but neglect the possibility of outdated information.We present an approach that is able to estimate lanes without relying on any map prior. The estimation is based solely on the trajectories of other traffic participants and is thereby able to incorporate complex environments. In particular, we are able to estimate the scene in the presence of heavy traffic and occlusions.The algorithm first estimates a coarse lane-level intersection model by Markov chain Monte Carlo sampling and refines it later by aligning the lane course with the measurements using a non-linear least squares formulation. We model the lanes as 1D cubic B-splines and can achieve error rates of less than 10cm within real-time.

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“…Apart from the popular applications above, RJMCMC has been used in other various applications such as detection of clusters in disease maps [26], graphical models based variable selection and automatic curve fitting [27], log-linear model selection [28], non-parametric drift estimation [29], delimiting species using multilocus sequence data [30], random effect models [31], generation of lane-accurate road network maps from vehicle trajectory data [32].…”

Section: Reversible Jump Mcmcmentioning

confidence: 99%

“…where N e refers to the sample size, N, for one-sample KS test and N 1 N 2 N 1 + N 2 for two-sample KS test. Substituting t obtained in (33) with (32) gives p-value.…”

Section: A Statistical Significance Testsmentioning

confidence: 99%

Beyond trans-dimensional RJMCMC with a case study in impulsive data modeling

2018

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Reversible jump Markov chain Monte Carlo (RJMCMC) is a Bayesian model estimation method which has been used for trans-dimensional sampling. In this study, we propose utilization of RJMCMC beyond trans-dimensional sampling. This new interpretation, which we call trans-space RJMCMC, reveals the undiscovered potential of RJMCMC by exploiting the original formulation to explore spaces of different classes or structures. This provides flexibility in using different types of candidate classes in the combined model space such as spaces of linear and nonlinear models or of various distribution families. As an application for the proposed method, we have performed a special case of trans-space sampling, namely trans-distributional RJMCMC in impulsive data modeling. In many areas such as seismology, radar, image, using Gaussian models is a common practice due to analytical ease. However, many noise processes do not follow a Gaussian character and generally exhibit events too impulsive to be successfully described by the Gaussian model. We test the proposed method to choose between various impulsive distribution families to model both synthetically generated noise processes and real life measurements on power line communications (PLC) impulsive noises and 2-D discrete wavelet transform (2-D DWT) coefficients. 6/222.

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Extracting Lane Geometry and Topology Information From Vehicle Fleet Trajectories in Complex Urban Scenarios Using a Reversible Jump McMc Method

Cited by 6 publications

References 14 publications

Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior

Lane-Level Road Map Construction considering Vehicle Lane-Changing Behavior

Fast Lane-Level Intersection Estimation using Markov Chain Monte Carlo Sampling and B-Spline Refinement

Beyond trans-dimensional RJMCMC with a case study in impulsive data modeling

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