Lane-changes prediction based on adaptive fuzzy neural network

Tang, Jinjun; Liu, Fang; Zhang, Wenhui; Ke, Ruimin; Zou, Yajie

doi:10.1016/j.eswa.2017.09.025

Cited by 160 publications

(77 citation statements)

References 18 publications

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“…This property enables modelling and short-term forecasting of traffic flow in urban arterial networks using multivariate traffic data [13,14]. Recent works to urban traffic flow prediction [15] and to lane-changes prediction [16] have been proposed with success. Furthermore, the successful use of fuzzy-based similarity measure in pattern recognition [17], in retrieval systems [12], and in recommendation systems [18] leads us to study its ability to complete missing values in uncorrelated multivariate time series.…”

Section: Introductionmentioning

confidence: 99%

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A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

Phan

Bigand

Caillault

2018

Applied Computational Intelligence and Soft Computing

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The completion of missing values is a prevalent problem in many domains of pattern recognition and signal processing. Analyzing data with incompleteness may lead to a loss of power and unreliable results, especially for large missing subsequence(s). Therefore, this paper aims to introduce a new approach for filling successive missing values in low/uncorrelated multivariate time series which allows managing a high level of uncertainty. In this way, we propose using a novel fuzzy weighting-based similarity measure. The proposed method involves three main steps. Firstly, for each incomplete signal, the data before a gap and the data after this gap are considered as two separated reference time series with their respective query windows and . We then find the most similar subsequence ( ) to the subsequence before this gap and the most similar one ( ) to the subsequence after the gap . To find these similar windows, we build a new similarity measure based on fuzzy grades of basic similarity measures and on fuzzy logic rules. Finally, we fill in the gap with average values of the window following and the one preceding . The experimental results have demonstrated that the proposed approach outperforms the state-of-the-art methods in case of multivariate time series having low/noncorrelated data but effective information on each signal.

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Section: Introductionmentioning

confidence: 99%

“…Calculate a fuzzy-based similarity measure between and ( ): (13) Save the to (14) ← + ℎ ℎ (15) end while (16) return ℎ ℎ = max{ } (17)…”

Section: Fuzzy-weighted Similarity Measure Between Subsequencesmentioning

confidence: 99%

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

Phan

Bigand

Caillault

2018

Applied Computational Intelligence and Soft Computing

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“…BP (Back Propagation) neural network (BPNN) is one of the most widely used and successful learning algorithms in current research, and is particularly suitable for solving complex problems with internal mechanisms [27][28][29]. In order to verify the performance of SVR model, a typical feed-forward BPNN is established to compare with SVR model on the performance of driving decision-making.…”

Section: The Performance Of Svr Modelmentioning

confidence: 99%

Study on Driving Decision-Making Mechanism of Autonomous Vehicle Based on an Optimized Support Vector Machine Regression

et al. 2017

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Featured Application: This work is specifically applied to the driving decision-making system of autonomous vehicles, allowing autonomous vehicles to run safely under complex urban road environment.Abstract: Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions.

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“…Network screening to identify sites (i.e., roadway segments or intersections) with promise for safety treatments is an important task in road safety management [1][2][3][4][5][6][7]. The identification of sites with promise, also known as crash hotspots or hazardous locations, is the first task in the overall safety management process [8].…”

Section: Introductionmentioning

confidence: 99%

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

Zou

Zhong

Ash

et al. 2017

Journal of Advanced Transportation

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Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, -means, and hierarchical clustering) to select "similar" sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

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Lane-changes prediction based on adaptive fuzzy neural network

Cited by 160 publications

References 18 publications

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series

Study on Driving Decision-Making Mechanism of Autonomous Vehicle Based on an Optimized Support Vector Machine Regression

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

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