Building representative velocity profiles using FastDTW and spectral clustering

Lohrer, Jurgen; Lienkamp, Markus

doi:10.1109/itst.2015.7377398

Cited by 9 publications

(7 citation statements)

References 17 publications

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“…FastDTW aims to reduce the computation time for quantifying similarity between series of length n from O(n 2 ) for DTW to O(n). FastDTW has been previously used in related tasks (Lohrer and Lienkamp 2015).…”

Section: Introductionmentioning

confidence: 99%

ADHD Prediction via Time Series Ensemble fed Driving Simulator Data

Grethlein

Sladek

Ontañón

2021

FLAIRS

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In this paper, we identify the on-road scenarios within a simulated driving environment where a group of clinical trial participants (n= 30) with and without Attention Deficit Hyper-activity Disorder (ADHD) drive perceivably different fromone another. We partition the simulated routes into smaller non-overlapping sections in order to determine which sections elicit behaviors that are predictive of ADHD. Then, we develop section-specific classifiers, which are used as voters in bagging ensemble classifiers. Our results show gains in classifying ADHD (increase in 5-fold average evaluation accuracy) over our previous efforts, as well as providing explainable evidence that driving behaviors indicative of ADHD tend to be exhibited in turns and curves.

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Section: Introductionmentioning

confidence: 99%

ADHD Prediction via Time Series Ensemble fed Driving Simulator Data

Grethlein

Sladek

Ontañón

2021

FLAIRS

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“…The routable OSM-based segmentation of road segments is the basis for building representative speed profiles. Lohrer et al [18] describes the process in detail, which is summarized as follows. The speed data of the FOT trips represent different scenarios at various times and traffic states.…”

Section: Speed Predictionmentioning

confidence: 99%

An approach for predicting vehicle velocity in combination with driver turns

Lohrer

Lienkamp

2016

Automot. Engine Technol.

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Statistical information such as average traffic flow on a road network link are not precise enough to enable detailed in-vehicle optimization strategies. Energy efficiency measures are further restricted if the vehicle is not aware of the destination of the trip. A modular and dynamic approach for predicting the vehicle speed in combination with driver turns is introduced. The objective is to adapt the vehicle components to the upcoming speed and state. The cloud-based approach foregoes the user-based selection of a route, trip destination or individual points of interest but is mainly based on historic speed profiles of different drivers. The concept provides a two-stage approach. The first stage is the prediction of upcoming turns and trip segments based on the historical features and the currently driven road segments. The second stage uses this information to predict the vehicle's speed.

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“…• "the distances between the time series are computed using FastDTW" [13]. To gauge how commonly used this algorithm is, consider the fact that at least five papers use the term FastDTW in their title [14], [15], [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates

Keogh

2022

IEEE Trans. Knowl. Data Eng.

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Many time series data mining problems can be solved with repeated use of distance measure. Examples of such tasks include similarity search, clustering, classification, anomaly detection and segmentation. For over two decades it has been known that the Dynamic Time Warping (DTW) distance measure is the best measure to use for most tasks, in most domains. Because the classic DTW algorithm has quadratic time complexity, many ideas have been introduced to reduce its amortized time, or to quickly approximate it. One of the most cited approximate approaches is FastDTW. The FastDTW algorithm has well over a thousand citations and has been explicitly used in several hundred research efforts. In this work, we make a surprising claim. In any realistic data mining application, the approximate FastDTW is much slower than the exact DTW. This fact clearly has implications for the community that uses this algorithm: allowing it to address much larger datasets, get exact results, and do so in less time. Our observation also has a more sobering lesson for the community. This work may serve as a reminder to the community to exercise more caution in uncritically accepting published results.

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Building representative velocity profiles using FastDTW and spectral clustering

Cited by 9 publications

References 17 publications

ADHD Prediction via Time Series Ensemble fed Driving Simulator Data

ADHD Prediction via Time Series Ensemble fed Driving Simulator Data

An approach for predicting vehicle velocity in combination with driver turns

FastDTW is Approximate and Generally Slower Than the Algorithm it Approximates

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