Proceedings Article10.1109/SMC.2016.7844736
Driving behaviour analysis using topological features
Mostafa Hossny,Shady Mohammed,Saeid Nahavandi,Kyle Nelson,Mohammed Hossny +4 more
- 01 Jan 2016
- pp 1-6
3
TL;DR: This paper captured a driver's head motion as an experimental behavioural cue, combined it with captured simulated vehicle data, and extracted descriptive statistics to show the significance of these barcode as features for driver behaviour prediction.
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Abstract: Driving behaviour prediction is a challenging problem due to the nonlinearity of human behaviour. Linear and nonlinear techniques have been used to solve this problem, and they provide good results presented in the performance of the current autonomous cars. However, they lack the ability to adapt to abruptness that happens because of the human factor. In this paper, we introduce a method to extract persistent homology barcode statistics. These statistics are useful as a representative of the driving process including the human behaviour. Human factor identification requires finding features that preserve certain properties against scalability, deformation, and abruptness. Topological Data Analysis (TDA) using persistent homology provides these features for driver behaviour prediction. We captured a driver's head motion as an experimental behavioural cue, combined it with captured simulated vehicle data (location and velocities). Barcodes are extracted using JavaPlex, then we extracted descriptive statistics to show the significance of these barcode as features for driver behaviour prediction. The correlation between the extracted features shows a promising start for a behavioural tracking applications using TDA.
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Citations
Eye behaviour as a hazard perception measure
Julie Iskander,Samer Hanoun,Imali Hettiarachchi,Mohammed Hossny,Khaled Saleh,Hailing Zhou,Saeid Nahavandi,Asim Bhatti +7 more
- 01 Jan 2018
TL;DR: The results show that eye movement dynamics and change in pupil diameter can provide good measures of the drivers' hazard perception and prediction.
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A Class of Topological Pseudodistances for Fast Comparison of Persistence Diagrams
Rolando Kindelan Nuñez,Mircea Petrache,Mauricio Cerda,Nancy Hitschfeld +3 more
TL;DR: A new class of topological pseudodistances called Extended Topological Pseudodistances (ETD)s is introduced for comparing persistence diagrams. ETDs have tunable complexity and can approximate both Wasserstein and Persistence Statistics distances.
A Class of Topological Pseudodistances for Fast Comparison of Persistence Diagrams
Rolando Kindelan Nunez,Mircea Petrache,Mauricio Cerda,Nancy Hitschfeld-Kahler +3 more
TL;DR: A class of pseudodistances called Extended Topological Pseudodistances (ETD)s are introduced, which have tunable complexity, and can approximate Sliced and classical Wasserstein distances at the high-complexity extreme, while being computationally lighter and close to Persistence Statistics at the lower complexity extreme, and thus allow users to interpolate between the two metrics.
References
Topology and data
TL;DR: This paper will discuss how geometry and topology can be applied to make useful contributions to the analysis of various kinds of data, particularly high throughput data from microarray or other sources.
Barcodes: The persistent topology of data
TL;DR: In this paper, a survey of the use of algebraic topology for feature detection and shape recognition in high-dimensional data is presented. But the main focus of the survey is on the application of topology to the classification of natural images.
A roadmap for the computation of persistent homology
Nina Otter,Nina Otter,Mason A. Porter,Mason A. Porter,Ulrike Tillmann,Ulrike Tillmann,Peter Grindrod,Heather A. Harrington +7 more
TL;DR: A friendly introduction to PH is given, the pipeline for the computation of PH is navigated with an eye towards applications, and a range of synthetic and real-world data sets are used to evaluate currently available open-source implementations for the computations of PH.
On the Local Behavior of Spaces of Natural Images
TL;DR: A theoretical model for the high-density 2-dimensional submanifold of ℳ showing that it has the topology of the Klein bottle and a polynomial representation is used to give coordinatization to various subspaces ofℳ.
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Topological estimation using witness complexes
Vin de Silva,Gunnar E. Carlsson +1 more
- 02 Jun 2004
TL;DR: This paper tackles the problem of computing topological invariants of geometric objects in a robust manner, using only point cloud data sampled from the object, and produces a nested family of simplicial complexes, which represent the data at different feature scales, suitable for calculating persistent homology.
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