Empowering Advanced Driver-Assistance Systems from Topological Data Analysis

Tarek Frahi,Manyong Han,Hyung Yun Choi,Elias Cueto,Antonio Falcó,Alberto Badias,Jean-Louis Duval,Francisco Chinesta

doi:10.3390/math9060634

Abstract

We are interested in evaluating the state of drivers to determine whether they are attentive to the road or not by using motion sensor data collected from car driving experiments. That is, our goal is to design a predictive model that can estimate the state of drivers given the data collected from motion sensors. For that purpose, we leverage recent developments in topological data analysis (TDA) to analyze and transform the data coming from sensor time series and build a machine learning model based on the topological features extracted with the TDA. We provide some experiments showing that our model proves to be accurate in the identification of the state of the user, predicting whether they are relaxed or tense.

Highlights

While there have recently been considerable advances in self-driving car technology, driving still relies mainly on human factors
advanced driver assistance systems (ADAS) systems, and the aim of this work is to contribute to the development of such a system based on a systematic analysis of drivers in actual driving conditions
We prove the following result, which helps us to identify the time series given by the vector X in R N with the function f X in L2 (R)

Summary

Introduction

While there have recently been considerable advances in self-driving car technology, driving still relies mainly on human factors. Even in self-driving mode, human drivers must often make decision in a fraction of a second to avoid accidents. It is still of utmost importance to develop systems capable of discerning if the human driver is attentive or not to the road conditions. The so-called advanced driver assistance systems (ADAS) [1,2] are systems that are able to improve the driver’s performance, among which, adaptive speed limiters, pedestrian detectors [3], and cruise controllers are some of the most popular systems. ADAS systems, and the aim of this work is to contribute to the development of such a system based on a systematic analysis of drivers in actual driving conditions. The estimation of the driver’s condition (degree of attention to the road, fatigue, etc.)

Objectives

Results

Conclusion