Driving Maneuver Detection using Features of Driver's attention and Face Shift through Deeping Learning

Abstract

Driving Maneuver Detection (DMD) is an important component in ADAS(Advanced Driver Assistance Systems). It provides information about driving maneuvers that can potentially lead to traffic accidents. This paper presents a DMD system that builds on deep learning models developed for extracting driver attention features and driver face shift features, and a Long Short-Term Memory (LSTM) based neural network designed to learn dependencies of maneuvers in a time period. We show through experiments that the proposed system is capable of learning the latent features of the five different classes of driving maneuvers, i.e. left turn, right turn, left lane change, right lane change, and driving straight, and the innovative use of the combined features, i.e. driver attention features, driver face shift and vehicle signals that makes the DMD system to perform significantly superior to a number of traditional methods on a naturalistic driving data set containing over 3100 maneuvers recorded from 20 different drivers.