An ensemble deep learning approach for driver lane change intention inference

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dc.contributor.authorXing, Yang
dc.contributor.authorLv, Chen
dc.contributor.authorWang, Huaji
dc.contributor.authorCao, Dongpu
dc.contributor.authorVelenis, Efstathios
dc.date.accessioned2020-07-16T16:08:02Z
dc.date.available2020-07-16T16:08:02Z
dc.date.freetoread2021-04-24
dc.date.issued2020-04-23
dc.description.abstractWith the rapid development of intelligent vehicles, drivers are increasingly likely to share their control authorities with the intelligent control unit. For building an efficient Advanced Driver Assistance Systems (ADAS) and shared-control systems, the vehicle needs to understand the drivers’ intent and their activities to generate assistant and collaborative control strategies. In this study, a driver intention inference system that focuses on the highway lane change maneuvers is proposed. First, a high-level driver intention mechanism and framework are introduced. Then, a vision-based intention inference system is proposed, which captures the multi-modal signals based on multiple low-cost cameras and the VBOX vehicle data acquisition system. A novel ensemble bi-directional recurrent neural network (RNN) model with Long Short-Term Memory (LSTM) units is proposed to deal with the time-series driving sequence and the temporal behavioral patterns. Naturalistic highway driving data that consists of lane-keeping, left and right lane change maneuvers are collected and used for model construction and evaluation. Furthermore, the driver's pre-maneuver activities are statistically analyzed. It is found that for situation-aware, drivers usually check the mirrors for more than six seconds before they initiate the lane change maneuver, and the time interval between steering the handwheel and crossing the lane is about 2 s on average. Finally, hypothesis testing is conducted to show the significant improvement of the proposed algorithm over existing ones. With five-fold cross-validation, the EBiLSTM model achieves an average accuracy of 96.1% for the intention that is inferred 0.5 s before the maneuver starts.en_UK
dc.identifier.citationXing Y, Lv C, Wang H, et al., (2020) An ensemble deep learning approach for driver lane change intention inference. Transportation Research Part C: Emerging Technologies, Volume 115, June 2020, Article number 102615en_UK
dc.identifier.cris27239211
dc.identifier.issn0968-090X
dc.identifier.urihttps://doi.org/10.1016/j.trc.2020.102615
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/15560
dc.language.isoenen_UK
dc.publisherElsevieren_UK
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectDriver intentionen_UK
dc.subjectADASen_UK
dc.subjectRNNen_UK
dc.subjectLSTMen_UK
dc.subjectIntelligent vehicleen_UK
dc.titleAn ensemble deep learning approach for driver lane change intention inferenceen_UK
dc.typeArticleen_UK

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