An ensemble deep learning approach for driver lane change intention inference

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dc.contributor.author Xing, Yang
dc.contributor.author Lv, Chen
dc.contributor.author Wang, Huaji
dc.contributor.author Cao, Dongpu
dc.contributor.author Velenis, Efstathios
dc.date.accessioned 2020-07-16T16:08:02Z
dc.date.available 2020-07-16T16:08:02Z
dc.date.issued 2020-04-23
dc.identifier.citation Xing 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 102615 en_UK
dc.identifier.issn 0968-090X
dc.identifier.uri https://doi.org/10.1016/j.trc.2020.102615
dc.identifier.uri http://dspace.lib.cranfield.ac.uk/handle/1826/15560
dc.description.abstract With 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.language.iso en en_UK
dc.publisher Elsevier en_UK
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Driver intention en_UK
dc.subject ADAS en_UK
dc.subject RNN en_UK
dc.subject LSTM en_UK
dc.subject Intelligent vehicle en_UK
dc.title An ensemble deep learning approach for driver lane change intention inference en_UK
dc.type Article en_UK


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