An ensemble deep learning approach for driver lane change intention inference

Show simple item record Xing, Yang Lv, Chen Wang, Huaji Cao, Dongpu Velenis, Efstathios 2020-07-16T16:08:02Z 2020-07-16T16:08:02Z 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.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 *
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

Files in this item

The following license files are associated with this item:

This item appears in the following Collection(s)

Show simple item record

Attribution-NonCommercial-NoDerivatives 4.0 International Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International

Search CERES


My Account