Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection

In this paper, a driver's intention prediction near a road intersection is proposed. Our approach uses a deep bidirectional Long Short-Term Memory (LSTM) with an attention mechanism model based on a hybrid-state system (HSS) framework. As intersection is considered to be as one of the major sou...

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Bibliographic Details
Published in:arXiv.org 2020-06
Main Authors: Abenezer Girma, Amsalu, Seifemichael, Workineh, Abrham, Khan, Mubbashar, Homaifar, Abdollah
Format: Article
Language:English
Subjects:
Accident prediction
Advanced driver assistance systems
Deep learning
Driver behavior
Drivers
Hybrid systems
Yaw
Online Access:Get full text
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Summary:In this paper, a driver's intention prediction near a road intersection is proposed. Our approach uses a deep bidirectional Long Short-Term Memory (LSTM) with an attention mechanism model based on a hybrid-state system (HSS) framework. As intersection is considered to be as one of the major source of road accidents, predicting a driver's intention at an intersection is very crucial. Our method uses a sequence to sequence modeling with an attention mechanism to effectively exploit temporal information out of the time-series vehicular data including velocity and yaw-rate. The model then predicts ahead of time whether the target vehicle/driver will go straight, stop, or take right or left turn. The performance of the proposed approach is evaluated on a naturalistic driving dataset and results show that our method achieves high accuracy as well as outperforms other methods. The proposed solution is promising to be applied in advanced driver assistance systems (ADAS) and as part of active safety system of autonomous vehicles.
ISSN:2331-8422