Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection
This addresses safety issues for drivers and autonomous vehicles at intersections, but it is incremental as it builds on existing deep learning and attention techniques.
The paper tackles predicting driver intentions at intersections to reduce accidents by using a deep bidirectional LSTM with attention mechanism, achieving high accuracy and outperforming other methods on a naturalistic driving dataset.
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.