A Memory-Attention Hierarchical Model for Driving-Behavior Recognition and Motion Prediction.

Saved in:

Bibliographic Details
Title:	A Memory-Attention Hierarchical Model for Driving-Behavior Recognition and Motion Prediction.
Authors:	Yin, Huilin¹ (AUTHOR), Wang, Jie¹ (AUTHOR), Lin, Jia¹ (AUTHOR) lj0408@tongji.edu.cn, Han, Daguang² (AUTHOR), Ying, Chunli³ (AUTHOR), Meng, Qian⁴ (AUTHOR)
Source:	International Journal of Automotive Technology. Aug2021, Vol. 22 Issue 4, p895-908. 14p.
Subjects:	Recurrent neural networks, Hidden Markov models, Motor vehicle driving, Brain physiology, Traffic safety, Autonomous vehicles, Short-term memory
Abstract:	Proper understanding and prediction of driving behavior of surrounding vehicles are one of the most significant requirements for automated driving especially when it comes to safety on a highway. In this paper, we propose a two-layer memory-attention hierarchical model (MAHM) for driving-behavior recognition and motion prediction. This model is based on the human driver's thinking as well as on brain physiology, i.e., working memory and the selective-attention mechanism. The first layer is a hidden Markov model (HMM), which is used to achieve efficient recognition of driving behavior. The second layer is a memory-attention recurrent neural network (MARNN) for motion prediction, which derives the data from vehicles of interest as input according to driving behavior. Finally, the experimental analysis is performed on the real-data NGSIM US-101 and HighD datasets for highway-driving scenes. We report our results from three perspectives: accuracy of driving-behavior classification, error of predicted trajectories, and execution time. [ABSTRACT FROM AUTHOR]
	Copyright of International Journal of Automotive Technology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database:	Engineering Source

Description
Abstract:	Proper understanding and prediction of driving behavior of surrounding vehicles are one of the most significant requirements for automated driving especially when it comes to safety on a highway. In this paper, we propose a two-layer memory-attention hierarchical model (MAHM) for driving-behavior recognition and motion prediction. This model is based on the human driver's thinking as well as on brain physiology, i.e., working memory and the selective-attention mechanism. The first layer is a hidden Markov model (HMM), which is used to achieve efficient recognition of driving behavior. The second layer is a memory-attention recurrent neural network (MARNN) for motion prediction, which derives the data from vehicles of interest as input according to driving behavior. Finally, the experimental analysis is performed on the real-data NGSIM US-101 and HighD datasets for highway-driving scenes. We report our results from three perspectives: accuracy of driving-behavior classification, error of predicted trajectories, and execution time. [ABSTRACT FROM AUTHOR]
ISSN:	12299138
DOI:	10.1007/s12239-021-0081-8