Driving Intention Identification Based on Long Short-Term Memory Neural Network

被引：5
|
作者：
Liu, Yonggang [1 ,2 ]
Zhao, Pan [1 ,2 ]
Qin, Datong [1 ,2 ]
Yang, Yang [1 ,2 ]
Chen, Zheng [3 ]
机构：
[1] Chongqing Univ, State Key Lab Mech Transmiss, Chongqing, Peoples R China
[2] Chongqing Univ, Sch Automot Engn, Chongqing, Peoples R China
[3] Kunming Univ Sci & Technol, Fac Transportat Engn, Kunming, Yunnan, Peoples R China
来源：
2019 IEEE VEHICLE POWER AND PROPULSION CONFERENCE (VPPC) | 2019年
基金：
欧盟地平线“2020”; 美国国家科学基金会;
关键词：
LSTM; driving intentions; identification;
D O I：
10.1109/vppc46532.2019.8952563
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In order to avoid frequent or accidental shift problems during the driving process, it is necessary to implement identification of driving intention based on vehicle driving data. In this study, the Long Short-Term Memory (LSTM) Neural Network is proposed to identify driving intentions in real time. First, according to the vehicle road test data, each driving intention to be identified is defined. Then, the intentions when driving on a straight and flat road are divided into acceleration, rapid acceleration, cruise, deceleration and rapid deceleration. Subsequently, a LSTM classification model is established to identify the driving intention with inputs of opening degree of the accelerator pedal, vehicle speed and brake pedal force. Identification results reveal that the highest accuracy of the proposed algorithm attains 95.36%, which is around 20% higher than that of the traditional back propagation neural network.
引用
收藏
页数：6
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