基于Residual BiLSTM网络的车辆切入意图预测研究

doi:10.19562/j.chinasae.qcgc.2021.07.003

Abstract

Abstract:

A vehicle cut?in intention prediction model based on Residual BiLSTM network is proposed in this paper according to the real road features in China. The cut?in features are extracted from the trajectory information of the cut?in vehicle and its interaction information with ego vehicle， and the softmax function is used to calculate the cut?in intention， e.i. the probability of left lane keeping， left lane cut?in， right lane cut?in or right lane keeping respectively. Finally， the prediction model is trained and tested with the naturalistic driving data set on the complex roads in China. The results show that the Residual BiLSTM model proposed has obvious advantages in cut?in intention prediction， with an accuracy 8.2 percentage points higher than that of LSTM model， and can predict the vehicle cut?in intention earlier， being of great significance in enhancing the decision?making and planning ability and safety of autonomous vehicles.

Key words: autonomous driving, cut?in intention prediction, deep learning, interaction information, naturalistic driving data

Jinghua Guo,Baoping Xiao,Jingyao Wang,Yugong Luo,Tao Chen,Keqiang Li. Study on Vehicle Cut⁃in Intention Prediction Based on Residual BiLSTM Network[J].Automotive Engineering, 2021, 43(7): 971-977.

Figures/Tables 13

References 16

1	SCHEEL O， SCHWARZ L， NAVAB N， et al. Situation assessment for planning lane changes： combining recurrent models and prediction［C］. 2018 IEEE International Conference on Robotics and Automation （ICRA）， 2018： 2082-2088.
2	LIU Q X， XU S H， LU C， et al. Early recognition of driving intention for lane change based on recurrent hidden semi⁃Markov model［J］. IEEE Transactions on Vehicular Technology， 2020， 69（10）： 10545-10557.
3	KASPER D， WEIDL G， DANG T， et al. Object⁃oriented Bayesian networks for detection of lane change maneuvers［J］. IEEE Intelligent Transportation Systems Magazine， 2012， 4（3）： 19-31.
4	OH C， CHOI J， PARK S. In⁃depth understanding of lane changing interactions for in⁃vehicle driving assistance systems［J］. International Journal of Automotive Technology， 2017， 18（2）： 357-363.
5	刘志强，吴雪刚，倪捷，等. 基于HMM和SVM级联算法的驾驶意图识别［J］. 汽车工程， 2018， 40（7）： 858-864.
	LIU Z Q， WU X G， NI J， et al. Driving intention recognition based on HMM and SVM cascade algorithm［J］. Automotive Engineering， 2018， 40（7）： 858-864.
6	ZHU B， YAN S D， ZHAO J， et al. Personalized lane⁃change assistance system with driver behavior identification［J］. IEEE Transactions on Vehicular Technology， 2018， 67（11）： 10293-10306.
7	黄玲，郭亨聪，张荣辉，等. 人机混驾环境下基于LSTM的无人驾驶车辆换道行为模型［J］. 中国公路学报， 2020， 33（7）： 156-166.
	HUANG L， GUO H C， ZHANG R H， et al. LSTM⁃based lane⁃changing behavior model for unmanned vehicle under the environment of heterogeneous human⁃driven and autonomous vehicles［J］. China Journal of Highway and Transport， 2020， 33（7）： 156-166.
8	WANG X， JIANG R， LIN Y L， et al. Capturing car⁃following behaviors by deep learning［J］. IEEE Transactions on Intelligent Transportation Systems， 2017， 19（3）： 910-920.
9	ZYNER A， WORRALL S， WARDA J， et al. Long short term memory for driver intent prediction［C］. 2017 IEEE Intelligent Vehicles Symposium （IV）， 2017： 1484-1489.
10	郭景华，李克强，王进，等. 基于危险场景聚类分析的前车随机运动状态预测研究［J］. 汽车工程， 2020， 42（7）： 847-853.
	GUO J H， LI K Q， WANG J， et al. Study on prediction of preceding vehicle's stochastic motion based on risk scenarios clustering analysis ［J］. Automotive Engineering， 2020， 42（7）： 847-853.
11	TOLEDO T，ZOHAR D. Modeling duration of lane changes［J］. Transportation Research Record， 2007， 1999（1）： 71-78.
12	SU S， MUELLING K， DOLAN J， et al. Learning vehicle surrounding⁃aware lane⁃changing behavior from observed trajectories［C］. 2018 IEEE Intelligent Vehicles Symposium （IV），2018： 1412-1417.
13	季学武，费聪，何祥坤，等. 基于LSTM网络的驾驶意图识别及车辆轨迹预测［J］. 中国公路学报， 2019， 32（6）： 34-42.
	JI X W， FEI C， HE X K， et al. Intention recognition and trajectory prediction for vehicles using LSTM network［J］. China Journal of Highway and Transport， 2019， 32（6）： 34-42.
14	JEONG Y， YI K. Bidirectional long shot⁃term memory⁃based interactive motion prediction of cut⁃in vehicles in urban environments［J］. IEEE Access， 2020， 8： 106183-106197.
15	李永刚，王朝晖，万晓依，等. 基于深度残差双单向DLSTM的时空一致视频事件识别［J］. 计算机学报， 2018， 41（12）： 2852-2866.
	LI Y G， WANG Z H， WAN X Y， et al. Deep residual dual unidirectional DLSTM for video event recognition with spatial⁃temporal consistency ［J］. Chinese Journal of Computers， 2018， 41（12）： 2852-2866.
16	WIRTHMULLER F， SCHLECHTRIEMEN J， HIPP J， et al. Teaching vehicles to anticipate： a systematic study on probabilistic behavior prediction using large data sets［J］. IEEE Transactions on Intelligent Transportation Systems， 2020： 1-16.

评价指标		精确率	召回率	F1?分数	准确率
意图	左保持	90.6	81.8	86.0	85.2
	左插入	83.5	93.0	88.0
	右插入	82.9	84.1	83.5
	右保持	84.6	81.8	83.2

评价指标		精确率	召回率	F1?分数	准确率
意图	左保持	96.6	93.0	94.8	93.4
	左插入	94.0	97.3	95.6
	右插入	91.0	93.6	92.3
	右保持	92.2	89.7	90.9

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Study on Vehicle Cut⁃in Intention Prediction Based on Residual BiLSTM Network

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