无人驾驶汽车周边车辆行为识别算法研究*

doi:10.19562/j.chinasae.qcgc.2020.11.003

Abstract

Abstract: The behavior recognition of surrounding vehicles is very important to improve the decision-making rationality and the control safety for driverless cars. Traditional methods of surrounding vehicle’s behavior recognition suffer from low accuracy and poor robustness, without considering the interaction between adjacent traffic objects. In order to solve these problems, SLSTMAT (Social-LSTM-Attention) algorithm is proposed to achieve high accuracy for surrounding vehicle’s behavior recognition. The social characteristics of target vehicle are innovatively introduced and extracted by convolutional neural network. Based on deep learning,the recognition model for vehicle behavior is established. Attention mechanism is applied to capture multiple time-step information in the behavior time window. The algorithm is verified by HighD trajectory data set and real vehicle data. The results show that the accuracy rate of SLSTMAT for surrounding vehicle’s behavior recognition reaches 94.01%, and the precision of behavior recognition reaches 90% at 1s before the target vehicle driving to the lane change point, which has high engineering application value

Key words: driverless cars, behavior recognition, long and short term memory network, attention mechanism, social characteristics

Cai Yingfeng, Tai Kangsheng, Wang Hai, Li Yicheng, Chen Long. Research on Behavior Recognition Algorithm of Surrounding Vehicles for Driverless Car[J].Automotive Engineering, 2020, 42(11): 1464-1472.

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Research on Behavior Recognition Algorithm of Surrounding Vehicles for Driverless Car

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