Automatic driving lane change safety prediction model based on LSTM
Wenjian Sun Linying Pan Jingyu Xu Weixiang Wan Yong Wang
Autonomous driving technology can improve traffic safety and reduce traffic
accidents. In addition, it improves traffic flow, reduces congestion, saves
energy and increases travel efficiency. In the relatively mature automatic
driving technology, the automatic driving function is divided into several
modules: perception, decision-making, planning and control, and a reasonable
division of labor can improve the stability of the system. Therefore,
autonomous vehicles need to have the ability to predict the trajectory of
surrounding vehicles in order to make reasonable decision planning and safety
measures to improve driving safety. By using deep learning method, a
safety-sensitive deep learning model based on short term memory (LSTM) network
is proposed. This model can alleviate the shortcomings of current automatic
driving trajectory planning, and the output trajectory not only ensures high
accuracy but also improves safety. The cell state simulation algorithm
simulates the trackability of the trajectory generated by this model. The
research results show that compared with the traditional model-based method,
the trajectory prediction method based on LSTM network has obvious advantages
in predicting the trajectory in the long time domain. The intention recognition
module considering interactive information has higher prediction and accuracy,
and the algorithm results show that the trajectory is very smooth based on the
premise of safe prediction and efficient lane change. And autonomous vehicles
can efficiently and safely complete lane changes.