  Existing intelligent driving technology often has a problem in balancing
smooth driving and fast obstacle avoidance, especially when the vehicle is in a
non-structural environment, and is prone to instability in emergency
situations. Therefore, this study proposed an autonomous obstacle avoidance
control strategy that can effectively guarantee vehicle stability based on
Attention-long short-term memory (Attention-LSTM) deep learning model with the
idea of humanoid driving. First, we designed the autonomous obstacle avoidance
control rules to guarantee the safety of unmanned vehicles. Second, we improved
the autonomous obstacle avoidance control strategy combined with the stability
analysis of special vehicles. Third, we constructed a deep learning obstacle
avoidance control through experiments, and the average relative error of this
system was 15%. Finally, the stability and accuracy of this control strategy
were verified numerically and experimentally. The method proposed in this study
can ensure that the unmanned vehicle can successfully avoid the obstacles while
driving smoothly.
