Stable Obstacle Avoidance Strategy for Crawler-Type Intelligent Transportation Vehicle in Non-Structural Environment Based on Attention-Learning

Existing intelligent driving technology often has difficulty balancing smooth driving and fast obstacle avoidance, especially when the vehicle is in a non-structural environment and is prone to instability during emergencies. Therefore, this study proposed an autonomous obstacle avoidance control strategy that can effectively guarantee vehicle stability based on an 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 based on the Attention-LSTM network model through experiments, and the average relative error of this system was 14.95%. 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 obstacles while driving smoothly.