Research on Stable Obstacle Avoidance Control Strategy for Tracked Intelligent Transportation Vehicles in Non-structural Environment Based on Deep Learning

TL;DR

This paper presents a deep learning-based obstacle avoidance control strategy for tracked intelligent transportation vehicles operating in non-structural environments, ensuring stability and smooth driving.

Contribution

It introduces an Attention-LSTM deep learning model combined with stability analysis to improve obstacle avoidance in unmanned vehicles.

Findings

Average relative error of 15% in obstacle avoidance control

Verified stability and accuracy through numerical and experimental methods

Ensures smooth and safe obstacle avoidance in complex environments

Abstract

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.